# Linear Algebra

In addition to (and as part of) its support for multi-dimensional arrays, Julia provides native implementations of many common and useful linear algebra operations which can be loaded with `using LinearAlgebra`. Basic operations, such as `tr`, `det`, and `inv` are all supported:

    julia> A = [1 2 3; 4 1 6; 7 8 1]
    3×3 Matrix{Int64}:
     1  2  3
     4  1  6
     7  8  1

    julia> tr(A)
    3

    julia> det(A)
    104.0

    julia> inv(A)
    3×3 Matrix{Float64}:
     -0.451923   0.211538    0.0865385
      0.365385  -0.192308    0.0576923
      0.240385   0.0576923  -0.0673077

As well as other useful operations, such as finding eigenvalues or eigenvectors:

    julia> A = [-4. -17.; 2. 2.]
    2×2 Matrix{Float64}:
     -4.0  -17.0
      2.0    2.0

    julia> eigvals(A)
    2-element Vector{ComplexF64}:
     -1.0 - 5.0im
     -1.0 + 5.0im

    julia> eigvecs(A)
    2×2 Matrix{ComplexF64}:
      0.945905-0.0im        0.945905+0.0im
     -0.166924+0.278207im  -0.166924-0.278207im

In addition, Julia provides many factorizations which can be used to speed up problems such as linear solve or matrix exponentiation by pre-factorizing a matrix into a form more amenable (for performance or memory reasons) to the problem. See the documentation on `factorize` for more information. As an example:

    julia> A = [1.5 2 -4; 3 -1 -6; -10 2.3 4]
    3×3 Matrix{Float64}:
       1.5   2.0  -4.0
       3.0  -1.0  -6.0
     -10.0   2.3   4.0

    julia> factorize(A)
    LU{Float64, Matrix{Float64}, Vector{Int64}}
    L factor:
    3×3 Matrix{Float64}:
      1.0    0.0       0.0
     -0.15   1.0       0.0
     -0.3   -0.132196  1.0
    U factor:
    3×3 Matrix{Float64}:
     -10.0  2.3     4.0
       0.0  2.345  -3.4
       0.0  0.0    -5.24947

Since `A` is not Hermitian, symmetric, triangular, tridiagonal, or bidiagonal, an LU factorization may be the best we can do. Compare with:

    julia> B = [1.5 2 -4; 2 -1 -3; -4 -3 5]
    3×3 Matrix{Float64}:
      1.5   2.0  -4.0
      2.0  -1.0  -3.0
     -4.0  -3.0   5.0

    julia> factorize(B)
    BunchKaufman{Float64, Matrix{Float64}, Vector{Int64}}
    D factor:
    3×3 Tridiagonal{Float64, Vector{Float64}}:
     -1.64286   0.0   ⋅
      0.0      -2.8  0.0
       ⋅        0.0  5.0
    U factor:
    3×3 UnitUpperTriangular{Float64, Matrix{Float64}}:
     1.0  0.142857  -0.8
      ⋅   1.0       -0.6
      ⋅    ⋅         1.0
    permutation:
    3-element Vector{Int64}:
     1
     2
     3

Here, Julia was able to detect that `B` is in fact symmetric, and used a more appropriate factorization. Often it's possible to write more efficient code for a matrix that is known to have certain properties e.g. it is symmetric, or tridiagonal. Julia provides some special types so that you can "tag" matrices as having these properties. For instance:

    julia> B = [1.5 2 -4; 2 -1 -3; -4 -3 5]
    3×3 Matrix{Float64}:
      1.5   2.0  -4.0
      2.0  -1.0  -3.0
     -4.0  -3.0   5.0

    julia> sB = Symmetric(B)
    3×3 Symmetric{Float64, Matrix{Float64}}:
      1.5   2.0  -4.0
      2.0  -1.0  -3.0
     -4.0  -3.0   5.0

`sB` has been tagged as a matrix that's (real) symmetric, so for later operations we might perform on it, such as eigenfactorization or computing matrix-vector products, efficiencies can be found by only referencing half of it. For example:

    julia> B = [1.5 2 -4; 2 -1 -3; -4 -3 5]
    3×3 Matrix{Float64}:
      1.5   2.0  -4.0
      2.0  -1.0  -3.0
     -4.0  -3.0   5.0

    julia> sB = Symmetric(B)
    3×3 Symmetric{Float64, Matrix{Float64}}:
      1.5   2.0  -4.0
      2.0  -1.0  -3.0
     -4.0  -3.0   5.0

    julia> x = [1; 2; 3]
    3-element Vector{Int64}:
     1
     2
     3

    julia> sB\x
    3-element Vector{Float64}:
     -1.7391304347826084
     -1.1086956521739126
     -1.4565217391304346

The `\` operation here performs the linear solution. The left-division operator is pretty powerful and it's easy to write compact, readable code that is flexible enough to solve all sorts of systems of linear equations.

## Special matrices

Matrices with special symmetries and structures arise often in linear algebra and are frequently associated with various matrix factorizations. Julia features a rich collection of special matrix types, which allow for fast computation with specialized routines that are specially developed for particular matrix types.

The following tables summarize the types of special matrices that have been implemented in Julia, as well as whether hooks to various optimized methods for them in LAPACK are available.

Type | Description
--- | ---
`Symmetric` | Symmetric matrix
`Hermitian` | Hermitian matrix
`UpperTriangular` | Upper triangular matrix
`UnitUpperTriangular` | Upper triangular matrix with unit diagonal
`LowerTriangular` | Lower triangular matrix
`UnitLowerTriangular` | Lower triangular matrix with unit diagonal
`UpperHessenberg` | Upper Hessenberg matrix
`Tridiagonal` | Tridiagonal matrix
`SymTridiagonal` | Symmetric tridiagonal matrix
`Bidiagonal` | Upper/lower bidiagonal matrix
`Diagonal` | Diagonal matrix
`UniformScaling` | Uniform scaling operator

### Elementary operations

Matrix type | `+` | `-` | `*` | `\` | Other functions with optimized methods
--- | --- | --- | --- | --- | ---
`Symmetric` |  |  |  | MV | `inv`, `sqrt`, `cbrt`, `exp`
`Hermitian` |  |  |  | MV | `inv`, `sqrt`, `cbrt`, `exp`
`UpperTriangular` |  |  | MV | MV | `inv`, `det`, `logdet`
`UnitUpperTriangular` |  |  | MV | MV | `inv`, `det`, `logdet`
`LowerTriangular` |  |  | MV | MV | `inv`, `det`, `logdet`
`UnitLowerTriangular` |  |  | MV | MV | `inv`, `det`, `logdet`
`UpperHessenberg` |  |  |  | MM | `inv`, `det`
`SymTridiagonal` | M | M | MS | MV | `eigmax`, `eigmin`
`Tridiagonal` | M | M | MS | MV |
`Bidiagonal` | M | M | MS | MV |
`Diagonal` | M | M | MV | MV | `inv`, `det`, `logdet`, `/`
`UniformScaling` | M | M | MVS | MVS | `/`

Legend:

Key | Description
--- | ---
M (matrix) | An optimized method for matrix-matrix operations is available
V (vector) | An optimized method for matrix-vector operations is available
S (scalar) | An optimized method for matrix-scalar operations is available

### Matrix factorizations

Matrix type | LAPACK | `eigen` | `eigvals` | `eigvecs` | `svd` | `svdvals`
--- | --- | --- | --- | --- | --- | ---
`Symmetric` | SY |  | ARI |  |  |
`Hermitian` | HE |  | ARI |  |  |
`UpperTriangular` | TR | A | A | A |  |
`UnitUpperTriangular` | TR | A | A | A |  |
`LowerTriangular` | TR | A | A | A |  |
`UnitLowerTriangular` | TR | A | A | A |  |
`SymTridiagonal` | ST | A | ARI | AV |  |
`Tridiagonal` | GT |  |  |  |  |
`Bidiagonal` | BD |  |  |  | A | A
`Diagonal` | DI |  | A |  |  |

Legend:

Key | Description | Example
--- | --- | ---
A (all) | An optimized method to find all the characteristic values and/or vectors is available | e.g. `eigvals(M)`
R (range) | An optimized method to find the `il`th through the `ih`th characteristic values are available | `eigvals(M, il, ih)`
I (interval) | An optimized method to find the characteristic values in the interval [`vl`, `vh`] is available | `eigvals(M, vl, vh)`
V (vectors) | An optimized method to find the characteristic vectors corresponding to the characteristic values `x=[x1, x2,...]` is available | `eigvecs(M, x)`

### The uniform scaling operator

A `UniformScaling` operator represents a scalar times the identity operator, `λ*I`. The identity operator `I` is defined as a constant and is an instance of `UniformScaling`. The size of these operators are generic and match the other matrix in the binary operations `+`, `-`, `*` and `\`. For `A+I` and `A-I` this means that `A` must be square. Multiplication with the identity operator `I` is a noop (except for checking that the scaling factor is one) and therefore almost without overhead.

To see the `UniformScaling` operator in action:

    julia> U = UniformScaling(2);

    julia> a = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> a + U
    2×2 Matrix{Int64}:
     3  2
     3  6

    julia> a * U
    2×2 Matrix{Int64}:
     2  4
     6  8

    julia> [a U]
    2×4 Matrix{Int64}:
     1  2  2  0
     3  4  0  2

    julia> b = [1 2 3; 4 5 6]
    2×3 Matrix{Int64}:
     1  2  3
     4  5  6

    julia> b - U
    ERROR: DimensionMismatch: matrix is not square: dimensions are (2, 3)
    Stacktrace:
    [...]

If you need to solve many systems of the form `(A+μI)x = b` for the same `A` and different `μ`, it might be beneficial to first compute the Hessenberg factorization `F` of `A` via the `hessenberg` function. Given `F`, Julia employs an efficient algorithm for `(F+μ*I) \ b` (equivalent to `(A+μ*I)x \ b`) and related operations like determinants.

## Matrix factorizations

Matrix factorizations (a.k.a. matrix decompositions) compute the factorization of a matrix into a product of matrices, and are one of the central concepts in (numerical) linear algebra.

The following table summarizes the types of matrix factorizations that have been implemented in Julia. Details of their associated methods can be found in the Standard functions section of the Linear Algebra documentation.

Type | Description
--- | ---
`BunchKaufman` | Bunch-Kaufman factorization
`Cholesky` | Cholesky factorization
`CholeskyPivoted` | Pivoted Cholesky factorization
`LDLt` | LDL(T) factorization
`LU` | LU factorization
`QR` | QR factorization
`QRCompactWY` | Compact WY form of the QR factorization
`QRPivoted` | Pivoted QR factorization
`LQ` | QR factorization of `transpose(A)`
`Hessenberg` | Hessenberg decomposition
`Eigen` | Spectral decomposition
`GeneralizedEigen` | Generalized spectral decomposition
`SVD` | Singular value decomposition
`GeneralizedSVD` | Generalized SVD
`Schur` | Schur decomposition
`GeneralizedSchur` | Generalized Schur decomposition

Adjoints and transposes of `Factorization` objects are lazily wrapped in `AdjointFactorization` and `TransposeFactorization` objects, respectively. Generically, transpose of real `Factorization`s are wrapped as `AdjointFactorization`.

## Orthogonal matrices (`AbstractQ`)

Some matrix factorizations generate orthogonal/unitary "matrix" factors. These factorizations include QR-related factorizations obtained from calls to `qr`, i.e., `QR`, `QRCompactWY` and `QRPivoted`, the Hessenberg factorization obtained from calls to `hessenberg`, and the LQ factorization obtained from `lq`. While these orthogonal/unitary factors admit a matrix representation, their internal representation is, for performance and memory reasons, different. Hence, they should be rather viewed as matrix-backed, function-based linear operators. In particular, reading, for instance, a column of its matrix representation requires running "matrix"-vector multiplication code, rather than simply reading out data from memory (possibly filling parts of the vector with structural zeros). Another clear distinction from other, non-triangular matrix types is that the underlying multiplication code allows for in-place modification during multiplication. Furthermore, objects of specific `AbstractQ` subtypes as those created via `qr`, `hessenberg` and `lq` can behave like a square or a rectangular matrix depending on context:

    julia> using LinearAlgebra

    julia> Q = qr(rand(3,2)).Q
    3×3 LinearAlgebra.QRCompactWYQ{Float64, Matrix{Float64}, Matrix{Float64}}

    julia> Matrix(Q)
    3×2 Matrix{Float64}:
     -0.320597   0.865734
     -0.765834  -0.475694
     -0.557419   0.155628

    julia> Q*I
    3×3 Matrix{Float64}:
     -0.320597   0.865734  -0.384346
     -0.765834  -0.475694  -0.432683
     -0.557419   0.155628   0.815514

    julia> Q*ones(2)
    3-element Vector{Float64}:
      0.5451367118802273
     -1.241527373086654
     -0.40179067589600226

    julia> Q*ones(3)
    3-element Vector{Float64}:
      0.16079054743832022
     -1.674209978965636
      0.41372375588835797

    julia> ones(1,2) * Q'
    1×3 Matrix{Float64}:
     0.545137  -1.24153  -0.401791

    julia> ones(1,3) * Q'
    1×3 Matrix{Float64}:
     0.160791  -1.67421  0.413724

Due to this distinction from dense or structured matrices, the abstract `AbstractQ` type does not subtype `AbstractMatrix`, but instead has its own type hierarchy. Custom types that subtype `AbstractQ` can rely on generic fallbacks if the following interface is satisfied. For example, for

    struct MyQ{T} <: LinearAlgebra.AbstractQ{T}
        # required fields
    end

provide overloads for

    Base.size(Q::MyQ) # size of corresponding square matrix representation
    Base.convert(::Type{AbstractQ{T}}, Q::MyQ) # eltype promotion [optional]
    LinearAlgebra.lmul!(Q::MyQ, x::AbstractVecOrMat) # left-multiplication
    LinearAlgebra.rmul!(A::AbstractMatrix, Q::MyQ) # right-multiplication

If `eltype` promotion is not of interest, the `convert` method is unnecessary, since by default `convert(::Type{AbstractQ{T}}, Q::AbstractQ{T})` returns `Q` itself. Adjoints of `AbstractQ`-typed objects are lazily wrapped in an `AdjointQ` wrapper type, which requires its own `LinearAlgebra.lmul!` and `LinearAlgebra.rmul!` methods. Given this set of methods, any `Q::MyQ` can be used like a matrix, preferably in a multiplicative context: multiplication via `*` with scalars, vectors and matrices from left and right, obtaining a matrix representation of `Q` via `Matrix(Q)` (or `Q*I`) and indexing into the matrix representation all work. In contrast, addition and subtraction as well as more generally broadcasting over elements in the matrix representation fail because that would be highly inefficient. For such use cases, consider computing the matrix representation up front and cache it for future reuse.

## Pivoting Strategies

Several of Julia's matrix factorizations support pivoting, which can be used to improve their numerical stability. In fact, some matrix factorizations, such as the LU factorization, may fail without pivoting.

In pivoting, first, a pivot element with good numerical properties is chosen based on a pivoting strategy. Next, the rows and columns of the original matrix are permuted to bring the chosen element in place for subsequent computation. Furthermore, the process is repeated for each stage of the factorization.

Consequently, besides the conventional matrix factors, the outputs of pivoted factorization schemes also include permutation matrices.

In the following, the pivoting strategies implemented in Julia are briefly described. Note that not all matrix factorizations may support them. Consult the documentation of the respective matrix factorization for details on the supported pivoting strategies.

See also `LinearAlgebra.ZeroPivotException`.

`LinearAlgebra.NoPivot` — Type

    NoPivot

Pivoting is not performed. This is the default strategy for `cholesky` and `qr` factorizations. Note, however, that other matrix factorizations such as the LU factorization may fail without pivoting, and may also be numerically unstable for floating-point matrices in the face of roundoff error. In such cases, this pivot strategy is mainly useful for pedagogical purposes.

source
`LinearAlgebra.RowNonZero` — Type

    RowNonZero

First non-zero element in the remaining rows is chosen as the pivot element.

Beware that for floating-point matrices, the resulting LU algorithm is numerically unstable — this strategy is mainly useful for comparison to hand calculations (which typically use this strategy) or for other algebraic types (e.g. rational numbers) not susceptible to roundoff errors. Otherwise, the default `RowMaximum` pivoting strategy should be generally preferred in Gaussian elimination.

Note that the element type of the matrix must admit an `iszero` method.

source
`LinearAlgebra.RowMaximum` — Type

    RowMaximum

A row (and potentially also column) pivot is chosen based on a maximum property. This is the default strategy for LU factorization and for pivoted Cholesky factorization (though [`NoPivot`] is the default for `cholesky`).

In the LU case, the maximum-magnitude element within the current column in the remaining rows is chosen as the pivot element. This is sometimes referred to as the "partial pivoting" algorithm. In this case, the element type of the matrix must admit an `abs` method, whose result type must admit a `<` method.

In the Cholesky case, the maximal element among the remaining diagonal elements is chosen as the pivot element. This is sometimes referred to as the "diagonal pivoting" algorithm, and leads to complete pivoting (i.e., of both rows and columns by the same permutation). In this case, the (real part of the) element type of the matrix must admit a `<` method.

source
`LinearAlgebra.ColumnNorm` — Type

    ColumnNorm

The column with the maximum norm is used for subsequent computation. This is used for pivoted QR factorization.

Note that the element type of the matrix must admit `norm` and `abs` methods, whose respective result types must admit a `<` method.

source

## Standard functions

Linear algebra functions in Julia are largely implemented by calling functions from LAPACK. Sparse matrix factorizations call functions from SuiteSparse. Other sparse solvers are available as Julia packages.

`Base.:*` — Method

    *(A::AbstractMatrix, B::AbstractMatrix)

Matrix multiplication.

Examples

    julia> [1 1; 0 1] * [1 0; 1 1]
    2×2 Matrix{Int64}:
     2  1
     1  1

source
`Base.:*` — Method

    *(A, B::AbstractMatrix, C)
    A * B * C * D

Chained multiplication of 3 or 4 matrices is done in the most efficient sequence, based on the sizes of the arrays. That is, the number of scalar multiplications needed for `(A * B) * C` (with 3 dense matrices) is compared to that for `A * (B * C)` to choose which of these to execute.

If the last factor is a vector, or the first a transposed vector, then it is efficient to deal with these first. In particular `x' * B * y` means `(x' * B) * y` for an ordinary column-major `B::Matrix`. Unlike `dot(x, B, y)`, this allocates an intermediate array.

If the first or last factor is a number, this will be fused with the matrix multiplication, using 5-arg `mul!`.

See also `muladd`, `dot`.

Julia 1.7

These optimisations require at least Julia 1.7.

source
`Base.:\` — Method

    \(A, B)

Matrix division using a polyalgorithm. For input matrices `A` and `B`, the result `X` is such that `A*X == B` when `A` is square. The solver that is used depends upon the structure of `A`. If `A` is upper or lower triangular (or diagonal), no factorization of `A` is required and the system is solved with either forward or backward substitution. For non-triangular square matrices, an LU factorization is used.

For rectangular `A` the result is the minimum-norm least squares solution computed by a pivoted QR factorization of `A` and a rank estimate of `A` based on the R factor.

When `A` is sparse, a similar polyalgorithm is used. For indefinite matrices, the `LDLt` factorization does not use pivoting during the numerical factorization and therefore the procedure can fail even for invertible matrices.

See also: `factorize`, `pinv`.

Examples

    julia> A = [1 0; 1 -2]; B = [32; -4];

    julia> X = A \ B
    2-element Vector{Float64}:
     32.0
     18.0

    julia> A * X == B
    true

source
`Base.:/` — Method

    A / B

Matrix right-division: `A / B` is equivalent to `(B' \ A')'` where `\` is the left-division operator. For square matrices, the result `X` is such that `A == X*B`.

See also: `rdiv!`.

Examples

    julia> A = Float64[1 4 5; 3 9 2]; B = Float64[1 4 2; 3 4 2; 8 7 1];

    julia> X = A / B
    2×3 Matrix{Float64}:
     -0.65   3.75  -1.2
      3.25  -2.75   1.0

    julia> isapprox(A, X*B)
    true

    julia> isapprox(X, A*pinv(B))
    true

source
`LinearAlgebra.SingularException` — Type

    SingularException

Exception thrown when the input matrix has one or more zero-valued eigenvalues, and is not invertible. A linear solve involving such a matrix cannot be computed. The `info` field indicates the location of (one of) the singular value(s).

source
`LinearAlgebra.PosDefException` — Type

    PosDefException

Exception thrown when the input matrix was not positive definite. Some linear algebra functions and factorizations are only applicable to positive definite matrices. The `info` field indicates the location of (one of) the eigenvalue(s) which is (are) less than/equal to 0.

source
`LinearAlgebra.ZeroPivotException` — Type

    ZeroPivotException <: Exception

Exception thrown when a matrix factorization/solve encounters a zero in a pivot (diagonal) position and cannot proceed. This may not mean that the matrix is singular: it may be fruitful to switch to a different factorization such as pivoted LU that can re-order variables to eliminate spurious zero pivots. The `info` field indicates the location of (one of) the zero pivot(s).

source
`LinearAlgebra.RankDeficientException` — Type

    RankDeficientException

Exception thrown when the input matrix is rank deficient. Some linear algebra functions, such as the Cholesky decomposition, are only applicable to matrices that are not rank deficient. The `info` field indicates the computed rank of the matrix.

source
`LinearAlgebra.LAPACKException` — Type

    LAPACKException

Generic LAPACK exception thrown either during direct calls to the LAPACK functions or during calls to other functions that use the LAPACK functions internally but lack specialized error handling. The `info` field contains additional information on the underlying error and depends on the LAPACK function that was invoked.

source
`LinearAlgebra.dot` — Function

    dot(x, y)
    x ⋅ y

Compute the dot product between two vectors. For complex vectors, the first vector is conjugated.

`dot` also works on arbitrary iterable objects, including arrays of any dimension, as long as `dot` is defined on the elements.

`dot` is semantically equivalent to `sum(dot(vx,vy) for (vx,vy) in zip(x, y))`, with the added restriction that the arguments must have equal lengths.

`x ⋅ y` (where `⋅` can be typed by tab-completing `\cdot` in the REPL) is a synonym for `dot(x, y)`.

Examples

    julia> dot([1; 1], [2; 3])
    5

    julia> dot([im; im], [1; 1])
    0 - 2im

    julia> dot(1:5, 2:6)
    70

    julia> x = fill(2., (5,5));

    julia> y = fill(3., (5,5));

    julia> dot(x, y)
    150.0

source
`LinearAlgebra.dot` — Method

    dot(x, A, y)

Compute the generalized dot product `dot(x, A*y)` between two vectors `x` and `y`, without storing the intermediate result of `A*y`. As for the two-argument `dot(_,_)`, this acts recursively. Moreover, for complex vectors, the first vector is conjugated.

Julia 1.4

Three-argument `dot` requires at least Julia 1.4.

Examples

    julia> dot([1; 1], [1 2; 3 4], [2; 3])
    26

    julia> dot(1:5, reshape(1:25, 5, 5), 2:6)
    4850

    julia> ⋅(1:5, reshape(1:25, 5, 5), 2:6) == dot(1:5, reshape(1:25, 5, 5), 2:6)
    true

source
`LinearAlgebra.cross` — Function

    cross(x, y)
    ×(x,y)

Compute the cross product of two 3-vectors.

Examples

    julia> a = [0;1;0]
    3-element Vector{Int64}:
     0
     1
     0

    julia> b = [0;0;1]
    3-element Vector{Int64}:
     0
     0
     1

    julia> cross(a,b)
    3-element Vector{Int64}:
     1
     0
     0

source
`LinearAlgebra.axpy!` — Function

    axpy!(α, x::AbstractArray, y::AbstractArray)

Overwrite `y` with `x * α + y` and return `y`. If `x` and `y` have the same axes, it's equivalent with `y .+= x .* a`.

Examples

    julia> x = [1; 2; 3];

    julia> y = [4; 5; 6];

    julia> axpy!(2, x, y)
    3-element Vector{Int64}:
      6
      9
     12

source
`LinearAlgebra.axpby!` — Function

    axpby!(α, x::AbstractArray, β, y::AbstractArray)

Overwrite `y` with `x * α + y * β` and return `y`. If `x` and `y` have the same axes, it's equivalent with `y .= x .* a .+ y .* β`.

Examples

    julia> x = [1; 2; 3];

    julia> y = [4; 5; 6];

    julia> axpby!(2, x, 2, y)
    3-element Vector{Int64}:
     10
     14
     18

source
`LinearAlgebra.rotate!` — Function

    rotate!(x, y, c, s)

Overwrite `x` with `c*x + s*y` and `y` with `-conj(s)*x + c*y`. Returns `x` and `y`.

Julia 1.5

`rotate!` requires at least Julia 1.5.

source
`LinearAlgebra.reflect!` — Function

    reflect!(x, y, c, s)

Overwrite `x` with `c*x + s*y` and `y` with `conj(s)*x - c*y`. Returns `x` and `y`.

Julia 1.5

`reflect!` requires at least Julia 1.5.

source
`LinearAlgebra.factorize` — Function

    factorize(A)

Compute a convenient factorization of `A`, based upon the type of the input matrix. If `A` is passed as a generic matrix, `factorize` checks to see if it is symmetric/triangular/etc. To this end, `factorize` may check every element of `A` to verify/rule out each property. It will short-circuit as soon as it can rule out symmetry/triangular structure. The return value can be reused for efficient solving of multiple systems. For example: `A=factorize(A); x=A\b; y=A\C`.

Properties of `A` | type of factorization
--- | ---
Dense Symmetric/Hermitian | Bunch-Kaufman (see `bunchkaufman`)
Sparse Symmetric/Hermitian | LDLt (see `ldlt`)
Triangular | Triangular
Diagonal | Diagonal
Bidiagonal | Bidiagonal
Tridiagonal | LU (see `lu`)
Symmetric real tridiagonal | LDLt (see `ldlt`)
General square | LU (see `lu`)
General non-square | QR (see `qr`)

Examples

    julia> A = Array(Bidiagonal(fill(1.0, (5, 5)), :U))
    5×5 Matrix{Float64}:
     1.0  1.0  0.0  0.0  0.0
     0.0  1.0  1.0  0.0  0.0
     0.0  0.0  1.0  1.0  0.0
     0.0  0.0  0.0  1.0  1.0
     0.0  0.0  0.0  0.0  1.0

    julia> factorize(A) # factorize will check to see that A is already factorized
    5×5 Bidiagonal{Float64, Vector{Float64}}:
     1.0  1.0   ⋅    ⋅    ⋅
      ⋅   1.0  1.0   ⋅    ⋅
      ⋅    ⋅   1.0  1.0   ⋅
      ⋅    ⋅    ⋅   1.0  1.0
      ⋅    ⋅    ⋅    ⋅   1.0

This returns a `5×5 Bidiagonal{Float64}`, which can now be passed to other linear algebra functions (e.g. eigensolvers) which will use specialized methods for `Bidiagonal` types.

source
`LinearAlgebra.Diagonal` — Type

    Diagonal(V::AbstractVector)

Construct a lazy matrix with `V` as its diagonal.

See also `UniformScaling` for the lazy identity matrix `I`, `diagm` to make a dense matrix, and `diag` to extract diagonal elements.

Examples

    julia> d = Diagonal([1, 10, 100])
    3×3 Diagonal{Int64, Vector{Int64}}:
     1   ⋅    ⋅
     ⋅  10    ⋅
     ⋅   ⋅  100

    julia> diagm([7, 13])
    2×2 Matrix{Int64}:
     7   0
     0  13

    julia> ans + I
    2×2 Matrix{Int64}:
     8   0
     0  14

    julia> I(2)
    2×2 Diagonal{Bool, Vector{Bool}}:
     1  ⋅
     ⋅  1

Note

A one-column matrix is not treated like a vector, but instead calls the method `Diagonal(A::AbstractMatrix)` which extracts 1-element `diag(A)`:

    julia> A = transpose([7.0 13.0])
    2×1 transpose(::Matrix{Float64}) with eltype Float64:
      7.0
     13.0

    julia> Diagonal(A)
    1×1 Diagonal{Float64, Vector{Float64}}:
     7.0

source

    Diagonal(A::AbstractMatrix)

Construct a matrix from the principal diagonal of `A`. The input matrix `A` may be rectangular, but the output will be square.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> D = Diagonal(A)
    2×2 Diagonal{Int64, Vector{Int64}}:
     1  ⋅
     ⋅  4

    julia> A = [1 2 3; 4 5 6]
    2×3 Matrix{Int64}:
     1  2  3
     4  5  6

    julia> Diagonal(A)
    2×2 Diagonal{Int64, Vector{Int64}}:
     1  ⋅
     ⋅  5

source

    Diagonal{T}(undef, n)

Construct an uninitialized `Diagonal{T}` of length `n`. See `undef`.

source
`LinearAlgebra.Bidiagonal` — Type

    Bidiagonal(dv::V, ev::V, uplo::Symbol) where V <: AbstractVector

Constructs an upper (`uplo=:U`) or lower (`uplo=:L`) bidiagonal matrix using the given diagonal (`dv`) and off-diagonal (`ev`) vectors. The result is of type `Bidiagonal` and provides efficient specialized linear solvers, but may be converted into a regular matrix with `convert(Array, _)` (or `Array(_)` for short). The length of `ev` must be one less than the length of `dv`.

Examples

    julia> dv = [1, 2, 3, 4]
    4-element Vector{Int64}:
     1
     2
     3
     4

    julia> ev = [7, 8, 9]
    3-element Vector{Int64}:
     7
     8
     9

    julia> Bu = Bidiagonal(dv, ev, :U) # ev is on the first superdiagonal
    4×4 Bidiagonal{Int64, Vector{Int64}}:
     1  7  ⋅  ⋅
     ⋅  2  8  ⋅
     ⋅  ⋅  3  9
     ⋅  ⋅  ⋅  4

    julia> Bl = Bidiagonal(dv, ev, :L) # ev is on the first subdiagonal
    4×4 Bidiagonal{Int64, Vector{Int64}}:
     1  ⋅  ⋅  ⋅
     7  2  ⋅  ⋅
     ⋅  8  3  ⋅
     ⋅  ⋅  9  4

source

    Bidiagonal(A, uplo::Symbol)

Construct a `Bidiagonal` matrix from the main diagonal of `A` and its first super- (if `uplo=:U`) or sub-diagonal (if `uplo=:L`).

Examples

    julia> A = [1 1 1 1; 2 2 2 2; 3 3 3 3; 4 4 4 4]
    4×4 Matrix{Int64}:
     1  1  1  1
     2  2  2  2
     3  3  3  3
     4  4  4  4

    julia> Bidiagonal(A, :U) # contains the main diagonal and first superdiagonal of A
    4×4 Bidiagonal{Int64, Vector{Int64}}:
     1  1  ⋅  ⋅
     ⋅  2  2  ⋅
     ⋅  ⋅  3  3
     ⋅  ⋅  ⋅  4

    julia> Bidiagonal(A, :L) # contains the main diagonal and first subdiagonal of A
    4×4 Bidiagonal{Int64, Vector{Int64}}:
     1  ⋅  ⋅  ⋅
     2  2  ⋅  ⋅
     ⋅  3  3  ⋅
     ⋅  ⋅  4  4

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`LinearAlgebra.SymTridiagonal` — Type

    SymTridiagonal(dv::V, ev::V) where V <: AbstractVector

Construct a symmetric tridiagonal matrix from the diagonal (`dv`) and first sub/super-diagonal (`ev`), respectively. The result is of type `SymTridiagonal` and provides efficient specialized eigensolvers, but may be converted into a regular matrix with `convert(Array, _)` (or `Array(_)` for short).

For `SymTridiagonal` block matrices, the elements of `dv` are symmetrized. The argument `ev` is interpreted as the superdiagonal. Blocks from the subdiagonal are (materialized) transpose of the corresponding superdiagonal blocks.

Examples

    julia> dv = [1, 2, 3, 4]
    4-element Vector{Int64}:
     1
     2
     3
     4

    julia> ev = [7, 8, 9]
    3-element Vector{Int64}:
     7
     8
     9

    julia> SymTridiagonal(dv, ev)
    4×4 SymTridiagonal{Int64, Vector{Int64}}:
     1  7  ⋅  ⋅
     7  2  8  ⋅
     ⋅  8  3  9
     ⋅  ⋅  9  4

    julia> A = SymTridiagonal(fill([1 2; 3 4], 3), fill([1 2; 3 4], 2));

    julia> A[1,1]
    2×2 Symmetric{Int64, Matrix{Int64}}:
     1  2
     2  4

    julia> A[1,2]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> A[2,1]
    2×2 Matrix{Int64}:
     1  3
     2  4

source

    SymTridiagonal(A::AbstractMatrix)

Construct a symmetric tridiagonal matrix from the diagonal and first superdiagonal of the symmetric matrix `A`.

Examples

    julia> A = [1 2 3; 2 4 5; 3 5 6]
    3×3 Matrix{Int64}:
     1  2  3
     2  4  5
     3  5  6

    julia> SymTridiagonal(A)
    3×3 SymTridiagonal{Int64, Vector{Int64}}:
     1  2  ⋅
     2  4  5
     ⋅  5  6

    julia> B = reshape([[1 2; 2 3], [1 2; 3 4], [1 3; 2 4], [1 2; 2 3]], 2, 2);

    julia> SymTridiagonal(B)
    2×2 SymTridiagonal{Matrix{Int64}, Vector{Matrix{Int64}}}:
     [1 2; 2 3]  [1 3; 2 4]
     [1 2; 3 4]  [1 2; 2 3]

source
`LinearAlgebra.Tridiagonal` — Type

    Tridiagonal(dl::V, d::V, du::V) where V <: AbstractVector

Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively. The result is of type `Tridiagonal` and provides efficient specialized linear solvers, but may be converted into a regular matrix with `convert(Array, _)` (or `Array(_)` for short). The lengths of `dl` and `du` must be one less than the length of `d`.

Note

The subdiagonal `dl` and the superdiagonal `du` must not be aliased to each other. If aliasing is detected, the constructor will use a copy of `du` as its argument.

Examples

    julia> dl = [1, 2, 3];

    julia> du = [4, 5, 6];

    julia> d = [7, 8, 9, 0];

    julia> Tridiagonal(dl, d, du)
    4×4 Tridiagonal{Int64, Vector{Int64}}:
     7  4  ⋅  ⋅
     1  8  5  ⋅
     ⋅  2  9  6
     ⋅  ⋅  3  0

source

    Tridiagonal(A)

Construct a tridiagonal matrix from the first sub-diagonal, diagonal and first super-diagonal of the matrix `A`.

Examples

    julia> A = [1 2 3 4; 1 2 3 4; 1 2 3 4; 1 2 3 4]
    4×4 Matrix{Int64}:
     1  2  3  4
     1  2  3  4
     1  2  3  4
     1  2  3  4

    julia> Tridiagonal(A)
    4×4 Tridiagonal{Int64, Vector{Int64}}:
     1  2  ⋅  ⋅
     1  2  3  ⋅
     ⋅  2  3  4
     ⋅  ⋅  3  4

source
`LinearAlgebra.Symmetric` — Type

    Symmetric(A::AbstractMatrix, uplo::Symbol=:U)

Construct a `Symmetric` view of the upper (if `uplo = :U`) or lower (if `uplo = :L`) triangle of the matrix `A`.

`Symmetric` views are mainly useful for real-symmetric matrices, for which specialized algorithms (e.g. for eigenproblems) are enabled for `Symmetric` types. More generally, see also `Hermitian(A)` for Hermitian matrices `A == A'`, which is effectively equivalent to `Symmetric` for real matrices but is also useful for complex matrices. (Whereas complex `Symmetric` matrices are supported but have few if any specialized algorithms.)

To compute the symmetric part of a real matrix, or more generally the Hermitian part `(A + A') / 2` of a real or complex matrix `A`, use `hermitianpart`.

Examples

    julia> A = [1 2 3; 4 5 6; 7 8 9]
    3×3 Matrix{Int64}:
     1  2  3
     4  5  6
     7  8  9

    julia> Supper = Symmetric(A)
    3×3 Symmetric{Int64, Matrix{Int64}}:
     1  2  3
     2  5  6
     3  6  9

    julia> Slower = Symmetric(A, :L)
    3×3 Symmetric{Int64, Matrix{Int64}}:
     1  4  7
     4  5  8
     7  8  9

    julia> hermitianpart(A)
    3×3 Hermitian{Float64, Matrix{Float64}}:
     1.0  3.0  5.0
     3.0  5.0  7.0
     5.0  7.0  9.0

Note that `Supper` will not be equal to `Slower` unless `A` is itself symmetric (e.g. if `A == transpose(A)`).

source
`LinearAlgebra.Hermitian` — Type

    Hermitian(A::AbstractMatrix, uplo::Symbol=:U)

Construct a `Hermitian` view of the upper (if `uplo = :U`) or lower (if `uplo = :L`) triangle of the matrix `A`.

To compute the Hermitian part of `A`, use `hermitianpart`.

Examples

    julia> A = [1 2+2im 3-3im; 4 5 6-6im; 7 8+8im 9]
    3×3 Matrix{Complex{Int64}}:
     1+0im  2+2im  3-3im
     4+0im  5+0im  6-6im
     7+0im  8+8im  9+0im

    julia> Hupper = Hermitian(A)
    3×3 Hermitian{Complex{Int64}, Matrix{Complex{Int64}}}:
     1+0im  2+2im  3-3im
     2-2im  5+0im  6-6im
     3+3im  6+6im  9+0im

    julia> Hlower = Hermitian(A, :L)
    3×3 Hermitian{Complex{Int64}, Matrix{Complex{Int64}}}:
     1+0im  4+0im  7+0im
     4+0im  5+0im  8-8im
     7+0im  8+8im  9+0im

    julia> hermitianpart(A)
    3×3 Hermitian{ComplexF64, Matrix{ComplexF64}}:
     1.0+0.0im  3.0+1.0im  5.0-1.5im
     3.0-1.0im  5.0+0.0im  7.0-7.0im
     5.0+1.5im  7.0+7.0im  9.0+0.0im

Note that `Hupper` will not be equal to `Hlower` unless `A` is itself Hermitian (e.g. if `A == adjoint(A)`).

All non-real parts of the diagonal will be ignored.

    Hermitian(fill(complex(1,1), 1, 1)) == fill(1, 1, 1)

source
`LinearAlgebra.LowerTriangular` — Type

    LowerTriangular(A::AbstractMatrix)

Construct a `LowerTriangular` view of the matrix `A`.

Examples

    julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0]
    3×3 Matrix{Float64}:
     1.0  2.0  3.0
     4.0  5.0  6.0
     7.0  8.0  9.0

    julia> LowerTriangular(A)
    3×3 LowerTriangular{Float64, Matrix{Float64}}:
     1.0   ⋅    ⋅
     4.0  5.0   ⋅
     7.0  8.0  9.0

source
`LinearAlgebra.UpperTriangular` — Type

    UpperTriangular(A::AbstractMatrix)

Construct an `UpperTriangular` view of the matrix `A`.

Examples

    julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0]
    3×3 Matrix{Float64}:
     1.0  2.0  3.0
     4.0  5.0  6.0
     7.0  8.0  9.0

    julia> UpperTriangular(A)
    3×3 UpperTriangular{Float64, Matrix{Float64}}:
     1.0  2.0  3.0
      ⋅   5.0  6.0
      ⋅    ⋅   9.0

source
`LinearAlgebra.UnitLowerTriangular` — Type

    UnitLowerTriangular(A::AbstractMatrix)

Construct a `UnitLowerTriangular` view of the matrix `A`. Such a view has the `oneunit` of the `eltype` of `A` on its diagonal.

Examples

    julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0]
    3×3 Matrix{Float64}:
     1.0  2.0  3.0
     4.0  5.0  6.0
     7.0  8.0  9.0

    julia> UnitLowerTriangular(A)
    3×3 UnitLowerTriangular{Float64, Matrix{Float64}}:
     1.0   ⋅    ⋅
     4.0  1.0   ⋅
     7.0  8.0  1.0

source
`LinearAlgebra.UnitUpperTriangular` — Type

    UnitUpperTriangular(A::AbstractMatrix)

Construct an `UnitUpperTriangular` view of the matrix `A`. Such a view has the `oneunit` of the `eltype` of `A` on its diagonal.

Examples

    julia> A = [1.0 2.0 3.0; 4.0 5.0 6.0; 7.0 8.0 9.0]
    3×3 Matrix{Float64}:
     1.0  2.0  3.0
     4.0  5.0  6.0
     7.0  8.0  9.0

    julia> UnitUpperTriangular(A)
    3×3 UnitUpperTriangular{Float64, Matrix{Float64}}:
     1.0  2.0  3.0
      ⋅   1.0  6.0
      ⋅    ⋅   1.0

source
`LinearAlgebra.UpperHessenberg` — Type

    UpperHessenberg(A::AbstractMatrix)

Construct an `UpperHessenberg` view of the matrix `A`. Entries of `A` below the first subdiagonal are ignored.

Julia 1.3

This type was added in Julia 1.3.

Efficient algorithms are implemented for `H \ b`, `det(H)`, and similar.

See also the `hessenberg` function to factor any matrix into a similar upper-Hessenberg matrix.

If `F::Hessenberg` is the factorization object, the unitary matrix can be accessed with `F.Q` and the Hessenberg matrix with `F.H`. When `Q` is extracted, the resulting type is the `HessenbergQ` object, and may be converted to a regular matrix with `convert(Array, _)` (or `Array(_)` for short).

Iterating the decomposition produces the factors `F.Q` and `F.H`.

Examples

    julia> A = [1 2 3 4; 5 6 7 8; 9 10 11 12; 13 14 15 16]
    4×4 Matrix{Int64}:
      1   2   3   4
      5   6   7   8
      9  10  11  12
     13  14  15  16

    julia> UpperHessenberg(A)
    4×4 UpperHessenberg{Int64, Matrix{Int64}}:
     1   2   3   4
     5   6   7   8
     ⋅  10  11  12
     ⋅   ⋅  15  16

source
`LinearAlgebra.UniformScaling` — Type

    UniformScaling{T<:Number}

Generically sized uniform scaling operator defined as a scalar times the identity operator, `λ*I`. Although without an explicit `size`, it acts similarly to a matrix in many cases and includes support for some indexing. See also `I`.

Julia 1.6

Indexing using ranges is available as of Julia 1.6.

Examples

    julia> J = UniformScaling(2.)
    UniformScaling{Float64}
    2.0*I

    julia> A = [1. 2.; 3. 4.]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> J*A
    2×2 Matrix{Float64}:
     2.0  4.0
     6.0  8.0

    julia> J[1:2, 1:2]
    2×2 Matrix{Float64}:
     2.0  0.0
     0.0  2.0

source
`LinearAlgebra.I` — Constant

    I

An object of type `UniformScaling`, representing an identity matrix of any size.

Examples

    julia> fill(1, (5,6)) * I == fill(1, (5,6))
    true

    julia> [1 2im 3; 1im 2 3] * I
    2×3 Matrix{Complex{Int64}}:
     1+0im  0+2im  3+0im
     0+1im  2+0im  3+0im

source
`LinearAlgebra.UniformScaling` — Method

    (I::UniformScaling)(n::Integer)

Construct a `Diagonal` matrix from a `UniformScaling`.

Julia 1.2

This method is available as of Julia 1.2.

Examples

    julia> I(3)
    3×3 Diagonal{Bool, Vector{Bool}}:
     1  ⋅  ⋅
     ⋅  1  ⋅
     ⋅  ⋅  1

    julia> (0.7*I)(3)
    3×3 Diagonal{Float64, Vector{Float64}}:
     0.7   ⋅    ⋅
      ⋅   0.7   ⋅
      ⋅    ⋅   0.7

source
`LinearAlgebra.Factorization` — Type

    LinearAlgebra.Factorization

Abstract type for matrix factorizations a.k.a. matrix decompositions. See online documentation for a list of available matrix factorizations.

source
`LinearAlgebra.LU` — Type

    LU <: Factorization

Matrix factorization type of the `LU` factorization of a square matrix `A`. This is the return type of `lu`, the corresponding matrix factorization function.

The individual components of the factorization `F::LU` can be accessed via `getproperty`:

Component | Description
--- | ---
`F.L` | `L` (unit lower triangular) part of `LU`
`F.U` | `U` (upper triangular) part of `LU`
`F.p` | (right) permutation `Vector`
`F.P` | (right) permutation `Matrix`

Iterating the factorization produces the components `F.L`, `F.U`, and `F.p`.

Examples

    julia> A = [4 3; 6 3]
    2×2 Matrix{Int64}:
     4  3
     6  3

    julia> F = lu(A)
    LU{Float64, Matrix{Float64}, Vector{Int64}}
    L factor:
    2×2 Matrix{Float64}:
     1.0       0.0
     0.666667  1.0
    U factor:
    2×2 Matrix{Float64}:
     6.0  3.0
     0.0  1.0

    julia> F.L * F.U == A[F.p, :]
    true

    julia> l, u, p = lu(A); # destructuring via iteration

    julia> l == F.L && u == F.U && p == F.p
    true

source
`LinearAlgebra.lu` — Function

    lu(A::AbstractSparseMatrixCSC; check = true, q = nothing, control = get_umfpack_control()) -> F::UmfpackLU

Compute the LU factorization of a sparse matrix `A`.

For sparse `A` with real or complex element type, the return type of `F` is `UmfpackLU{Tv, Ti}`, with `Tv` = `Float64` or `ComplexF64` respectively and `Ti` is an integer type (`Int32` or `Int64`).

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

The permutation `q` can either be a permutation vector or `nothing`. If no permutation vector is provided or `q` is `nothing`, UMFPACK's default is used. If the permutation is not zero-based, a zero-based copy is made.

The `control` vector defaults to the Julia SparseArrays package's default configuration for UMFPACK (NB: this is modified from the UMFPACK defaults to disable iterative refinement), but can be changed by passing a vector of length `UMFPACK_CONTROL`, see the UMFPACK manual for possible configurations. For example to reenable iterative refinement:

    umfpack_control = SparseArrays.UMFPACK.get_umfpack_control(Float64, Int64) # read Julia default configuration for a Float64 sparse matrix
    SparseArrays.UMFPACK.show_umf_ctrl(umfpack_control) # optional - display values
    umfpack_control[SparseArrays.UMFPACK.JL_UMFPACK_IRSTEP] = 2.0 # reenable iterative refinement (2 is UMFPACK default max iterative refinement steps)

    Alu = lu(A; control = umfpack_control)
    x = Alu \ b   # solve Ax = b, including UMFPACK iterative refinement

The individual components of the factorization `F` can be accessed by indexing:

Component | Description
--- | ---
`L` | `L` (lower triangular) part of `LU`
`U` | `U` (upper triangular) part of `LU`
`p` | right permutation `Vector`
`q` | left permutation `Vector`
`Rs` | `Vector` of scaling factors
`:` | `(L,U,p,q,Rs)` components

The relation between `F` and `A` is

`F.L*F.U == (F.Rs .* A)[F.p, F.q]`

`F` further supports the following functions:

- `\`
- `det`

See also `lu!`

Note

`lu(A::AbstractSparseMatrixCSC)` uses the UMFPACK[ACM832] library that is part of SuiteSparse. As this library only supports sparse matrices with `Float64` or `ComplexF64` elements, `lu` converts `A` into a copy that is of type `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{ComplexF64}` as appropriate.

source

    lu(A, pivot = RowMaximum(); check = true, allowsingular = false) -> F::LU

Compute the LU factorization of `A`.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

By default, with `check = true`, an error is also thrown when the decomposition produces valid factors, but the upper-triangular factor `U` is rank-deficient. This may be changed by passing `allowsingular = true`.

In most cases, if `A` is a subtype `S` of `AbstractMatrix{T}` with an element type `T` supporting `+`, `-`, `*` and `/`, the return type is `LU{T,S{T}}`.

In general, LU factorization involves a permutation of the rows of the matrix (corresponding to the `F.p` output described below), known as "pivoting" (because it corresponds to choosing which row contains the "pivot", the diagonal entry of `F.U`). One of the following pivoting strategies can be selected via the optional `pivot` argument:

- `RowMaximum()` (default): the standard pivoting strategy; the pivot corresponds to the element of maximum absolute value among the remaining, to be factorized rows. This pivoting strategy requires the element type to also support `abs` and `<`. (This is generally the only numerically stable option for floating-point matrices.)
- `RowNonZero()`: the pivot corresponds to the first non-zero element among the remaining, to be factorized rows. (This corresponds to the typical choice in hand calculations, and is also useful for more general algebraic number types that support `iszero` but not `abs` or `<`.)
- `NoPivot()`: pivoting turned off (will fail if a zero entry is encountered in a pivot position, even when `allowsingular = true`).

The individual components of the factorization `F` can be accessed via `getproperty`:

Component | Description
--- | ---
`F.L` | `L` (lower triangular) part of `LU`
`F.U` | `U` (upper triangular) part of `LU`
`F.p` | (right) permutation `Vector`
`F.P` | (right) permutation `Matrix`

Iterating the factorization produces the components `F.L`, `F.U`, and `F.p`.

The relationship between `F` and `A` is

`F.L*F.U == A[F.p, :]`

`F` further supports the following functions:

Supported function | `LU` | `LU{T,Tridiagonal{T}}`
--- | --- | ---
`/` | ✓ |
`\` | ✓ | ✓
`inv` | ✓ | ✓
`det` | ✓ | ✓
`logdet` | ✓ | ✓
`logabsdet` | ✓ | ✓
`size` | ✓ | ✓

Julia 1.11

The `allowsingular` keyword argument was added in Julia 1.11.

Examples

    julia> A = [4 3; 6 3]
    2×2 Matrix{Int64}:
     4  3
     6  3

    julia> F = lu(A)
    LU{Float64, Matrix{Float64}, Vector{Int64}}
    L factor:
    2×2 Matrix{Float64}:
     1.0       0.0
     0.666667  1.0
    U factor:
    2×2 Matrix{Float64}:
     6.0  3.0
     0.0  1.0

    julia> F.L * F.U == A[F.p, :]
    true

    julia> l, u, p = lu(A); # destructuring via iteration

    julia> l == F.L && u == F.U && p == F.p
    true

    julia> lu([1 2; 1 2], allowsingular = true)
    LU{Float64, Matrix{Float64}, Vector{Int64}}
    L factor:
    2×2 Matrix{Float64}:
     1.0  0.0
     1.0  1.0
    U factor (rank-deficient):
    2×2 Matrix{Float64}:
     1.0  2.0
     0.0  0.0

source
`LinearAlgebra.lu!` — Function

    lu!(F::UmfpackLU, A::AbstractSparseMatrixCSC; check=true, reuse_symbolic=true, q=nothing) -> F::UmfpackLU

Compute the LU factorization of a sparse matrix `A`, reusing the symbolic factorization of an already existing LU factorization stored in `F`. Unless `reuse_symbolic` is set to false, the sparse matrix `A` must have an identical nonzero pattern as the matrix used to create the LU factorization `F`, otherwise an error is thrown. If the size of `A` and `F` differ, all vectors will be resized accordingly.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

The permutation `q` can either be a permutation vector or `nothing`. If no permutation vector is provided or `q` is `nothing`, UMFPACK's default is used. If the permutation is not zero based, a zero based copy is made.

See also `lu`

Note

`lu!(F::UmfpackLU, A::AbstractSparseMatrixCSC)` uses the UMFPACK library that is part of SuiteSparse. As this library only supports sparse matrices with `Float64` or `ComplexF64` elements, `lu!` will automatically convert the types to those set by the LU factorization or `SparseMatrixCSC{ComplexF64}` as appropriate.

Julia 1.5

`lu!` for `UmfpackLU` requires at least Julia 1.5.

Examples

    julia> A = sparse(Float64[1.0 2.0; 0.0 3.0]);

    julia> F = lu(A);

    julia> B = sparse(Float64[1.0 1.0; 0.0 1.0]);

    julia> lu!(F, B);

    julia> F \ ones(2)
    2-element Vector{Float64}:
     0.0
     1.0

source

    lu!(A, pivot = RowMaximum(); check = true, allowsingular = false) -> LU

`lu!` is the same as `lu`, but saves space by overwriting the input `A`, instead of creating a copy. An `InexactError` exception is thrown if the factorization produces a number not representable by the element type of `A`, e.g. for integer types.

Julia 1.11

The `allowsingular` keyword argument was added in Julia 1.11.

Examples

    julia> A = [4. 3.; 6. 3.]
    2×2 Matrix{Float64}:
     4.0  3.0
     6.0  3.0

    julia> F = lu!(A)
    LU{Float64, Matrix{Float64}, Vector{Int64}}
    L factor:
    2×2 Matrix{Float64}:
     1.0       0.0
     0.666667  1.0
    U factor:
    2×2 Matrix{Float64}:
     6.0  3.0
     0.0  1.0

    julia> iA = [4 3; 6 3]
    2×2 Matrix{Int64}:
     4  3
     6  3

    julia> lu!(iA)
    ERROR: InexactError: Int64(0.6666666666666666)
    Stacktrace:
    [...]

source
`LinearAlgebra.Cholesky` — Type

    Cholesky <: Factorization

Matrix factorization type of the Cholesky factorization of a dense symmetric/Hermitian positive definite matrix `A`. This is the return type of `cholesky`, the corresponding matrix factorization function.

The triangular Cholesky factor can be obtained from the factorization `F::Cholesky` via `F.L` and `F.U`, where `A ≈ F.U' * F.U ≈ F.L * F.L'`.

The following functions are available for `Cholesky` objects: `size`, `\`, `inv`, `det`, `logdet` and `isposdef`.

Iterating the decomposition produces the components `L` and `U`.

Examples

    julia> A = [4. 12. -16.; 12. 37. -43.; -16. -43. 98.]
    3×3 Matrix{Float64}:
       4.0   12.0  -16.0
      12.0   37.0  -43.0
     -16.0  -43.0   98.0

    julia> C = cholesky(A)
    Cholesky{Float64, Matrix{Float64}}
    U factor:
    3×3 UpperTriangular{Float64, Matrix{Float64}}:
     2.0  6.0  -8.0
      ⋅   1.0   5.0
      ⋅    ⋅    3.0

    julia> C.U
    3×3 UpperTriangular{Float64, Matrix{Float64}}:
     2.0  6.0  -8.0
      ⋅   1.0   5.0
      ⋅    ⋅    3.0

    julia> C.L
    3×3 LowerTriangular{Float64, Matrix{Float64}}:
      2.0   ⋅    ⋅
      6.0  1.0   ⋅
     -8.0  5.0  3.0

    julia> C.L * C.U == A
    true

    julia> l, u = C; # destructuring via iteration

    julia> l == C.L && u == C.U
    true

source
`LinearAlgebra.CholeskyPivoted` — Type

    CholeskyPivoted

Matrix factorization type of the pivoted Cholesky factorization of a dense symmetric/Hermitian positive semi-definite matrix `A`. This is the return type of `cholesky(_, ::RowMaximum)`, the corresponding matrix factorization function.

The triangular Cholesky factor can be obtained from the factorization `F::CholeskyPivoted` via `F.L` and `F.U`, and the permutation via `F.p`, where `A[F.p, F.p] ≈ Ur' * Ur ≈ Lr * Lr'` with `Ur = F.U[1:F.rank, :]` and `Lr = F.L[:, 1:F.rank]`, or alternatively `A ≈ Up' * Up ≈ Lp * Lp'` with `Up = F.U[1:F.rank, invperm(F.p)]` and `Lp = F.L[invperm(F.p), 1:F.rank]`.

The following functions are available for `CholeskyPivoted` objects: `size`, `\`, `inv`, `det`, and `rank`.

Iterating the decomposition produces the components `L` and `U`.

Examples

    julia> X = [1.0, 2.0, 3.0, 4.0];

    julia> A = X * X';

    julia> C = cholesky(A, RowMaximum(), check = false)
    CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}
    U factor with rank 1:
    4×4 UpperTriangular{Float64, Matrix{Float64}}:
     4.0  2.0  3.0  1.0
      ⋅   0.0  6.0  2.0
      ⋅    ⋅   9.0  3.0
      ⋅    ⋅    ⋅   1.0
    permutation:
    4-element Vector{Int64}:
     4
     2
     3
     1

    julia> C.U[1:C.rank, :]' * C.U[1:C.rank, :] ≈ A[C.p, C.p]
    true

    julia> l, u = C; # destructuring via iteration

    julia> l == C.L && u == C.U
    true

source
`LinearAlgebra.cholesky` — Function

    cholesky(A, NoPivot(); check = true) -> Cholesky

Compute the Cholesky factorization of a dense symmetric positive definite matrix `A` and return a `Cholesky` factorization. The matrix `A` can either be a `Symmetric` or `Hermitian` `AbstractMatrix` or a perfectly symmetric or Hermitian `AbstractMatrix`.

The triangular Cholesky factor can be obtained from the factorization `F` via `F.L` and `F.U`, where `A ≈ F.U' * F.U ≈ F.L * F.L'`.

The following functions are available for `Cholesky` objects: `size`, `\`, `inv`, `det`, `logdet` and `isposdef`.

If you have a matrix `A` that is slightly non-Hermitian due to roundoff errors in its construction, wrap it in `Hermitian(A)` before passing it to `cholesky` in order to treat it as perfectly Hermitian.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

Examples

    julia> A = [4. 12. -16.; 12. 37. -43.; -16. -43. 98.]
    3×3 Matrix{Float64}:
       4.0   12.0  -16.0
      12.0   37.0  -43.0
     -16.0  -43.0   98.0

    julia> C = cholesky(A)
    Cholesky{Float64, Matrix{Float64}}
    U factor:
    3×3 UpperTriangular{Float64, Matrix{Float64}}:
     2.0  6.0  -8.0
      ⋅   1.0   5.0
      ⋅    ⋅    3.0

    julia> C.U
    3×3 UpperTriangular{Float64, Matrix{Float64}}:
     2.0  6.0  -8.0
      ⋅   1.0   5.0
      ⋅    ⋅    3.0

    julia> C.L
    3×3 LowerTriangular{Float64, Matrix{Float64}}:
      2.0   ⋅    ⋅
      6.0  1.0   ⋅
     -8.0  5.0  3.0

    julia> C.L * C.U == A
    true

source

    cholesky(A, RowMaximum(); tol = 0.0, check = true) -> CholeskyPivoted

Compute the pivoted Cholesky factorization of a dense symmetric positive semi-definite matrix `A` and return a `CholeskyPivoted` factorization. The matrix `A` can either be a `Symmetric` or `Hermitian` `AbstractMatrix` or a perfectly symmetric or Hermitian `AbstractMatrix`.

The triangular Cholesky factor can be obtained from the factorization `F` via `F.L` and `F.U`, and the permutation via `F.p`, where `A[F.p, F.p] ≈ Ur' * Ur ≈ Lr * Lr'` with `Ur = F.U[1:F.rank, :]` and `Lr = F.L[:, 1:F.rank]`, or alternatively `A ≈ Up' * Up ≈ Lp * Lp'` with `Up = F.U[1:F.rank, invperm(F.p)]` and `Lp = F.L[invperm(F.p), 1:F.rank]`.

The following functions are available for `CholeskyPivoted` objects: `size`, `\`, `inv`, `det`, and `rank`.

The argument `tol` determines the tolerance for determining the rank. For negative values, the tolerance is equal to `eps()*size(A,1)*maximum(diag(A))`.

If you have a matrix `A` that is slightly non-Hermitian due to roundoff errors in its construction, wrap it in `Hermitian(A)` before passing it to `cholesky` in order to treat it as perfectly Hermitian.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

Examples

    julia> X = [1.0, 2.0, 3.0, 4.0];

    julia> A = X * X';

    julia> C = cholesky(A, RowMaximum(), check = false)
    CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}
    U factor with rank 1:
    4×4 UpperTriangular{Float64, Matrix{Float64}}:
     4.0  2.0  3.0  1.0
      ⋅   0.0  6.0  2.0
      ⋅    ⋅   9.0  3.0
      ⋅    ⋅    ⋅   1.0
    permutation:
    4-element Vector{Int64}:
     4
     2
     3
     1

    julia> C.U[1:C.rank, :]' * C.U[1:C.rank, :] ≈ A[C.p, C.p]
    true

    julia> l, u = C; # destructuring via iteration

    julia> l == C.L && u == C.U
    true

source

    cholesky(A::SparseMatrixCSC; shift = 0.0, check = true, perm = nothing) -> CHOLMOD.Factor

Compute the Cholesky factorization of a sparse positive definite matrix `A`. `A` must be a `SparseMatrixCSC` or a `Symmetric`/`Hermitian` view of a `SparseMatrixCSC`. Note that even if `A` doesn't have the type tag, it must still be symmetric or Hermitian. If `perm` is not given, a fill-reducing permutation is used. `F = cholesky(A)` is most frequently used to solve systems of equations with `F\b`, but also the methods `diag`, `det`, and `logdet` are defined for `F`. You can also extract individual factors from `F`, using `F.L`. However, since pivoting is on by default, the factorization is internally represented as `A == P'*L*L'*P` with a permutation matrix `P`; using just `L` without accounting for `P` will give incorrect answers. To include the effects of permutation, it's typically preferable to extract "combined" factors like `PtL = F.PtL` (the equivalent of `P'*L`) and `LtP = F.UP` (the equivalent of `L'*P`).

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

Setting the optional `shift` keyword argument computes the factorization of `A+shift*I` instead of `A`. If the `perm` argument is provided, it should be a permutation of `1:size(A,1)` giving the ordering to use (instead of CHOLMOD's default AMD ordering).

See also `ldlt` for a similar factorization that does not require positive definiteness, but can be significantly slower than `cholesky`.

Examples

In the following example, the fill-reducing permutation used is `[3, 2, 1]`. If `perm` is set to `1:3` to enforce no permutation, the number of nonzero elements in the factor is 6.

    julia> A = [2 1 1; 1 2 0; 1 0 2]
    3×3 Matrix{Int64}:
     2  1  1
     1  2  0
     1  0  2

    julia> C = cholesky(sparse(A))
    SparseArrays.CHOLMOD.Factor{Float64, Int64}
    type:    LLt
    method:  simplicial
    maxnnz:  5
    nnz:     5
    success: true

    julia> C.p
    3-element Vector{Int64}:
     3
     2
     1

    julia> L = sparse(C.L);

    julia> Matrix(L)
    3×3 Matrix{Float64}:
     1.41421   0.0       0.0
     0.0       1.41421   0.0
     0.707107  0.707107  1.0

    julia> L * L' ≈ A[C.p, C.p]
    true

    julia> P = sparse(1:3, C.p, ones(3))
    3×3 SparseMatrixCSC{Float64, Int64} with 3 stored entries:
      ⋅    ⋅   1.0
      ⋅   1.0   ⋅
     1.0   ⋅    ⋅

    julia> P' * L * L' * P ≈ A
    true

    julia> C = cholesky(sparse(A), perm=1:3)
    SparseArrays.CHOLMOD.Factor{Float64, Int64}
    type:    LLt
    method:  simplicial
    maxnnz:  6
    nnz:     6
    success: true

    julia> L = sparse(C.L);

    julia> Matrix(L)
    3×3 Matrix{Float64}:
     1.41421    0.0       0.0
     0.707107   1.22474   0.0
     0.707107  -0.408248  1.1547

    julia> L * L' ≈ A
    true

Note

This method uses the CHOLMOD[ACM887][DavisHager2009] library from SuiteSparse. CHOLMOD only supports real or complex types in single or double precision. Input matrices not of those element types will be converted to these types as appropriate.

Many other functions from CHOLMOD are wrapped but not exported from the `Base.SparseArrays.CHOLMOD` module.

source
`LinearAlgebra.cholesky!` — Function

    cholesky!(A::AbstractMatrix, NoPivot(); check = true) -> Cholesky

The same as `cholesky`, but saves space by overwriting the input `A`, instead of creating a copy. An `InexactError` exception is thrown if the factorization produces a number not representable by the element type of `A`, e.g. for integer types.

Examples

    julia> A = [1 2; 2 50]
    2×2 Matrix{Int64}:
     1   2
     2  50

    julia> cholesky!(A)
    ERROR: InexactError: Int64(6.782329983125268)
    Stacktrace:
    [...]

source

    cholesky!(A::AbstractMatrix, RowMaximum(); tol = 0.0, check = true) -> CholeskyPivoted

The same as `cholesky`, but saves space by overwriting the input `A`, instead of creating a copy. An `InexactError` exception is thrown if the factorization produces a number not representable by the element type of `A`, e.g. for integer types.

source

    cholesky!(F::CHOLMOD.Factor, A::SparseMatrixCSC; shift = 0.0, check = true) -> CHOLMOD.Factor

Compute the Cholesky ($LL'$) factorization of `A`, reusing the symbolic factorization `F`. `A` must be a `SparseMatrixCSC` or a `Symmetric`/ `Hermitian` view of a `SparseMatrixCSC`. Note that even if `A` doesn't have the type tag, it must still be symmetric or Hermitian.

See also `cholesky`.

Note

This method uses the CHOLMOD library from SuiteSparse, which only supports real or complex types in single or double precision. Input matrices not of those element types will be converted to these types as appropriate.

source
`LinearAlgebra.lowrankupdate` — Function

    lowrankupdate(C::Cholesky, v::AbstractVector) -> CC::Cholesky

Update a Cholesky factorization `C` with the vector `v`. If `A = C.U'C.U` then `CC = cholesky(C.U'C.U + v*v')` but the computation of `CC` only uses `O(n^2)` operations.

source

    lowrankupdate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor

Get an `LDLt` Factorization of `A + C*C'` given an `LDLt` or `LLt` factorization `F` of `A`.

The returned factor is always an `LDLt` factorization.

See also `lowrankupdate!`, `lowrankdowndate`, `lowrankdowndate!`.

source
`LinearAlgebra.lowrankdowndate` — Function

    lowrankdowndate(C::Cholesky, v::AbstractVector) -> CC::Cholesky

Downdate a Cholesky factorization `C` with the vector `v`. If `A = C.U'C.U` then `CC = cholesky(C.U'C.U - v*v')` but the computation of `CC` only uses `O(n^2)` operations.

source

    lowrankdowndate(F::CHOLMOD.Factor, C::AbstractArray) -> FF::CHOLMOD.Factor

Get an `LDLt` Factorization of `A + C*C'` given an `LDLt` or `LLt` factorization `F` of `A`.

The returned factor is always an `LDLt` factorization.

See also `lowrankdowndate!`, `lowrankupdate`, `lowrankupdate!`.

source
`LinearAlgebra.lowrankupdate!` — Function

    lowrankupdate!(C::Cholesky, v::AbstractVector) -> CC::Cholesky

Update a Cholesky factorization `C` with the vector `v`. If `A = C.U'C.U` then `CC = cholesky(C.U'C.U + v*v')` but the computation of `CC` only uses `O(n^2)` operations. The input factorization `C` is updated in place such that on exit `C == CC`. The vector `v` is destroyed during the computation.

source

    lowrankupdate!(F::CHOLMOD.Factor, C::AbstractArray)

Update an `LDLt` or `LLt` Factorization `F` of `A` to a factorization of `A + C*C'`.

`LLt` factorizations are converted to `LDLt`.

See also `lowrankupdate`, `lowrankdowndate`, `lowrankdowndate!`.

source
`LinearAlgebra.lowrankdowndate!` — Function

    lowrankdowndate!(C::Cholesky, v::AbstractVector) -> CC::Cholesky

Downdate a Cholesky factorization `C` with the vector `v`. If `A = C.U'C.U` then `CC = cholesky(C.U'C.U - v*v')` but the computation of `CC` only uses `O(n^2)` operations. The input factorization `C` is updated in place such that on exit `C == CC`. The vector `v` is destroyed during the computation.

source

    lowrankdowndate!(F::CHOLMOD.Factor, C::AbstractArray)

Update an `LDLt` or `LLt` Factorization `F` of `A` to a factorization of `A - C*C'`.

`LLt` factorizations are converted to `LDLt`.

See also `lowrankdowndate`, `lowrankupdate`, `lowrankupdate!`.

source
`LinearAlgebra.LDLt` — Type

    LDLt <: Factorization

Matrix factorization type of the `LDLt` factorization of a real `SymTridiagonal` matrix `S` such that `S = L*Diagonal(d)*L'`, where `L` is a `UnitLowerTriangular` matrix and `d` is a vector. The main use of an `LDLt` factorization `F = ldlt(S)` is to solve the linear system of equations `Sx = b` with `F\b`. This is the return type of `ldlt`, the corresponding matrix factorization function.

The individual components of the factorization `F::LDLt` can be accessed via `getproperty`:

Component | Description
--- | ---
`F.L` | `L` (unit lower triangular) part of `LDLt`
`F.D` | `D` (diagonal) part of `LDLt`
`F.Lt` | `Lt` (unit upper triangular) part of `LDLt`
`F.d` | diagonal values of `D` as a `Vector`

Examples

    julia> S = SymTridiagonal([3., 4., 5.], [1., 2.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     3.0  1.0   ⋅
     1.0  4.0  2.0
      ⋅   2.0  5.0

    julia> F = ldlt(S)
    LDLt{Float64, SymTridiagonal{Float64, Vector{Float64}}}
    L factor:
    3×3 UnitLowerTriangular{Float64, SymTridiagonal{Float64, Vector{Float64}}}:
     1.0        ⋅         ⋅
     0.333333  1.0        ⋅
     0.0       0.545455  1.0
    D factor:
    3×3 Diagonal{Float64, Vector{Float64}}:
     3.0   ⋅        ⋅
      ⋅   3.66667   ⋅
      ⋅    ⋅       3.90909

source
`LinearAlgebra.ldlt` — Function

    ldlt(S::SymTridiagonal) -> LDLt

Compute an `LDLt` (i.e., $LDL^T$) factorization of the real symmetric tridiagonal matrix `S` such that `S = L*Diagonal(d)*L'` where `L` is a unit lower triangular matrix and `d` is a vector. The main use of an `LDLt` factorization `F = ldlt(S)` is to solve the linear system of equations `Sx = b` with `F\b`.

See also `bunchkaufman` for a similar, but pivoted, factorization of arbitrary symmetric or Hermitian matrices.

Examples

    julia> S = SymTridiagonal([3., 4., 5.], [1., 2.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     3.0  1.0   ⋅
     1.0  4.0  2.0
      ⋅   2.0  5.0

    julia> ldltS = ldlt(S);

    julia> b = [6., 7., 8.];

    julia> ldltS \ b
    3-element Vector{Float64}:
     1.7906976744186047
     0.627906976744186
     1.3488372093023255

    julia> S \ b
    3-element Vector{Float64}:
     1.7906976744186047
     0.627906976744186
     1.3488372093023255

source

    ldlt(A::SparseMatrixCSC; shift = 0.0, check = true, perm=nothing) -> CHOLMOD.Factor

Compute the $LDL'$ factorization of a sparse matrix `A`. `A` must be a `SparseMatrixCSC` or a `Symmetric`/`Hermitian` view of a `SparseMatrixCSC`. Note that even if `A` doesn't have the type tag, it must still be symmetric or Hermitian. A fill-reducing permutation is used. `F = ldlt(A)` is most frequently used to solve systems of equations `A*x = b` with `F\b`. The returned factorization object `F` also supports the methods `diag`, `det`, `logdet`, and `inv`. You can extract individual factors from `F` using `F.L`. However, since pivoting is on by default, the factorization is internally represented as `A == P'*L*D*L'*P` with a permutation matrix `P`; using just `L` without accounting for `P` will give incorrect answers. To include the effects of permutation, it is typically preferable to extract "combined" factors like `PtL = F.PtL` (the equivalent of `P'*L`) and `LtP = F.UP` (the equivalent of `L'*P`). The complete list of supported factors is `:L, :PtL, :D, :UP, :U, :LD, :DU, :PtLD, :DUP`.

Unlike the related Cholesky factorization, the $LDL'$ factorization does not require `A` to be positive definite. However, it still requires all leading principal minors to be well-conditioned and will fail if this is not satisfied.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

Setting the optional `shift` keyword argument computes the factorization of `A+shift*I` instead of `A`. If the `perm` argument is provided, it should be a permutation of `1:size(A,1)` giving the ordering to use (instead of CHOLMOD's default AMD ordering).

See also `cholesky` for a factorization that can be significantly faster than `ldlt`, but requires `A` to be positive definite.

Note

This method uses the CHOLMOD[ACM887][DavisHager2009] library from SuiteSparse. CHOLMOD only supports real or complex types in single or double precision. Input matrices not of those element types will be converted to these types as appropriate.

Many other functions from CHOLMOD are wrapped but not exported from the `Base.SparseArrays.CHOLMOD` module.

source
`LinearAlgebra.ldlt!` — Function

    ldlt!(S::SymTridiagonal) -> LDLt

Same as `ldlt`, but saves space by overwriting the input `S`, instead of creating a copy.

Examples

    julia> S = SymTridiagonal([3., 4., 5.], [1., 2.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     3.0  1.0   ⋅
     1.0  4.0  2.0
      ⋅   2.0  5.0

    julia> ldltS = ldlt!(S);

    julia> ldltS === S
    false

    julia> S
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     3.0       0.333333   ⋅
     0.333333  3.66667   0.545455
      ⋅        0.545455  3.90909

source

    ldlt!(F::CHOLMOD.Factor, A::SparseMatrixCSC; shift = 0.0, check = true) -> CHOLMOD.Factor

Compute the $LDL'$ factorization of `A`, reusing the symbolic factorization `F`. `A` must be a `SparseMatrixCSC` or a `Symmetric`/`Hermitian` view of a `SparseMatrixCSC`. Note that even if `A` doesn't have the type tag, it must still be symmetric or Hermitian.

See also `ldlt`.

Note

This method uses the CHOLMOD library from SuiteSparse, which only supports real or complex types in single or double precision. Input matrices not of those element types will be converted to these types as appropriate.

source
`LinearAlgebra.QR` — Type

    QR <: Factorization

A QR matrix factorization stored in a packed format, typically obtained from `qr`. If $A$ is an `m`×`n` matrix, then

\[A = Q R\]

where $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. The matrix $Q$ is stored as a sequence of Householder reflectors $v_i$ and coefficients $\tau_i$ where:

\[Q = \prod_{i=1}^{\min(m,n)} (I - \tau_i v_i v_i^T).\]

Iterating the decomposition produces the components `Q` and `R`.

The object has two fields:

- `factors` is an `m`×`n` matrix.

  - The upper triangular part contains the elements of $R$, that is `R = triu(F.factors)` for a `QR` object `F`.

  - The subdiagonal part contains the reflectors $v_i$ stored in a packed format where $v_i$ is the $i$th column of the matrix `V = I + tril(F.factors, -1)`.

- `τ` is a vector of length `min(m,n)` containing the coefficients $\tau_i$.

source
`LinearAlgebra.QRCompactWY` — Type

    QRCompactWY <: Factorization

A QR matrix factorization stored in a compact blocked format, typically obtained from `qr`. If $A$ is an `m`×`n` matrix, then

\[A = Q R\]

where $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. It is similar to the `QR` format except that the orthogonal/unitary matrix $Q$ is stored in Compact WY format [Schreiber1989]. For the block size $n_b$, it is stored as a `m`×`n` lower trapezoidal matrix $V$ and a matrix $T = (T_1 \; T_2 \; ... \; T_{b-1} \; T_b')$ composed of $b = \lceil \min(m,n) / n_b \rceil$ upper triangular matrices $T_j$ of size $n_b$×$n_b$ ($j = 1, ..., b-1$) and an upper trapezoidal $n_b$×$\min(m,n) - (b-1) n_b$ matrix $T_b'$ ($j=b$) whose upper square part denoted with $T_b$ satisfying

\[Q = \prod_{i=1}^{\min(m,n)} (I - \tau_i v_i v_i^T) = \prod_{j=1}^{b} (I - V_j T_j V_j^T)\]

such that $v_i$ is the $i$th column of $V$, $\tau_i$ is the $i$th element of `[diag(T_1); diag(T_2); …; diag(T_b)]`, and $(V_1 \; V_2 \; ... \; V_b)$ is the left `m`×`min(m, n)` block of $V$. When constructed using `qr`, the block size is given by $n_b = \min(m, n, 36)$.

Iterating the decomposition produces the components `Q` and `R`.

The object has two fields:

- `factors`, as in the `QR` type, is an `m`×`n` matrix.

  - The upper triangular part contains the elements of $R$, that is `R = triu(F.factors)` for a `QR` object `F`.

  - The subdiagonal part contains the reflectors $v_i$ stored in a packed format such that `V = I + tril(F.factors, -1)`.

- `T` is a $n_b$-by-$\min(m,n)$ matrix as described above. The subdiagonal elements for each triangular matrix $T_j$ are ignored.

Note

This format should not to be confused with the older WY representation [Bischof1987].

source
`LinearAlgebra.QRPivoted` — Type

    QRPivoted <: Factorization

A QR matrix factorization with column pivoting in a packed format, typically obtained from `qr`. If $A$ is an `m`×`n` matrix, then

\[A P = Q R\]

where $P$ is a permutation matrix, $Q$ is an orthogonal/unitary matrix and $R$ is upper triangular. The matrix $Q$ is stored as a sequence of Householder reflectors:

\[Q = \prod_{i=1}^{\min(m,n)} (I - \tau_i v_i v_i^T).\]

Iterating the decomposition produces the components `Q`, `R`, and `p`.

The object has three fields:

- `factors` is an `m`×`n` matrix.

  - The upper triangular part contains the elements of $R$, that is `R = triu(F.factors)` for a `QR` object `F`.

  - The subdiagonal part contains the reflectors $v_i$ stored in a packed format where $v_i$ is the $i$th column of the matrix `V = I + tril(F.factors, -1)`.

- `τ` is a vector of length `min(m,n)` containing the coefficients $au_i$.

- `jpvt` is an integer vector of length `n` corresponding to the permutation $P$.

source
`LinearAlgebra.qr` — Function

    qr(A::SparseMatrixCSC; tol=_default_tol(A), ordering=ORDERING_DEFAULT) -> QRSparse

Compute the `QR` factorization of a sparse matrix `A`. Fill-reducing row and column permutations are used such that `F.R = F.Q'*A[F.prow,F.pcol]`. The main application of this type is to solve least squares or underdetermined problems with `\`. The function calls the C library SPQR[ACM933].

Note

`qr(A::SparseMatrixCSC)` uses the SPQR library that is part of SuiteSparse. As this library only supports sparse matrices with `Float64` or `ComplexF64` elements, as of Julia v1.4 `qr` converts `A` into a copy that is of type `SparseMatrixCSC{Float64}` or `SparseMatrixCSC{ComplexF64}` as appropriate.

Examples

    julia> A = sparse([1,2,3,4], [1,1,2,2], [1.0,1.0,1.0,1.0])
    4×2 SparseMatrixCSC{Float64, Int64} with 4 stored entries:
     1.0   ⋅
     1.0   ⋅
      ⋅   1.0
      ⋅   1.0

    julia> qr(A)
    SparseArrays.SPQR.QRSparse{Float64, Int64}
    Q factor:
    4×4 SparseArrays.SPQR.QRSparseQ{Float64, Int64}
    R factor:
    2×2 SparseMatrixCSC{Float64, Int64} with 2 stored entries:
     -1.41421    ⋅
       ⋅       -1.41421
    Row permutation:
    4-element Vector{Int64}:
     1
     3
     4
     2
    Column permutation:
    2-element Vector{Int64}:
     1
     2

source

    qr(A, pivot = NoPivot(); blocksize) -> F

Compute the QR factorization of the matrix `A`: an orthogonal (or unitary if `A` is complex-valued) matrix `Q`, and an upper triangular matrix `R` such that

\[A = Q R\]

The returned object `F` stores the factorization in a packed format:

- if `pivot == ColumnNorm()` then `F` is a `QRPivoted` object,

- otherwise if the element type of `A` is a BLAS type (`Float32`, `Float64`, `ComplexF32` or `ComplexF64`), then `F` is a `QRCompactWY` object,

- otherwise `F` is a `QR` object.

The individual components of the decomposition `F` can be retrieved via property accessors:

- `F.Q`: the orthogonal/unitary matrix `Q`
- `F.R`: the upper triangular matrix `R`
- `F.p`: the permutation vector of the pivot (`QRPivoted` only)
- `F.P`: the permutation matrix of the pivot (`QRPivoted` only)

Note

Each reference to the upper triangular factor via `F.R` allocates a new array. It is therefore advisable to cache that array, say, by `R = F.R` and continue working with `R`.

Iterating the decomposition produces the components `Q`, `R`, and if extant `p`.

The following functions are available for the `QR` objects: `inv`, `size`, and `\`. When `A` is rectangular, `\` will return a least squares solution and if the solution is not unique, the one with smallest norm is returned. When `A` is not full rank, factorization with (column) pivoting is required to obtain a minimum norm solution.

Multiplication with respect to either full/square or non-full/square `Q` is allowed, i.e. both `F.Q*F.R` and `F.Q*A` are supported. A `Q` matrix can be converted into a regular matrix with `Matrix`. This operation returns the "thin" Q factor, i.e., if `A` is `m`×`n` with `m>=n`, then `Matrix(F.Q)` yields an `m`×`n` matrix with orthonormal columns. To retrieve the "full" Q factor, an `m`×`m` orthogonal matrix, use `F.Q*I` or `collect(F.Q)`. If `m<=n`, then `Matrix(F.Q)` yields an `m`×`m` orthogonal matrix.

The block size for QR decomposition can be specified by keyword argument `blocksize :: Integer` when `pivot == NoPivot()` and `A isa StridedMatrix{<:BlasFloat}`. It is ignored when `blocksize > minimum(size(A))`. See `QRCompactWY`.

Julia 1.4

The `blocksize` keyword argument requires Julia 1.4 or later.

Examples

    julia> A = [3.0 -6.0; 4.0 -8.0; 0.0 1.0]
    3×2 Matrix{Float64}:
     3.0  -6.0
     4.0  -8.0
     0.0   1.0

    julia> F = qr(A)
    LinearAlgebra.QRCompactWY{Float64, Matrix{Float64}, Matrix{Float64}}
    Q factor: 3×3 LinearAlgebra.QRCompactWYQ{Float64, Matrix{Float64}, Matrix{Float64}}
    R factor:
    2×2 Matrix{Float64}:
     -5.0  10.0
      0.0  -1.0

    julia> F.Q * F.R == A
    true

Note

`qr` returns multiple types because LAPACK uses several representations that minimize the memory storage requirements of products of Householder elementary reflectors, so that the `Q` and `R` matrices can be stored compactly rather than two separate dense matrices.

source
`LinearAlgebra.qr!` — Function

    qr!(A, pivot = NoPivot(); blocksize)

`qr!` is the same as `qr` when `A` is a subtype of `AbstractMatrix`, but saves space by overwriting the input `A`, instead of creating a copy. An `InexactError` exception is thrown if the factorization produces a number not representable by the element type of `A`, e.g. for integer types.

Julia 1.4

The `blocksize` keyword argument requires Julia 1.4 or later.

Examples

    julia> a = [1. 2.; 3. 4.]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> qr!(a)
    LinearAlgebra.QRCompactWY{Float64, Matrix{Float64}, Matrix{Float64}}
    Q factor: 2×2 LinearAlgebra.QRCompactWYQ{Float64, Matrix{Float64}, Matrix{Float64}}
    R factor:
    2×2 Matrix{Float64}:
     -3.16228  -4.42719
      0.0      -0.632456

    julia> a = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> qr!(a)
    ERROR: InexactError: Int64(3.1622776601683795)
    Stacktrace:
    [...]

source
`LinearAlgebra.LQ` — Type

    LQ <: Factorization

Matrix factorization type of the `LQ` factorization of a matrix `A`. The `LQ` decomposition is the `QR` decomposition of `transpose(A)`. This is the return type of `lq`, the corresponding matrix factorization function.

If `S::LQ` is the factorization object, the lower triangular component can be obtained via `S.L`, and the orthogonal/unitary component via `S.Q`, such that `A ≈ S.L*S.Q`.

Iterating the decomposition produces the components `S.L` and `S.Q`.

Examples

    julia> A = [5. 7.; -2. -4.]
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> S = lq(A)
    LQ{Float64, Matrix{Float64}, Vector{Float64}}
    L factor:
    2×2 Matrix{Float64}:
     -8.60233   0.0
      4.41741  -0.697486
    Q factor: 2×2 LinearAlgebra.LQPackedQ{Float64, Matrix{Float64}, Vector{Float64}}

    julia> S.L * S.Q
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> l, q = S; # destructuring via iteration

    julia> l == S.L &&  q == S.Q
    true

source
`LinearAlgebra.lq` — Function

    lq(A) -> S::LQ

Compute the LQ decomposition of `A`. The decomposition's lower triangular component can be obtained from the `LQ` object `S` via `S.L`, and the orthogonal/unitary component via `S.Q`, such that `A ≈ S.L*S.Q`.

Iterating the decomposition produces the components `S.L` and `S.Q`.

The LQ decomposition is the QR decomposition of `transpose(A)`, and it is useful in order to compute the minimum-norm solution `lq(A) \ b` to an underdetermined system of equations (`A` has more columns than rows, but has full row rank).

Examples

    julia> A = [5. 7.; -2. -4.]
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> S = lq(A)
    LQ{Float64, Matrix{Float64}, Vector{Float64}}
    L factor:
    2×2 Matrix{Float64}:
     -8.60233   0.0
      4.41741  -0.697486
    Q factor: 2×2 LinearAlgebra.LQPackedQ{Float64, Matrix{Float64}, Vector{Float64}}

    julia> S.L * S.Q
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> l, q = S; # destructuring via iteration

    julia> l == S.L &&  q == S.Q
    true

source
`LinearAlgebra.lq!` — Function

    lq!(A) -> LQ

Compute the `LQ` factorization of `A`, using the input matrix as a workspace. See also `lq`.

source
`LinearAlgebra.BunchKaufman` — Type

    BunchKaufman <: Factorization

Matrix factorization type of the Bunch-Kaufman factorization of a symmetric or Hermitian matrix `A` as `P'UDU'P` or `P'LDL'P`, depending on whether the upper (the default) or the lower triangle is stored in `A`. If `A` is complex symmetric then `U'` and `L'` denote the unconjugated transposes, i.e. `transpose(U)` and `transpose(L)`, respectively. This is the return type of `bunchkaufman`, the corresponding matrix factorization function.

If `S::BunchKaufman` is the factorization object, the components can be obtained via `S.D`, `S.U` or `S.L` as appropriate given `S.uplo`, and `S.p`.

Iterating the decomposition produces the components `S.D`, `S.U` or `S.L` as appropriate given `S.uplo`, and `S.p`.

Examples

    julia> A = Float64.([1 2; 2 3])
    2×2 Matrix{Float64}:
     1.0  2.0
     2.0  3.0

    julia> S = bunchkaufman(A) # A gets wrapped internally by Symmetric(A)
    BunchKaufman{Float64, Matrix{Float64}, Vector{Int64}}
    D factor:
    2×2 Tridiagonal{Float64, Vector{Float64}}:
     -0.333333  0.0
      0.0       3.0
    U factor:
    2×2 UnitUpperTriangular{Float64, Matrix{Float64}}:
     1.0  0.666667
      ⋅   1.0
    permutation:
    2-element Vector{Int64}:
     1
     2

    julia> d, u, p = S; # destructuring via iteration

    julia> d == S.D && u == S.U && p == S.p
    true

    julia> S = bunchkaufman(Symmetric(A, :L))
    BunchKaufman{Float64, Matrix{Float64}, Vector{Int64}}
    D factor:
    2×2 Tridiagonal{Float64, Vector{Float64}}:
     3.0   0.0
     0.0  -0.333333
    L factor:
    2×2 UnitLowerTriangular{Float64, Matrix{Float64}}:
     1.0        ⋅
     0.666667  1.0
    permutation:
    2-element Vector{Int64}:
     2
     1

source
`LinearAlgebra.bunchkaufman` — Function

    bunchkaufman(A, rook::Bool=false; check = true) -> S::BunchKaufman

Compute the Bunch-Kaufman [Bunch1977] factorization of a symmetric or Hermitian matrix `A` as `P'*U*D*U'*P` or `P'*L*D*L'*P`, depending on which triangle is stored in `A`, and return a `BunchKaufman` object. Note that if `A` is complex symmetric then `U'` and `L'` denote the unconjugated transposes, i.e. `transpose(U)` and `transpose(L)`.

Iterating the decomposition produces the components `S.D`, `S.U` or `S.L` as appropriate given `S.uplo`, and `S.p`.

If `rook` is `true`, rook pivoting is used. If `rook` is false, rook pivoting is not used.

When `check = true`, an error is thrown if the decomposition fails. When `check = false`, responsibility for checking the decomposition's validity (via `issuccess`) lies with the user.

The following functions are available for `BunchKaufman` objects: `size`, `\`, `inv`, `issymmetric`, `ishermitian`, `getindex`.

Examples

    julia> A = Float64.([1 2; 2 3])
    2×2 Matrix{Float64}:
     1.0  2.0
     2.0  3.0

    julia> S = bunchkaufman(A) # A gets wrapped internally by Symmetric(A)
    BunchKaufman{Float64, Matrix{Float64}, Vector{Int64}}
    D factor:
    2×2 Tridiagonal{Float64, Vector{Float64}}:
     -0.333333  0.0
      0.0       3.0
    U factor:
    2×2 UnitUpperTriangular{Float64, Matrix{Float64}}:
     1.0  0.666667
      ⋅   1.0
    permutation:
    2-element Vector{Int64}:
     1
     2

    julia> d, u, p = S; # destructuring via iteration

    julia> d == S.D && u == S.U && p == S.p
    true

    julia> S.U*S.D*S.U' - S.P*A*S.P'
    2×2 Matrix{Float64}:
     0.0  0.0
     0.0  0.0

    julia> S = bunchkaufman(Symmetric(A, :L))
    BunchKaufman{Float64, Matrix{Float64}, Vector{Int64}}
    D factor:
    2×2 Tridiagonal{Float64, Vector{Float64}}:
     3.0   0.0
     0.0  -0.333333
    L factor:
    2×2 UnitLowerTriangular{Float64, Matrix{Float64}}:
     1.0        ⋅
     0.666667  1.0
    permutation:
    2-element Vector{Int64}:
     2
     1

    julia> S.L*S.D*S.L' - A[S.p, S.p]
    2×2 Matrix{Float64}:
     0.0  0.0
     0.0  0.0

source
`LinearAlgebra.bunchkaufman!` — Function

    bunchkaufman!(A, rook::Bool=false; check = true) -> BunchKaufman

`bunchkaufman!` is the same as `bunchkaufman`, but saves space by overwriting the input `A`, instead of creating a copy.

source
`LinearAlgebra.Eigen` — Type

    Eigen <: Factorization

Matrix factorization type of the eigenvalue/spectral decomposition of a square matrix `A`. This is the return type of `eigen`, the corresponding matrix factorization function.

If `F::Eigen` is the factorization object, the eigenvalues can be obtained via `F.values` and the eigenvectors as the columns of the matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

Examples

    julia> F = eigen([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0])
    Eigen{Float64, Float64, Matrix{Float64}, Vector{Float64}}
    values:
    3-element Vector{Float64}:
      1.0
      3.0
     18.0
    vectors:
    3×3 Matrix{Float64}:
     1.0  0.0  0.0
     0.0  1.0  0.0
     0.0  0.0  1.0

    julia> F.values
    3-element Vector{Float64}:
      1.0
      3.0
     18.0

    julia> F.vectors
    3×3 Matrix{Float64}:
     1.0  0.0  0.0
     0.0  1.0  0.0
     0.0  0.0  1.0

    julia> vals, vecs = F; # destructuring via iteration

    julia> vals == F.values && vecs == F.vectors
    true

source
`LinearAlgebra.GeneralizedEigen` — Type

    GeneralizedEigen <: Factorization

Matrix factorization type of the generalized eigenvalue/spectral decomposition of `A` and `B`. This is the return type of `eigen`, the corresponding matrix factorization function, when called with two matrix arguments.

If `F::GeneralizedEigen` is the factorization object, the eigenvalues can be obtained via `F.values` and the eigenvectors as the columns of the matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

Examples

    julia> A = [1 0; 0 -1]
    2×2 Matrix{Int64}:
     1   0
     0  -1

    julia> B = [0 1; 1 0]
    2×2 Matrix{Int64}:
     0  1
     1  0

    julia> F = eigen(A, B)
    GeneralizedEigen{ComplexF64, ComplexF64, Matrix{ComplexF64}, Vector{ComplexF64}}
    values:
    2-element Vector{ComplexF64}:
     0.0 - 1.0im
     0.0 + 1.0im
    vectors:
    2×2 Matrix{ComplexF64}:
      0.0+1.0im   0.0-1.0im
     -1.0+0.0im  -1.0-0.0im

    julia> F.values
    2-element Vector{ComplexF64}:
     0.0 - 1.0im
     0.0 + 1.0im

    julia> F.vectors
    2×2 Matrix{ComplexF64}:
      0.0+1.0im   0.0-1.0im
     -1.0+0.0im  -1.0-0.0im

    julia> vals, vecs = F; # destructuring via iteration

    julia> vals == F.values && vecs == F.vectors
    true

source
`LinearAlgebra.eigvals` — Function

    eigvals(A; permute::Bool=true, scale::Bool=true, sortby) -> values

Return the eigenvalues of `A`.

For general non-symmetric matrices it is possible to specify how the matrix is balanced before the eigenvalue calculation. The `permute`, `scale`, and `sortby` keywords are the same as for `eigen`.

Examples

    julia> diag_matrix = [1 0; 0 4]
    2×2 Matrix{Int64}:
     1  0
     0  4

    julia> eigvals(diag_matrix)
    2-element Vector{Float64}:
     1.0
     4.0

source

For a scalar input, `eigvals` will return a scalar.

Examples

    julia> eigvals(-2)
    -2

source

    eigvals(A, B) -> values

Compute the generalized eigenvalues of `A` and `B`.

Examples

    julia> A = [1 0; 0 -1]
    2×2 Matrix{Int64}:
     1   0
     0  -1

    julia> B = [0 1; 1 0]
    2×2 Matrix{Int64}:
     0  1
     1  0

    julia> eigvals(A,B)
    2-element Vector{ComplexF64}:
     0.0 - 1.0im
     0.0 + 1.0im

source

    eigvals(A::Union{Hermitian, Symmetric}; alg::Algorithm = default_eigen_alg(A))) -> values

Return the eigenvalues of `A`.

`alg` specifies which algorithm and LAPACK method to use for eigenvalue decomposition:

- `alg = DivideAndConquer()`: Calls `LAPACK.syevd!`.
- `alg = QRIteration()`: Calls `LAPACK.syev!`.
- `alg = RobustRepresentations()` (default): Multiple relatively robust representations method, Calls `LAPACK.syevr!`.

See James W. Demmel et al, SIAM J. Sci. Comput. 30, 3, 1508 (2008) for a comparison of the accuracy and performance of different methods.

The default `alg` used may change in the future.

Julia 1.12

The `alg` keyword argument requires Julia 1.12 or later.

source

    eigvals(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) -> values

Return the eigenvalues of `A`. It is possible to calculate only a subset of the eigenvalues by specifying a `UnitRange` `irange` covering indices of the sorted eigenvalues, e.g. the 2nd to 8th eigenvalues.

Examples

    julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     1.0  2.0   ⋅
     2.0  2.0  3.0
      ⋅   3.0  1.0

    julia> eigvals(A, 2:2)
    1-element Vector{Float64}:
     0.9999999999999996

    julia> eigvals(A)
    3-element Vector{Float64}:
     -2.1400549446402604
      1.0000000000000002
      5.140054944640259

source

    eigvals(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) -> values

Return the eigenvalues of `A`. It is possible to calculate only a subset of the eigenvalues by specifying a pair `vl` and `vu` for the lower and upper boundaries of the eigenvalues.

Examples

    julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     1.0  2.0   ⋅
     2.0  2.0  3.0
      ⋅   3.0  1.0

    julia> eigvals(A, -1, 2)
    1-element Vector{Float64}:
     1.0000000000000009

    julia> eigvals(A)
    3-element Vector{Float64}:
     -2.1400549446402604
      1.0000000000000002
      5.140054944640259

source
`LinearAlgebra.eigvals!` — Function

    eigvals!(A; permute::Bool=true, scale::Bool=true, sortby) -> values

Same as `eigvals`, but saves space by overwriting the input `A`, instead of creating a copy. The `permute`, `scale`, and `sortby` keywords are the same as for `eigen`.

Note

The input matrix `A` will not contain its eigenvalues after `eigvals!` is called on it - `A` is used as a workspace.

Examples

    julia> A = [1. 2.; 3. 4.]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> eigvals!(A)
    2-element Vector{Float64}:
     -0.3722813232690143
      5.372281323269014

    julia> A
    2×2 Matrix{Float64}:
     -0.372281  -1.0
      0.0        5.37228

source

    eigvals!(A, B; sortby) -> values

Same as `eigvals`, but saves space by overwriting the input `A` (and `B`), instead of creating copies.

Note

The input matrices `A` and `B` will not contain their eigenvalues after `eigvals!` is called. They are used as workspaces.

Examples

    julia> A = [1. 0.; 0. -1.]
    2×2 Matrix{Float64}:
     1.0   0.0
     0.0  -1.0

    julia> B = [0. 1.; 1. 0.]
    2×2 Matrix{Float64}:
     0.0  1.0
     1.0  0.0

    julia> eigvals!(A, B)
    2-element Vector{ComplexF64}:
     0.0 - 1.0im
     0.0 + 1.0im

    julia> A
    2×2 Matrix{Float64}:
     -0.0  -1.0
      1.0  -0.0

    julia> B
    2×2 Matrix{Float64}:
     1.0  0.0
     0.0  1.0

source

    eigvals!(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) -> values

Same as `eigvals`, but saves space by overwriting the input `A`, instead of creating a copy. `irange` is a range of eigenvalue indices to search for - for instance, the 2nd to 8th eigenvalues.

source

    eigvals!(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) -> values

Same as `eigvals`, but saves space by overwriting the input `A`, instead of creating a copy. `vl` is the lower bound of the interval to search for eigenvalues, and `vu` is the upper bound.

source
`LinearAlgebra.eigmax` — Function

    eigmax(A; permute::Bool=true, scale::Bool=true)

Return the largest eigenvalue of `A`. The option `permute=true` permutes the matrix to become closer to upper triangular, and `scale=true` scales the matrix by its diagonal elements to make rows and columns more equal in norm. Note that if the eigenvalues of `A` are complex, this method will fail, since complex numbers cannot be sorted.

Examples

    julia> A = [0 im; -im 0]
    2×2 Matrix{Complex{Int64}}:
     0+0im  0+1im
     0-1im  0+0im

    julia> eigmax(A)
    1.0

    julia> A = [0 im; -1 0]
    2×2 Matrix{Complex{Int64}}:
      0+0im  0+1im
     -1+0im  0+0im

    julia> eigmax(A)
    ERROR: DomainError with Complex{Int64}[0+0im 0+1im; -1+0im 0+0im]:
    `A` cannot have complex eigenvalues.
    Stacktrace:
    [...]

source
`LinearAlgebra.eigmin` — Function

    eigmin(A; permute::Bool=true, scale::Bool=true)

Return the smallest eigenvalue of `A`. The option `permute=true` permutes the matrix to become closer to upper triangular, and `scale=true` scales the matrix by its diagonal elements to make rows and columns more equal in norm. Note that if the eigenvalues of `A` are complex, this method will fail, since complex numbers cannot be sorted.

Examples

    julia> A = [0 im; -im 0]
    2×2 Matrix{Complex{Int64}}:
     0+0im  0+1im
     0-1im  0+0im

    julia> eigmin(A)
    -1.0

    julia> A = [0 im; -1 0]
    2×2 Matrix{Complex{Int64}}:
      0+0im  0+1im
     -1+0im  0+0im

    julia> eigmin(A)
    ERROR: DomainError with Complex{Int64}[0+0im 0+1im; -1+0im 0+0im]:
    `A` cannot have complex eigenvalues.
    Stacktrace:
    [...]

source
`LinearAlgebra.eigvecs` — Function

    eigvecs(A::SymTridiagonal[, eigvals]) -> Matrix

Return a matrix `M` whose columns are the eigenvectors of `A`. (The `k`th eigenvector can be obtained from the slice `M[:, k]`.)

If the optional vector of eigenvalues `eigvals` is specified, `eigvecs` returns the specific corresponding eigenvectors.

Examples

    julia> A = SymTridiagonal([1.; 2.; 1.], [2.; 3.])
    3×3 SymTridiagonal{Float64, Vector{Float64}}:
     1.0  2.0   ⋅
     2.0  2.0  3.0
      ⋅   3.0  1.0

    julia> eigvals(A)
    3-element Vector{Float64}:
     -2.1400549446402604
      1.0000000000000002
      5.140054944640259

    julia> eigvecs(A)
    3×3 Matrix{Float64}:
      0.418304  -0.83205      0.364299
     -0.656749  -7.39009e-16  0.754109
      0.627457   0.5547       0.546448

    julia> eigvecs(A, [1.])
    3×1 Matrix{Float64}:
      0.8320502943378438
      4.263514128092366e-17
     -0.5547001962252291

source

    eigvecs(A; permute::Bool=true, scale::Bool=true, `sortby`) -> Matrix

Return a matrix `M` whose columns are the eigenvectors of `A`. (The `k`th eigenvector can be obtained from the slice `M[:, k]`.) The `permute`, `scale`, and `sortby` keywords are the same as for `eigen`.

Examples

    julia> eigvecs([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0])
    3×3 Matrix{Float64}:
     1.0  0.0  0.0
     0.0  1.0  0.0
     0.0  0.0  1.0

source

    eigvecs(A, B) -> Matrix

Return a matrix `M` whose columns are the generalized eigenvectors of `A` and `B`. (The `k`th eigenvector can be obtained from the slice `M[:, k]`.)

Examples

    julia> A = [1 0; 0 -1]
    2×2 Matrix{Int64}:
     1   0
     0  -1

    julia> B = [0 1; 1 0]
    2×2 Matrix{Int64}:
     0  1
     1  0

    julia> eigvecs(A, B)
    2×2 Matrix{ComplexF64}:
      0.0+1.0im   0.0-1.0im
     -1.0+0.0im  -1.0-0.0im

source
`LinearAlgebra.eigen` — Function

    eigen(A; permute::Bool=true, scale::Bool=true, sortby) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an `Eigen` factorization object `F` which contains the eigenvalues in `F.values` and the normalized eigenvectors in the columns of the matrix `F.vectors`. This corresponds to solving an eigenvalue problem of the form `Ax = λx`, where `A` is a matrix, `x` is an eigenvector, and `λ` is an eigenvalue. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

The following functions are available for `Eigen` objects: `inv`, `det`, and `isposdef`.

For general nonsymmetric matrices it is possible to specify how the matrix is balanced before the eigenvector calculation. The option `permute=true` permutes the matrix to become closer to upper triangular, and `scale=true` scales the matrix by its diagonal elements to make rows and columns more equal in norm. The default is `true` for both options.

By default, the eigenvalues and vectors are sorted lexicographically by `(real(λ),imag(λ))`. A different comparison function `by(λ)` can be passed to `sortby`, or you can pass `sortby=nothing` to leave the eigenvalues in an arbitrary order. Some special matrix types (e.g. `Diagonal` or `SymTridiagonal`) may implement their own sorting convention and not accept a `sortby` keyword.

Examples

    julia> F = eigen([1.0 0.0 0.0; 0.0 3.0 0.0; 0.0 0.0 18.0])
    Eigen{Float64, Float64, Matrix{Float64}, Vector{Float64}}
    values:
    3-element Vector{Float64}:
      1.0
      3.0
     18.0
    vectors:
    3×3 Matrix{Float64}:
     1.0  0.0  0.0
     0.0  1.0  0.0
     0.0  0.0  1.0

    julia> F.values
    3-element Vector{Float64}:
      1.0
      3.0
     18.0

    julia> F.vectors
    3×3 Matrix{Float64}:
     1.0  0.0  0.0
     0.0  1.0  0.0
     0.0  0.0  1.0

    julia> vals, vecs = F; # destructuring via iteration

    julia> vals == F.values && vecs == F.vectors
    true

source

    eigen(A, B; sortby) -> GeneralizedEigen

Compute the generalized eigenvalue decomposition of `A` and `B`, returning a `GeneralizedEigen` factorization object `F` which contains the generalized eigenvalues in `F.values` and the generalized eigenvectors in the columns of the matrix `F.vectors`. This corresponds to solving a generalized eigenvalue problem of the form `Ax = λBx`, where `A, B` are matrices, `x` is an eigenvector, and `λ` is an eigenvalue. (The `k`th generalized eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

By default, the eigenvalues and vectors are sorted lexicographically by `(real(λ),imag(λ))`. A different comparison function `by(λ)` can be passed to `sortby`, or you can pass `sortby=nothing` to leave the eigenvalues in an arbitrary order.

Examples

    julia> A = [1 0; 0 -1]
    2×2 Matrix{Int64}:
     1   0
     0  -1

    julia> B = [0 1; 1 0]
    2×2 Matrix{Int64}:
     0  1
     1  0

    julia> F = eigen(A, B);

    julia> F.values
    2-element Vector{ComplexF64}:
     0.0 - 1.0im
     0.0 + 1.0im

    julia> F.vectors
    2×2 Matrix{ComplexF64}:
      0.0+1.0im   0.0-1.0im
     -1.0+0.0im  -1.0-0.0im

    julia> vals, vecs = F; # destructuring via iteration

    julia> vals == F.values && vecs == F.vectors
    true

source

    eigen(A::Union{Hermitian, Symmetric}; alg::LinearAlgebra.Algorithm = LinearAlgebra.default_eigen_alg(A)) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an `Eigen` factorization object `F` which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

`alg` specifies which algorithm and LAPACK method to use for eigenvalue decomposition:

- `alg = DivideAndConquer()`: Calls `LAPACK.syevd!`.
- `alg = QRIteration()`: Calls `LAPACK.syev!`.
- `alg = RobustRepresentations()` (default): Multiple relatively robust representations method, Calls `LAPACK.syevr!`.

See James W. Demmel et al, SIAM J. Sci. Comput. 30, 3, 1508 (2008) for a comparison of the accuracy and performance of different algorithms.

The default `alg` used may change in the future.

Julia 1.12

The `alg` keyword argument requires Julia 1.12 or later.

The following functions are available for `Eigen` objects: `inv`, `det`, and `isposdef`.

source

    eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, irange::UnitRange) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an `Eigen` factorization object `F` which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

The following functions are available for `Eigen` objects: `inv`, `det`, and `isposdef`.

The `UnitRange` `irange` specifies indices of the sorted eigenvalues to search for.

Note

If `irange` is not `1:n`, where `n` is the dimension of `A`, then the returned factorization will be a truncated factorization.

source

    eigen(A::Union{SymTridiagonal, Hermitian, Symmetric}, vl::Real, vu::Real) -> Eigen

Compute the eigenvalue decomposition of `A`, returning an `Eigen` factorization object `F` which contains the eigenvalues in `F.values` and the orthonormal eigenvectors in the columns of the matrix `F.vectors`. (The `k`th eigenvector can be obtained from the slice `F.vectors[:, k]`.)

Iterating the decomposition produces the components `F.values` and `F.vectors`.

The following functions are available for `Eigen` objects: `inv`, `det`, and `isposdef`.

`vl` is the lower bound of the window of eigenvalues to search for, and `vu` is the upper bound.

Note

If [`vl`, `vu`] does not contain all eigenvalues of `A`, then the returned factorization will be a truncated factorization.

source
`LinearAlgebra.eigen!` — Function

    eigen!(A; permute, scale, sortby)
    eigen!(A, B; sortby)

Same as `eigen`, but saves space by overwriting the input `A` (and `B`), instead of creating a copy.

source
`LinearAlgebra.Hessenberg` — Type

    Hessenberg <: Factorization

A `Hessenberg` object represents the Hessenberg factorization `QHQ'` of a square matrix, or a shift `Q(H+μI)Q'` thereof, which is produced by the `hessenberg` function.

source
`LinearAlgebra.hessenberg` — Function

    hessenberg(A) -> Hessenberg

Compute the Hessenberg decomposition of `A` and return a `Hessenberg` object. If `F` is the factorization object, the unitary matrix can be accessed with `F.Q` (of type `LinearAlgebra.HessenbergQ`) and the Hessenberg matrix with `F.H` (of type `UpperHessenberg`), either of which may be converted to a regular matrix with `Matrix(F.H)` or `Matrix(F.Q)`.

If `A` is `Hermitian` or real-`Symmetric`, then the Hessenberg decomposition produces a real-symmetric tridiagonal matrix and `F.H` is of type `SymTridiagonal`.

Note that the shifted factorization `A+μI = Q (H+μI) Q'` can be constructed efficiently by `F + μ*I` using the `UniformScaling` object `I`, which creates a new `Hessenberg` object with shared storage and a modified shift. The shift of a given `F` is obtained by `F.μ`. This is useful because multiple shifted solves `(F + μ*I) \ b` (for different `μ` and/or `b`) can be performed efficiently once `F` is created.

Iterating the decomposition produces the factors `F.Q, F.H, F.μ`.

Examples

    julia> A = [4. 9. 7.; 4. 4. 1.; 4. 3. 2.]
    3×3 Matrix{Float64}:
     4.0  9.0  7.0
     4.0  4.0  1.0
     4.0  3.0  2.0

    julia> F = hessenberg(A)
    Hessenberg{Float64, UpperHessenberg{Float64, Matrix{Float64}}, Matrix{Float64}, Vector{Float64}, Bool}
    Q factor: 3×3 LinearAlgebra.HessenbergQ{Float64, Matrix{Float64}, Vector{Float64}, false}
    H factor:
    3×3 UpperHessenberg{Float64, Matrix{Float64}}:
      4.0      -11.3137       -1.41421
     -5.65685    5.0           2.0
       ⋅        -8.88178e-16   1.0

    julia> F.Q * F.H * F.Q'
    3×3 Matrix{Float64}:
     4.0  9.0  7.0
     4.0  4.0  1.0
     4.0  3.0  2.0

    julia> q, h = F; # destructuring via iteration

    julia> q == F.Q && h == F.H
    true

source
`LinearAlgebra.hessenberg!` — Function

    hessenberg!(A) -> Hessenberg

`hessenberg!` is the same as `hessenberg`, but saves space by overwriting the input `A`, instead of creating a copy.

source
`LinearAlgebra.Schur` — Type

    Schur <: Factorization

Matrix factorization type of the Schur factorization of a matrix `A`. This is the return type of `schur(_)`, the corresponding matrix factorization function.

If `F::Schur` is the factorization object, the (quasi) triangular Schur factor can be obtained via either `F.Schur` or `F.T` and the orthogonal/unitary Schur vectors via `F.vectors` or `F.Z` such that `A = F.vectors * F.Schur * F.vectors'`. The eigenvalues of `A` can be obtained with `F.values`.

Iterating the decomposition produces the components `F.T`, `F.Z`, and `F.values`.

Examples

    julia> A = [5. 7.; -2. -4.]
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> F = schur(A)
    Schur{Float64, Matrix{Float64}, Vector{Float64}}
    T factor:
    2×2 Matrix{Float64}:
     3.0   9.0
     0.0  -2.0
    Z factor:
    2×2 Matrix{Float64}:
      0.961524  0.274721
     -0.274721  0.961524
    eigenvalues:
    2-element Vector{Float64}:
      3.0
     -2.0

    julia> F.vectors * F.Schur * F.vectors'
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> t, z, vals = F; # destructuring via iteration

    julia> t == F.T && z == F.Z && vals == F.values
    true

source
`LinearAlgebra.GeneralizedSchur` — Type

    GeneralizedSchur <: Factorization

Matrix factorization type of the generalized Schur factorization of two matrices `A` and `B`. This is the return type of `schur(_, _)`, the corresponding matrix factorization function.

If `F::GeneralizedSchur` is the factorization object, the (quasi) triangular Schur factors can be obtained via `F.S` and `F.T`, the left unitary/orthogonal Schur vectors via `F.left` or `F.Q`, and the right unitary/orthogonal Schur vectors can be obtained with `F.right` or `F.Z` such that `A=F.left*F.S*F.right'` and `B=F.left*F.T*F.right'`. The generalized eigenvalues of `A` and `B` can be obtained with `F.α./F.β`.

Iterating the decomposition produces the components `F.S`, `F.T`, `F.Q`, `F.Z`, `F.α`, and `F.β`.

source
`LinearAlgebra.schur` — Function

    schur(A) -> F::Schur

Computes the Schur factorization of the matrix `A`. The (quasi) triangular Schur factor can be obtained from the `Schur` object `F` with either `F.Schur` or `F.T` and the orthogonal/unitary Schur vectors can be obtained with `F.vectors` or `F.Z` such that `A = F.vectors * F.Schur * F.vectors'`. The eigenvalues of `A` can be obtained with `F.values`.

For real `A`, the Schur factorization is "quasitriangular", which means that it is upper-triangular except with 2×2 diagonal blocks for any conjugate pair of complex eigenvalues; this allows the factorization to be purely real even when there are complex eigenvalues. To obtain the (complex) purely upper-triangular Schur factorization from a real quasitriangular factorization, you can use `Schur{Complex}(schur(A))`.

Iterating the decomposition produces the components `F.T`, `F.Z`, and `F.values`.

Examples

    julia> A = [5. 7.; -2. -4.]
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> F = schur(A)
    Schur{Float64, Matrix{Float64}, Vector{Float64}}
    T factor:
    2×2 Matrix{Float64}:
     3.0   9.0
     0.0  -2.0
    Z factor:
    2×2 Matrix{Float64}:
      0.961524  0.274721
     -0.274721  0.961524
    eigenvalues:
    2-element Vector{Float64}:
      3.0
     -2.0

    julia> F.vectors * F.Schur * F.vectors'
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> t, z, vals = F; # destructuring via iteration

    julia> t == F.T && z == F.Z && vals == F.values
    true

source

    schur(A, B) -> F::GeneralizedSchur

Computes the Generalized Schur (or QZ) factorization of the matrices `A` and `B`. The (quasi) triangular Schur factors can be obtained from the `Schur` object `F` with `F.S` and `F.T`, the left unitary/orthogonal Schur vectors can be obtained with `F.left` or `F.Q` and the right unitary/orthogonal Schur vectors can be obtained with `F.right` or `F.Z` such that `A=F.left*F.S*F.right'` and `B=F.left*F.T*F.right'`. The generalized eigenvalues of `A` and `B` can be obtained with `F.α./F.β`.

Iterating the decomposition produces the components `F.S`, `F.T`, `F.Q`, `F.Z`, `F.α`, and `F.β`.

source
`LinearAlgebra.schur!` — Function

    schur!(A) -> F::Schur

Same as `schur` but uses the input argument `A` as workspace.

Examples

    julia> A = [5. 7.; -2. -4.]
    2×2 Matrix{Float64}:
      5.0   7.0
     -2.0  -4.0

    julia> F = schur!(A)
    Schur{Float64, Matrix{Float64}, Vector{Float64}}
    T factor:
    2×2 Matrix{Float64}:
     3.0   9.0
     0.0  -2.0
    Z factor:
    2×2 Matrix{Float64}:
      0.961524  0.274721
     -0.274721  0.961524
    eigenvalues:
    2-element Vector{Float64}:
      3.0
     -2.0

    julia> A
    2×2 Matrix{Float64}:
     3.0   9.0
     0.0  -2.0

source

    schur!(A::StridedMatrix, B::StridedMatrix) -> F::GeneralizedSchur

Same as `schur` but uses the input matrices `A` and `B` as workspace.

source
`LinearAlgebra.ordschur` — Function

    ordschur(F::Schur, select::Union{Vector{Bool},BitVector}) -> F::Schur

Reorders the Schur factorization `F` of a matrix `A = Z*T*Z'` according to the logical array `select` returning the reordered factorization `F` object. The selected eigenvalues appear in the leading diagonal of `F.Schur` and the corresponding leading columns of `F.vectors` form an orthogonal/unitary basis of the corresponding right invariant subspace. In the real case, a complex conjugate pair of eigenvalues must be either both included or both excluded via `select`.

source

    ordschur(F::GeneralizedSchur, select::Union{Vector{Bool},BitVector}) -> F::GeneralizedSchur

Reorders the Generalized Schur factorization `F` of a matrix pair `(A, B) = (Q*S*Z', Q*T*Z')` according to the logical array `select` and returns a GeneralizedSchur object `F`. The selected eigenvalues appear in the leading diagonal of both `F.S` and `F.T`, and the left and right orthogonal/unitary Schur vectors are also reordered such that `(A, B) = F.Q*(F.S, F.T)*F.Z'` still holds and the generalized eigenvalues of `A` and `B` can still be obtained with `F.α./F.β`.

source
`LinearAlgebra.ordschur!` — Function

    ordschur!(F::Schur, select::Union{Vector{Bool},BitVector}) -> F::Schur

Same as `ordschur` but overwrites the factorization `F`.

source

    ordschur!(F::GeneralizedSchur, select::Union{Vector{Bool},BitVector}) -> F::GeneralizedSchur

Same as `ordschur` but overwrites the factorization `F`.

source
`LinearAlgebra.SVD` — Type

    SVD <: Factorization

Matrix factorization type of the singular value decomposition (SVD) of a matrix `A`. This is the return type of `svd(_)`, the corresponding matrix factorization function.

If `F::SVD` is the factorization object, `U`, `S`, `V` and `Vt` can be obtained via `F.U`, `F.S`, `F.V` and `F.Vt`, such that `A = U * Diagonal(S) * Vt`. The singular values in `S` are sorted in descending order.

Iterating the decomposition produces the components `U`, `S`, and `V`.

Examples

    julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.]
    4×5 Matrix{Float64}:
     1.0  0.0  0.0  0.0  2.0
     0.0  0.0  3.0  0.0  0.0
     0.0  0.0  0.0  0.0  0.0
     0.0  2.0  0.0  0.0  0.0

    julia> F = svd(A)
    SVD{Float64, Float64, Matrix{Float64}, Vector{Float64}}
    U factor:
    4×4 Matrix{Float64}:
     0.0  1.0   0.0  0.0
     1.0  0.0   0.0  0.0
     0.0  0.0   0.0  1.0
     0.0  0.0  -1.0  0.0
    singular values:
    4-element Vector{Float64}:
     3.0
     2.23606797749979
     2.0
     0.0
    Vt factor:
    4×5 Matrix{Float64}:
     -0.0        0.0  1.0  -0.0  0.0
      0.447214   0.0  0.0   0.0  0.894427
      0.0       -1.0  0.0   0.0  0.0
      0.0        0.0  0.0   1.0  0.0

    julia> F.U * Diagonal(F.S) * F.Vt
    4×5 Matrix{Float64}:
     1.0  0.0  0.0  0.0  2.0
     0.0  0.0  3.0  0.0  0.0
     0.0  0.0  0.0  0.0  0.0
     0.0  2.0  0.0  0.0  0.0

    julia> u, s, v = F; # destructuring via iteration

    julia> u == F.U && s == F.S && v == F.V
    true

source
`LinearAlgebra.GeneralizedSVD` — Type

    GeneralizedSVD <: Factorization

Matrix factorization type of the generalized singular value decomposition (SVD) of two matrices `A` and `B`, such that `A = F.U*F.D1*F.R0*F.Q'` and `B = F.V*F.D2*F.R0*F.Q'`. This is the return type of `svd(_, _)`, the corresponding matrix factorization function.

For an M-by-N matrix `A` and P-by-N matrix `B`,

- `U` is a M-by-M orthogonal matrix,
- `V` is a P-by-P orthogonal matrix,
- `Q` is a N-by-N orthogonal matrix,
- `D1` is a M-by-(K+L) diagonal matrix with 1s in the first K entries,
- `D2` is a P-by-(K+L) matrix whose top right L-by-L block is diagonal,
- `R0` is a (K+L)-by-N matrix whose rightmost (K+L)-by-(K+L) block is nonsingular upper block triangular,

`K+L` is the effective numerical rank of the matrix `[A; B]`.

Iterating the decomposition produces the components `U`, `V`, `Q`, `D1`, `D2`, and `R0`.

The entries of `F.D1` and `F.D2` are related, as explained in the LAPACK documentation for the generalized SVD and the xGGSVD3 routine which is called underneath (in LAPACK 3.6.0 and newer).

Examples

    julia> A = [1. 0.; 0. -1.]
    2×2 Matrix{Float64}:
     1.0   0.0
     0.0  -1.0

    julia> B = [0. 1.; 1. 0.]
    2×2 Matrix{Float64}:
     0.0  1.0
     1.0  0.0

    julia> F = svd(A, B)
    GeneralizedSVD{Float64, Matrix{Float64}, Float64, Vector{Float64}}
    U factor:
    2×2 Matrix{Float64}:
     1.0  0.0
     0.0  1.0
    V factor:
    2×2 Matrix{Float64}:
     -0.0  -1.0
      1.0   0.0
    Q factor:
    2×2 Matrix{Float64}:
     1.0  0.0
     0.0  1.0
    D1 factor:
    2×2 Matrix{Float64}:
     0.707107  0.0
     0.0       0.707107
    D2 factor:
    2×2 Matrix{Float64}:
     0.707107  0.0
     0.0       0.707107
    R0 factor:
    2×2 Matrix{Float64}:
     1.41421   0.0
     0.0      -1.41421

    julia> F.U*F.D1*F.R0*F.Q'
    2×2 Matrix{Float64}:
     1.0   0.0
     0.0  -1.0

    julia> F.V*F.D2*F.R0*F.Q'
    2×2 Matrix{Float64}:
     -0.0  1.0
      1.0  0.0

source
`LinearAlgebra.svd` — Function

    svd(A; full::Bool = false, alg::Algorithm = default_svd_alg(A)) -> SVD

Compute the singular value decomposition (SVD) of `A` and return an `SVD` object.

`U`, `S`, `V` and `Vt` can be obtained from the factorization `F` with `F.U`, `F.S`, `F.V` and `F.Vt`, such that `A = U * Diagonal(S) * Vt`. The algorithm produces `Vt` and hence `Vt` is more efficient to extract than `V`. The singular values in `S` are sorted in descending order.

Iterating the decomposition produces the components `U`, `S`, and `V`.

If `full = false` (default), a "thin" SVD is returned. For an $M \times N$ matrix `A`, in the full factorization `U` is $M \times M$ and `V` is $N \times N$, while in the thin factorization `U` is $M \times K$ and `V` is $N \times K$, where $K = \min(M,N)$ is the number of singular values.

`alg` specifies which algorithm and LAPACK method to use for SVD:

- `alg = LinearAlgebra.DivideAndConquer()` (default): Calls `LAPACK.gesdd!`.
- `alg = LinearAlgebra.QRIteration()`: Calls `LAPACK.gesvd!` (typically slower but more accurate) .

Julia 1.3

The `alg` keyword argument requires Julia 1.3 or later.

Examples

    julia> A = rand(4,3);

    julia> F = svd(A); # Store the Factorization Object

    julia> A ≈ F.U * Diagonal(F.S) * F.Vt
    true

    julia> U, S, V = F; # destructuring via iteration

    julia> A ≈ U * Diagonal(S) * V'
    true

    julia> Uonly, = svd(A); # Store U only

    julia> Uonly == U
    true

source

    svd(A, B) -> GeneralizedSVD

Compute the generalized SVD of `A` and `B`, returning a `GeneralizedSVD` factorization object `F` such that `[A;B] = [F.U * F.D1; F.V * F.D2] * F.R0 * F.Q'`

- `U` is a M-by-M orthogonal matrix,
- `V` is a P-by-P orthogonal matrix,
- `Q` is a N-by-N orthogonal matrix,
- `D1` is a M-by-(K+L) diagonal matrix with 1s in the first K entries,
- `D2` is a P-by-(K+L) matrix whose top right L-by-L block is diagonal,
- `R0` is a (K+L)-by-N matrix whose rightmost (K+L)-by-(K+L) block is nonsingular upper block triangular,

`K+L` is the effective numerical rank of the matrix `[A; B]`.

Iterating the decomposition produces the components `U`, `V`, `Q`, `D1`, `D2`, and `R0`.

The generalized SVD is used in applications such as when one wants to compare how much belongs to `A` vs. how much belongs to `B`, as in human vs yeast genome, or signal vs noise, or between clusters vs within clusters. (See Edelman and Wang for discussion: https://arxiv.org/abs/1901.00485)

It decomposes `[A; B]` into `[UC; VS]H`, where `[UC; VS]` is a natural orthogonal basis for the column space of `[A; B]`, and `H = RQ'` is a natural non-orthogonal basis for the rowspace of `[A;B]`, where the top rows are most closely attributed to the `A` matrix, and the bottom to the `B` matrix. The multi-cosine/sine matrices `C` and `S` provide a multi-measure of how much `A` vs how much `B`, and `U` and `V` provide directions in which these are measured.

Examples

    julia> A = randn(3,2); B=randn(4,2);

    julia> F = svd(A, B);

    julia> U,V,Q,C,S,R = F;

    julia> H = R*Q';

    julia> [A; B] ≈ [U*C; V*S]*H
    true

    julia> [A; B] ≈ [F.U*F.D1; F.V*F.D2]*F.R0*F.Q'
    true

    julia> Uonly, = svd(A,B);

    julia> U == Uonly
    true

source
`LinearAlgebra.svd!` — Function

    svd!(A; full::Bool = false, alg::Algorithm = default_svd_alg(A)) -> SVD

`svd!` is the same as `svd`, but saves space by overwriting the input `A`, instead of creating a copy. See documentation of `svd` for details.

source

    svd!(A, B) -> GeneralizedSVD

`svd!` is the same as `svd`, but modifies the arguments `A` and `B` in-place, instead of making copies. See documentation of `svd` for details.

source
`LinearAlgebra.svdvals` — Function

    svdvals(A)

Return the singular values of `A` in descending order.

Examples

    julia> A = [1. 0. 0. 0. 2.; 0. 0. 3. 0. 0.; 0. 0. 0. 0. 0.; 0. 2. 0. 0. 0.]
    4×5 Matrix{Float64}:
     1.0  0.0  0.0  0.0  2.0
     0.0  0.0  3.0  0.0  0.0
     0.0  0.0  0.0  0.0  0.0
     0.0  2.0  0.0  0.0  0.0

    julia> svdvals(A)
    4-element Vector{Float64}:
     3.0
     2.23606797749979
     2.0
     0.0

source

    svdvals(A, B)

Return the generalized singular values from the generalized singular value decomposition of `A` and `B`. See also `svd`.

Examples

    julia> A = [1. 0.; 0. -1.]
    2×2 Matrix{Float64}:
     1.0   0.0
     0.0  -1.0

    julia> B = [0. 1.; 1. 0.]
    2×2 Matrix{Float64}:
     0.0  1.0
     1.0  0.0

    julia> svdvals(A, B)
    2-element Vector{Float64}:
     1.0
     1.0

source
`LinearAlgebra.svdvals!` — Function

    svdvals!(A)

Return the singular values of `A`, saving space by overwriting the input. See also `svdvals` and `svd`.

source

    svdvals!(A, B)

Return the generalized singular values from the generalized singular value decomposition of `A` and `B`, saving space by overwriting `A` and `B`. See also `svd` and `svdvals`.

source
`LinearAlgebra.Givens` — Type

    LinearAlgebra.Givens(i1,i2,c,s) -> G

A Givens rotation linear operator. The fields `c` and `s` represent the cosine and sine of the rotation angle, respectively. The `Givens` type supports left multiplication `G*A` and conjugated transpose right multiplication `A*G'`. The type doesn't have a `size` and can therefore be multiplied with matrices of arbitrary size as long as `i2<=size(A,2)` for `G*A` or `i2<=size(A,1)` for `A*G'`.

See also `givens`.

source
`LinearAlgebra.givens` — Function

    givens(f::T, g::T, i1::Integer, i2::Integer) where {T} -> (G::Givens, r::T)

Computes the Givens rotation `G` and scalar `r` such that for any vector `x` where

    x[i1] = f
    x[i2] = g

the result of the multiplication

    y = G*x

has the property that

    y[i1] = r
    y[i2] = 0

See also `LinearAlgebra.Givens`.

source

    givens(A::AbstractArray, i1::Integer, i2::Integer, j::Integer) -> (G::Givens, r)

Computes the Givens rotation `G` and scalar `r` such that the result of the multiplication

    B = G*A

has the property that

    B[i1,j] = r
    B[i2,j] = 0

See also `LinearAlgebra.Givens`.

source

    givens(x::AbstractVector, i1::Integer, i2::Integer) -> (G::Givens, r)

Computes the Givens rotation `G` and scalar `r` such that the result of the multiplication

    B = G*x

has the property that

    B[i1] = r
    B[i2] = 0

See also `LinearAlgebra.Givens`.

source
`LinearAlgebra.triu` — Function

    triu(M, k::Integer = 0)

Return the upper triangle of `M` starting from the `k`th superdiagonal.

Examples

    julia> a = fill(1.0, (4,4))
    4×4 Matrix{Float64}:
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0

    julia> triu(a,3)
    4×4 Matrix{Float64}:
     0.0  0.0  0.0  1.0
     0.0  0.0  0.0  0.0
     0.0  0.0  0.0  0.0
     0.0  0.0  0.0  0.0

    julia> triu(a,-3)
    4×4 Matrix{Float64}:
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0

source
`LinearAlgebra.triu!` — Function

    triu!(M)

Upper triangle of a matrix, overwriting `M` in the process. See also `triu`.

source

    triu!(M, k::Integer)

Return the upper triangle of `M` starting from the `k`th superdiagonal, overwriting `M` in the process.

Examples

    julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5]
    5×5 Matrix{Int64}:
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5

    julia> triu!(M, 1)
    5×5 Matrix{Int64}:
     0  2  3  4  5
     0  0  3  4  5
     0  0  0  4  5
     0  0  0  0  5
     0  0  0  0  0

source
`LinearAlgebra.tril` — Function

    tril(M, k::Integer = 0)

Return the lower triangle of `M` starting from the `k`th superdiagonal.

Examples

    julia> a = fill(1.0, (4,4))
    4×4 Matrix{Float64}:
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0

    julia> tril(a,3)
    4×4 Matrix{Float64}:
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0
     1.0  1.0  1.0  1.0

    julia> tril(a,-3)
    4×4 Matrix{Float64}:
     0.0  0.0  0.0  0.0
     0.0  0.0  0.0  0.0
     0.0  0.0  0.0  0.0
     1.0  0.0  0.0  0.0

source
`LinearAlgebra.tril!` — Function

    tril!(M)

Lower triangle of a matrix, overwriting `M` in the process. See also `tril`.

source

    tril!(M, k::Integer)

Return the lower triangle of `M` starting from the `k`th superdiagonal, overwriting `M` in the process.

Examples

    julia> M = [1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5; 1 2 3 4 5]
    5×5 Matrix{Int64}:
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5

    julia> tril!(M, 2)
    5×5 Matrix{Int64}:
     1  2  3  0  0
     1  2  3  4  0
     1  2  3  4  5
     1  2  3  4  5
     1  2  3  4  5

source
`LinearAlgebra.diagind` — Function

    diagind(M::AbstractMatrix, k::Integer = 0, indstyle::IndexStyle = IndexLinear())
    diagind(M::AbstractMatrix, indstyle::IndexStyle = IndexLinear())

An `AbstractRange` giving the indices of the `k`th diagonal of the matrix `M`. Optionally, an index style may be specified which determines the type of the range returned. If `indstyle isa IndexLinear` (default), this returns an `AbstractRange{Integer}`. On the other hand, if `indstyle isa IndexCartesian`, this returns an `AbstractRange{CartesianIndex{2}}`.

If `k` is not provided, it is assumed to be `0` (corresponding to the main diagonal).

See also: `diag`, `diagm`, `Diagonal`.

Examples

    julia> A = [1 2 3; 4 5 6; 7 8 9]
    3×3 Matrix{Int64}:
     1  2  3
     4  5  6
     7  8  9

    julia> diagind(A, -1)
    2:4:6

    julia> diagind(A, IndexCartesian())
    StepRangeLen(CartesianIndex(1, 1), CartesianIndex(1, 1), 3)

Julia 1.11

Specifying an `IndexStyle` requires at least Julia 1.11.

source
`LinearAlgebra.diag` — Function

    diag(M, k::Integer=0)

The `k`th diagonal of a matrix, as a vector.

See also `diagm`, `diagind`, `Diagonal`, `isdiag`.

Examples

    julia> A = [1 2 3; 4 5 6; 7 8 9]
    3×3 Matrix{Int64}:
     1  2  3
     4  5  6
     7  8  9

    julia> diag(A,1)
    2-element Vector{Int64}:
     2
     6

source
`LinearAlgebra.diagm` — Function

    diagm(kv::Pair{<:Integer,<:AbstractVector}...)
    diagm(m::Integer, n::Integer, kv::Pair{<:Integer,<:AbstractVector}...)

Construct a matrix from `Pair`s of diagonals and vectors. Vector `kv.second` will be placed on the `kv.first` diagonal. By default the matrix is square and its size is inferred from `kv`, but a non-square size `m`×`n` (padded with zeros as needed) can be specified by passing `m,n` as the first arguments. For repeated diagonal indices `kv.first` the values in the corresponding vectors `kv.second` will be added.

`diagm` constructs a full matrix; if you want storage-efficient versions with fast arithmetic, see `Diagonal`, `Bidiagonal` `Tridiagonal` and `SymTridiagonal`.

Examples

    julia> diagm(1 => [1,2,3])
    4×4 Matrix{Int64}:
     0  1  0  0
     0  0  2  0
     0  0  0  3
     0  0  0  0

    julia> diagm(1 => [1,2,3], -1 => [4,5])
    4×4 Matrix{Int64}:
     0  1  0  0
     4  0  2  0
     0  5  0  3
     0  0  0  0

    julia> diagm(1 => [1,2,3], 1 => [1,2,3])
    4×4 Matrix{Int64}:
     0  2  0  0
     0  0  4  0
     0  0  0  6
     0  0  0  0

source

    diagm(v::AbstractVector)
    diagm(m::Integer, n::Integer, v::AbstractVector)

Construct a matrix with elements of the vector as diagonal elements. By default, the matrix is square and its size is given by `length(v)`, but a non-square size `m`×`n` can be specified by passing `m,n` as the first arguments. The diagonal will be zero-padded if necessary.

Examples

    julia> diagm([1,2,3])
    3×3 Matrix{Int64}:
     1  0  0
     0  2  0
     0  0  3

    julia> diagm(4, 5, [1,2,3])
    4×5 Matrix{Int64}:
     1  0  0  0  0
     0  2  0  0  0
     0  0  3  0  0
     0  0  0  0  0

source
`LinearAlgebra.rank` — Function

    rank(::QRSparse{Tv,Ti}) -> Ti

Return the rank of the QR factorization

source

    rank(S::SparseMatrixCSC{Tv,Ti}; [tol::Real]) -> Ti

Calculate rank of `S` by calculating its QR factorization. Values smaller than `tol` are considered as zero. See SPQR's manual.

source

    rank(A::AbstractMatrix; atol::Real=0, rtol::Real=atol>0 ? 0 : n*ϵ)
    rank(A::AbstractMatrix, rtol::Real)

Compute the numerical rank of a matrix by counting how many outputs of `svdvals(A)` are greater than `max(atol, rtol*σ₁)` where `σ₁` is `A`'s largest calculated singular value. `atol` and `rtol` are the absolute and relative tolerances, respectively. The default relative tolerance is `n*ϵ`, where `n` is the size of the smallest dimension of `A`, and `ϵ` is the `eps` of the element type of `A`.

Note

Numerical rank can be a sensitive and imprecise characterization of ill-conditioned matrices with singular values that are close to the threshold tolerance `max(atol, rtol*σ₁)`. In such cases, slight perturbations to the singular-value computation or to the matrix can change the result of `rank` by pushing one or more singular values across the threshold. These variations can even occur due to changes in floating-point errors between different Julia versions, architectures, compilers, or operating systems.

Julia 1.1

The `atol` and `rtol` keyword arguments requires at least Julia 1.1. In Julia 1.0 `rtol` is available as a positional argument, but this will be deprecated in Julia 2.0.

Examples

    julia> rank(Matrix(I, 3, 3))
    3

    julia> rank(diagm(0 => [1, 0, 2]))
    2

    julia> rank(diagm(0 => [1, 0.001, 2]), rtol=0.1)
    2

    julia> rank(diagm(0 => [1, 0.001, 2]), rtol=0.00001)
    3

    julia> rank(diagm(0 => [1, 0.001, 2]), atol=1.5)
    1

source

    rank(S::SVD{<:Any, T}; atol::Real=0, rtol::Real=min(n,m)*ϵ) where {T}

Compute the numerical rank of the SVD object `S` by counting how many singular values are greater than `max(atol, rtol*σ₁)` where `σ₁` is the largest calculated singular value. This is equivalent to the default `rank(::AbstractMatrix)` method except that it re-uses an existing SVD factorization. `atol` and `rtol` are the absolute and relative tolerances, respectively. The default relative tolerance is `n*ϵ`, where `n` is the size of the smallest dimension of UΣV' and `ϵ` is the `eps` of the element type of `S`.

Julia 1.12

The `rank(::SVD)` method requires at least Julia 1.12.

source

    rank(A::QRPivoted{<:Any, T}; atol::Real=0, rtol::Real=min(n,m)*ϵ) where {T}

Compute the numerical rank of the QR factorization `A` by counting how many diagonal entries of `A.factors` are greater than `max(atol, rtol*Δ₁)` where `Δ₁` is the largest calculated such entry. This is similar to the `rank(::AbstractMatrix)` method insofar as it counts the number of (numerically) nonzero coefficients from a matrix factorization, although the default method uses an SVD instead of a QR factorization. Like `rank(::SVD)`, this method also re-uses an existing matrix factorization.

Using a QR factorization to compute rank should typically produce the same result as using SVD, although it may be more prone to overestimating the rank in pathological cases where the matrix is ill-conditioned. It is also worth noting that it is generally faster to compute a QR factorization than it is to compute an SVD, so this method may be preferred when performance is a concern.

`atol` and `rtol` are the absolute and relative tolerances, respectively. The default relative tolerance is `n*ϵ`, where `n` is the size of the smallest dimension of `A` and `ϵ` is the `eps` of the element type of `A`.

Julia 1.12

The `rank(::QRPivoted)` method requires at least Julia 1.12.

source
`LinearAlgebra.norm` — Function

    norm(A, p::Real=2)

For any iterable container `A` (including arrays of any dimension) of numbers (or any element type for which `norm` is defined), compute the `p`-norm (defaulting to `p=2`) as if `A` were a vector of the corresponding length.

The `p`-norm is defined as

\[\|A\|_p = \left( \sum_{i=1}^n | a_i | ^p \right)^{1/p}\]

with $a_i$ the entries of $A$, $| a_i |$ the `norm` of $a_i$, and $n$ the length of $A$. Since the `p`-norm is computed using the `norm`s of the entries of `A`, the `p`-norm of a vector of vectors is not compatible with the interpretation of it as a block vector in general if `p != 2`.

`p` can assume any numeric value (even though not all values produce a mathematically valid vector norm). In particular, `norm(A, Inf)` returns the largest value in `abs.(A)`, whereas `norm(A, -Inf)` returns the smallest. If `A` is a matrix and `p=2`, then this is equivalent to the Frobenius norm.

The second argument `p` is not necessarily a part of the interface for `norm`, i.e. a custom type may only implement `norm(A)` without second argument.

Use `opnorm` to compute the operator norm of a matrix.

Examples

    julia> v = [3, -2, 6]
    3-element Vector{Int64}:
      3
     -2
      6

    julia> norm(v)
    7.0

    julia> norm(v, 1)
    11.0

    julia> norm(v, Inf)
    6.0

    julia> norm([1 2 3; 4 5 6; 7 8 9])
    16.881943016134134

    julia> norm([1 2 3 4 5 6 7 8 9])
    16.881943016134134

    julia> norm(1:9)
    16.881943016134134

    julia> norm(hcat(v,v), 1) == norm(vcat(v,v), 1) != norm([v,v], 1)
    true

    julia> norm(hcat(v,v), 2) == norm(vcat(v,v), 2) == norm([v,v], 2)
    true

    julia> norm(hcat(v,v), Inf) == norm(vcat(v,v), Inf) != norm([v,v], Inf)
    true

source

    norm(x::Number, p::Real=2)

For numbers, return $\left( |x|^p \right)^{1/p}$.

Examples

    julia> norm(2, 1)
    2.0

    julia> norm(-2, 1)
    2.0

    julia> norm(2, 2)
    2.0

    julia> norm(-2, 2)
    2.0

    julia> norm(2, Inf)
    2.0

    julia> norm(-2, Inf)
    2.0

source
`LinearAlgebra.opnorm` — Function

    opnorm(A::AbstractMatrix, p::Real=2)

Compute the operator norm (or matrix norm) induced by the vector `p`-norm, where valid values of `p` are `1`, `2`, or `Inf`. (Note that for sparse matrices, `p=2` is currently not implemented.) Use `norm` to compute the Frobenius norm.

When `p=1`, the operator norm is the maximum absolute column sum of `A`:

\[\|A\|_1 = \max_{1 ≤ j ≤ n} \sum_{i=1}^m | a_{ij} |\]

with $a_{ij}$ the entries of $A$, and $m$ and $n$ its dimensions.

When `p=2`, the operator norm is the spectral norm, equal to the largest singular value of `A`.

When `p=Inf`, the operator norm is the maximum absolute row sum of `A`:

\[\|A\|_\infty = \max_{1 ≤ i ≤ m} \sum _{j=1}^n | a_{ij} |\]

Examples

    julia> A = [1 -2 -3; 2 3 -1]
    2×3 Matrix{Int64}:
     1  -2  -3
     2   3  -1

    julia> opnorm(A, Inf)
    6.0

    julia> opnorm(A, 1)
    5.0

source

    opnorm(x::Number, p::Real=2)

For numbers, return $\left( |x|^p \right)^{1/p}$. This is equivalent to `norm`.

source

    opnorm(A::Adjoint{<:Any,<:AbstractVector}, q::Real=2)
    opnorm(A::Transpose{<:Any,<:AbstractVector}, q::Real=2)

For Adjoint/Transpose-wrapped vectors, return the operator $q$-norm of `A`, which is equivalent to the `p`-norm with value `p = q/(q-1)`. They coincide at `p = q = 2`. Use `norm` to compute the `p` norm of `A` as a vector.

The difference in norm between a vector space and its dual arises to preserve the relationship between duality and the dot product, and the result is consistent with the operator `p`-norm of a `1 × n` matrix.

Examples

    julia> v = [1; im];

    julia> vc = v';

    julia> opnorm(vc, 1)
    1.0

    julia> norm(vc, 1)
    2.0

    julia> norm(v, 1)
    2.0

    julia> opnorm(vc, 2)
    1.4142135623730951

    julia> norm(vc, 2)
    1.4142135623730951

    julia> norm(v, 2)
    1.4142135623730951

    julia> opnorm(vc, Inf)
    2.0

    julia> norm(vc, Inf)
    1.0

    julia> norm(v, Inf)
    1.0

source
`LinearAlgebra.normalize!` — Function

    normalize!(a::AbstractArray, p::Real=2)

Normalize the array `a` in-place so that its `p`-norm equals unity, i.e. `norm(a, p) == 1`. See also `normalize` and `norm`.

source
`LinearAlgebra.normalize` — Function

    normalize(a, p::Real=2)

Normalize `a` so that its `p`-norm equals unity, i.e. `norm(a, p) == 1`. For scalars, this is similar to sign(a), except normalize(0) = NaN. See also `normalize!`, `norm`, and `sign`.

Examples

    julia> a = [1,2,4];

    julia> b = normalize(a)
    3-element Vector{Float64}:
     0.2182178902359924
     0.4364357804719848
     0.8728715609439696

    julia> norm(b)
    1.0

    julia> c = normalize(a, 1)
    3-element Vector{Float64}:
     0.14285714285714285
     0.2857142857142857
     0.5714285714285714

    julia> norm(c, 1)
    1.0

    julia> a = [1 2 4 ; 1 2 4]
    2×3 Matrix{Int64}:
     1  2  4
     1  2  4

    julia> norm(a)
    6.48074069840786

    julia> normalize(a)
    2×3 Matrix{Float64}:
     0.154303  0.308607  0.617213
     0.154303  0.308607  0.617213

    julia> normalize(3, 1)
    1.0

    julia> normalize(-8, 1)
    -1.0

    julia> normalize(0, 1)
    NaN

source
`LinearAlgebra.cond` — Function

    cond(M, p::Real=2)

Condition number of the matrix `M`, computed using the operator `p`-norm. Valid values for `p` are `1`, `2` (default), or `Inf`.

source
`LinearAlgebra.condskeel` — Function

    condskeel(M, [x, p::Real=Inf])

\[\kappa_S(M, p) = \left\Vert \left\vert M \right\vert \left\vert M^{-1} \right\vert \right\Vert_p \\ \kappa_S(M, x, p) = \frac{\left\Vert \left\vert M \right\vert \left\vert M^{-1} \right\vert \left\vert x \right\vert \right\Vert_p}{\left \Vert x \right \Vert_p}\]

Skeel condition number $\kappa_S$ of the matrix `M`, optionally with respect to the vector `x`, as computed using the operator `p`-norm. $\left\vert M \right\vert$ denotes the matrix of (entry wise) absolute values of $M$; $\left\vert M \right\vert_{ij} = \left\vert M_{ij} \right\vert$. Valid values for `p` are `1`, `2` and `Inf` (default).

This quantity is also known in the literature as the Bauer condition number, relative condition number, or componentwise relative condition number.

source
`LinearAlgebra.tr` — Function

    tr(M)

Matrix trace. Sums the diagonal elements of `M`.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> tr(A)
    5

source
`LinearAlgebra.det` — Function

    det(M)

Matrix determinant.

See also: `logdet` and `logabsdet`.

Examples

    julia> M = [1 0; 2 2]
    2×2 Matrix{Int64}:
     1  0
     2  2

    julia> det(M)
    2.0

Note that, in general, `det` computes a floating-point approximation of the determinant, even for integer matrices, typically via Gaussian elimination. Julia includes an exact algorithm for integer determinants (the Bareiss algorithm), but only uses it by default for `BigInt` matrices (since determinants quickly overflow any fixed integer precision):

    julia> det(BigInt[1 0; 2 2]) # exact integer determinant
    2

source
`LinearAlgebra.logdet` — Function

    logdet(M)

Logarithm of matrix determinant. Equivalent to `log(det(M))`, but may provide increased accuracy and avoids overflow/underflow.

Examples

    julia> M = [1 0; 2 2]
    2×2 Matrix{Int64}:
     1  0
     2  2

    julia> logdet(M)
    0.6931471805599453

    julia> logdet(Matrix(I, 3, 3))
    0.0

source
`LinearAlgebra.logabsdet` — Function

    logabsdet(M)

Log of absolute value of matrix determinant. Equivalent to `(log(abs(det(M))), sign(det(M)))`, but may provide increased accuracy and/or speed.

Examples

    julia> A = [-1. 0.; 0. 1.]
    2×2 Matrix{Float64}:
     -1.0  0.0
      0.0  1.0

    julia> det(A)
    -1.0

    julia> logabsdet(A)
    (0.0, -1.0)

    julia> B = [2. 0.; 0. 1.]
    2×2 Matrix{Float64}:
     2.0  0.0
     0.0  1.0

    julia> det(B)
    2.0

    julia> logabsdet(B)
    (0.6931471805599453, 1.0)

source
`Base.inv` — Method

    inv(M)

Matrix inverse. Computes matrix `N` such that `M * N = I`, where `I` is the identity matrix. Computed by solving the left-division `N = M \ I`.

A `SingularException` is thrown if `M` fails numerical inversion.

Examples

    julia> M = [2 5; 1 3]
    2×2 Matrix{Int64}:
     2  5
     1  3

    julia> N = inv(M)
    2×2 Matrix{Float64}:
      3.0  -5.0
     -1.0   2.0

    julia> M*N == N*M == Matrix(I, 2, 2)
    true

source
`LinearAlgebra.pinv` — Function

    pinv(M; atol::Real=0, rtol::Real=atol>0 ? 0 : n*ϵ)
    pinv(M, rtol::Real) = pinv(M; rtol=rtol) # to be deprecated in Julia 2.0

Computes the Moore-Penrose pseudoinverse.

For matrices `M` with floating point elements, it is convenient to compute the pseudoinverse by inverting only singular values greater than `max(atol, rtol*σ₁)` where `σ₁` is the largest singular value of `M`.

The optimal choice of absolute (`atol`) and relative tolerance (`rtol`) varies both with the value of `M` and the intended application of the pseudoinverse. The default relative tolerance is `n*ϵ`, where `n` is the size of the smallest dimension of `M`, and `ϵ` is the `eps` of the element type of `M`.

For inverting dense ill-conditioned matrices in a least-squares sense, `rtol = sqrt(eps(real(float(oneunit(eltype(M))))))` is recommended.

For more information, see [issue8859], [B96], [S84], [KY88].

Examples

    julia> M = [1.5 1.3; 1.2 1.9]
    2×2 Matrix{Float64}:
     1.5  1.3
     1.2  1.9

    julia> N = pinv(M)
    2×2 Matrix{Float64}:
      1.47287   -1.00775
     -0.930233   1.16279

    julia> M * N
    2×2 Matrix{Float64}:
     1.0          -2.22045e-16
     4.44089e-16   1.0

source
`LinearAlgebra.nullspace` — Function

    nullspace(M; atol::Real=0, rtol::Real=atol>0 ? 0 : n*ϵ)
    nullspace(M, rtol::Real) = nullspace(M; rtol=rtol) # to be deprecated in Julia 2.0

Computes a basis for the nullspace of `M` by including the singular vectors of `M` whose singular values have magnitudes smaller than `max(atol, rtol*σ₁)`, where `σ₁` is `M`'s largest singular value.

By default, the relative tolerance `rtol` is `n*ϵ`, where `n` is the size of the smallest dimension of `M`, and `ϵ` is the `eps` of the element type of `M`.

Examples

    julia> M = [1 0 0; 0 1 0; 0 0 0]
    3×3 Matrix{Int64}:
     1  0  0
     0  1  0
     0  0  0

    julia> nullspace(M)
    3×1 Matrix{Float64}:
     0.0
     0.0
     1.0

    julia> nullspace(M, rtol=3)
    3×3 Matrix{Float64}:
     0.0  1.0  0.0
     1.0  0.0  0.0
     0.0  0.0  1.0

    julia> nullspace(M, atol=0.95)
    3×1 Matrix{Float64}:
     0.0
     0.0
     1.0

source
`Base.kron` — Function

    kron(A, B)

Computes the Kronecker product of two vectors, matrices or numbers.

For real vectors `v` and `w`, the Kronecker product is related to the outer product by `kron(v,w) == vec(w * transpose(v))` or `w * transpose(v) == reshape(kron(v,w), (length(w), length(v)))`. Note how the ordering of `v` and `w` differs on the left and right of these expressions (due to column-major storage). For complex vectors, the outer product `w * v'` also differs by conjugation of `v`.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> B = [im 1; 1 -im]
    2×2 Matrix{Complex{Int64}}:
     0+1im  1+0im
     1+0im  0-1im

    julia> kron(A, B)
    4×4 Matrix{Complex{Int64}}:
     0+1im  1+0im  0+2im  2+0im
     1+0im  0-1im  2+0im  0-2im
     0+3im  3+0im  0+4im  4+0im
     3+0im  0-3im  4+0im  0-4im

    julia> v = [1, 2]; w = [3, 4, 5];

    julia> w*transpose(v)
    3×2 Matrix{Int64}:
     3   6
     4   8
     5  10

    julia> reshape(kron(v,w), (length(w), length(v)))
    3×2 Matrix{Int64}:
     3   6
     4   8
     5  10

source
`Base.kron!` — Function

    kron!(C, A, B)

Computes the Kronecker product of `A` and `B` and stores the result in `C`, overwriting the existing content of `C`. This is the in-place version of `kron`.

Julia 1.6

This function requires Julia 1.6 or later.

source
`Base.exp` — Method

    exp(A::AbstractMatrix)

Compute the matrix exponential of `A`, defined by

\[e^A = \sum_{n=0}^{\infty} \frac{A^n}{n!}.\]

For symmetric or Hermitian `A`, an eigendecomposition (`eigen`) is used, otherwise the scaling and squaring algorithm (see [H05]) is chosen.

Examples

    julia> A = Matrix(1.0I, 2, 2)
    2×2 Matrix{Float64}:
     1.0  0.0
     0.0  1.0

    julia> exp(A)
    2×2 Matrix{Float64}:
     2.71828  0.0
     0.0      2.71828

source
`Base.cis` — Method

    cis(A::AbstractMatrix)

More efficient method for `exp(im*A)` of square matrix `A` (especially if `A` is `Hermitian` or real-`Symmetric`).

See also `cispi`, `sincos`, `exp`.

Julia 1.7

Support for using `cis` with matrices was added in Julia 1.7.

Examples

    julia> cis([π 0; 0 π]) ≈ -I
    true

source
`Base.:^` — Method

    ^(A::AbstractMatrix, p::Number)

Matrix power, equivalent to $\exp(p\log(A))$

Examples

    julia> [1 2; 0 3]^3
    2×2 Matrix{Int64}:
     1  26
     0  27

source
`Base.:^` — Method

    ^(b::Number, A::AbstractMatrix)

Matrix exponential, equivalent to $\exp(\log(b)A)$.

Julia 1.1

Support for raising `Irrational` numbers (like `ℯ`) to a matrix was added in Julia 1.1.

Examples

    julia> 2^[1 2; 0 3]
    2×2 Matrix{Float64}:
     2.0  6.0
     0.0  8.0

    julia> ℯ^[1 2; 0 3]
    2×2 Matrix{Float64}:
     2.71828  17.3673
     0.0      20.0855

source
`Base.log` — Method

    log(A::AbstractMatrix)

If `A` has no negative real eigenvalue, compute the principal matrix logarithm of `A`, i.e. the unique matrix $X$ such that $e^X = A$ and $-\pi < Im(\lambda) < \pi$ for all the eigenvalues $\lambda$ of $X$. If `A` has nonpositive eigenvalues, a nonprincipal matrix function is returned whenever possible.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used, if `A` is triangular an improved version of the inverse scaling and squaring method is employed (see [AH12] and [AHR13]). If `A` is real with no negative eigenvalues, then the real Schur form is computed. Otherwise, the complex Schur form is computed. Then the upper (quasi-)triangular algorithm in [AHR13] is used on the upper (quasi-)triangular factor.

Examples

    julia> A = Matrix(2.7182818*I, 2, 2)
    2×2 Matrix{Float64}:
     2.71828  0.0
     0.0      2.71828

    julia> log(A)
    2×2 Matrix{Float64}:
     1.0  0.0
     0.0  1.0

source
`Base.sqrt` — Method

    sqrt(x)

Return $\sqrt{x}$.

Throw a `DomainError` for negative `Real` arguments. Use `Complex` negative arguments instead to obtain a `Complex` result.

The prefix operator `√` is equivalent to `sqrt`.

Branch cut

`sqrt` has a branch cut along the negative real axis; `-0.0im` is taken to be below the axis.

See also: `hypot`.

Examples

    julia> sqrt(big(81))
    9.0

    julia> sqrt(big(-81))
    ERROR: DomainError with -81.0:
    NaN result for non-NaN input.
    Stacktrace:
     [1] sqrt(::BigFloat) at ./mpfr.jl:501
    [...]

    julia> sqrt(big(complex(-81)))
    0.0 + 9.0im

    julia> sqrt(-81 - 0.0im)  # -0.0im is below the branch cut
    0.0 - 9.0im

    julia> .√(1:4)
    4-element Vector{Float64}:
     1.0
     1.4142135623730951
     1.7320508075688772
     2.0

source

    sqrt(A::AbstractMatrix)

If `A` has no negative real eigenvalues, compute the principal matrix square root of `A`, that is the unique matrix $X$ with eigenvalues having positive real part such that $X^2 = A$. Otherwise, a nonprincipal square root is returned.

If `A` is real-symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the square root. For such matrices, eigenvalues λ that appear to be slightly negative due to roundoff errors are treated as if they were zero. More precisely, matrices with all eigenvalues `≥ -rtol*(max |λ|)` are treated as semidefinite (yielding a Hermitian square root), with negative eigenvalues taken to be zero. `rtol` is a keyword argument to `sqrt` (in the Hermitian/real-symmetric case only) that defaults to machine precision scaled by `size(A,1)`.

Otherwise, the square root is determined by means of the Björck-Hammarling method [BH83], which computes the complex Schur form (`schur`) and then the complex square root of the triangular factor. If a real square root exists, then an extension of this method [H87] that computes the real Schur form and then the real square root of the quasi-triangular factor is instead used.

Examples

    julia> A = [4 0; 0 4]
    2×2 Matrix{Int64}:
     4  0
     0  4

    julia> sqrt(A)
    2×2 Matrix{Float64}:
     2.0  0.0
     0.0  2.0

source
`Base.Math.cbrt` — Method

    cbrt(A::AbstractMatrix{<:Real})

Computes the real-valued cube root of a real-valued matrix `A`. If `T = cbrt(A)`, then we have `T*T*T ≈ A`, see example given below.

If `A` is symmetric, i.e., of type `HermOrSym{<:Real}`, then (`eigen`) is used to find the cube root. Otherwise, a specialized version of the p-th root algorithm [S03] is utilized, which exploits the real-valued Schur decomposition (`schur`) to compute the cube root.

Examples

    julia> A = [0.927524 -0.15857; -1.3677 -1.01172]
    2×2 Matrix{Float64}:
      0.927524  -0.15857
     -1.3677    -1.01172

    julia> T = cbrt(A)
    2×2 Matrix{Float64}:
      0.910077  -0.151019
     -1.30257   -0.936818

    julia> T*T*T ≈ A
    true

source
`Base.cos` — Method

    cos(A::AbstractMatrix)

Compute the matrix cosine of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the cosine. Otherwise, the cosine is determined by calling `exp`.

Examples

    julia> cos(fill(1.0, (2,2)))
    2×2 Matrix{Float64}:
      0.291927  -0.708073
     -0.708073   0.291927

source
`Base.sin` — Method

    sin(A::AbstractMatrix)

Compute the matrix sine of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the sine. Otherwise, the sine is determined by calling `exp`.

Examples

    julia> sin(fill(1.0, (2,2)))
    2×2 Matrix{Float64}:
     0.454649  0.454649
     0.454649  0.454649

source
`Base.Math.sincos` — Method

    sincos(A::AbstractMatrix)

Compute the matrix sine and cosine of a square matrix `A`.

Examples

    julia> S, C = sincos(fill(1.0, (2,2)));

    julia> S
    2×2 Matrix{Float64}:
     0.454649  0.454649
     0.454649  0.454649

    julia> C
    2×2 Matrix{Float64}:
      0.291927  -0.708073
     -0.708073   0.291927

source
`Base.tan` — Method

    tan(A::AbstractMatrix)

Compute the matrix tangent of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the tangent. Otherwise, the tangent is determined by calling `exp`.

Examples

    julia> tan(fill(1.0, (2,2)))
    2×2 Matrix{Float64}:
     -1.09252  -1.09252
     -1.09252  -1.09252

source
`Base.Math.sec` — Method

    sec(A::AbstractMatrix)

Compute the matrix secant of a square matrix `A`.

source
`Base.Math.csc` — Method

    csc(A::AbstractMatrix)

Compute the matrix cosecant of a square matrix `A`.

source
`Base.Math.cot` — Method

    cot(A::AbstractMatrix)

Compute the matrix cotangent of a square matrix `A`.

source
`Base.cosh` — Method

    cosh(A::AbstractMatrix)

Compute the matrix hyperbolic cosine of a square matrix `A`.

source
`Base.sinh` — Method

    sinh(A::AbstractMatrix)

Compute the matrix hyperbolic sine of a square matrix `A`.

source
`Base.tanh` — Method

    tanh(A::AbstractMatrix)

Compute the matrix hyperbolic tangent of a square matrix `A`.

source
`Base.Math.sech` — Method

    sech(A::AbstractMatrix)

Compute the matrix hyperbolic secant of square matrix `A`.

source
`Base.Math.csch` — Method

    csch(A::AbstractMatrix)

Compute the matrix hyperbolic cosecant of square matrix `A`.

source
`Base.Math.coth` — Method

    coth(A::AbstractMatrix)

Compute the matrix hyperbolic cotangent of square matrix `A`.

source
`Base.acos` — Method

    acos(A::AbstractMatrix)

Compute the inverse matrix cosine of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the inverse cosine. Otherwise, the inverse cosine is determined by using `log` and `sqrt`. For the theory and logarithmic formulas used to compute this function, see [AH16_1].

Examples

    julia> acos(cos([0.5 0.1; -0.2 0.3]))
    2×2 Matrix{ComplexF64}:
      0.5-8.32667e-17im  0.1+0.0im
     -0.2+2.63678e-16im  0.3-3.46945e-16im

source
`Base.asin` — Method

    asin(A::AbstractMatrix)

Compute the inverse matrix sine of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the inverse sine. Otherwise, the inverse sine is determined by using `log` and `sqrt`. For the theory and logarithmic formulas used to compute this function, see [AH16_2].

Examples

    julia> asin(sin([0.5 0.1; -0.2 0.3]))
    2×2 Matrix{ComplexF64}:
      0.5-4.16334e-17im  0.1-5.55112e-17im
     -0.2+9.71445e-17im  0.3-1.249e-16im

source
`Base.atan` — Method

    atan(A::AbstractMatrix)

Compute the inverse matrix tangent of a square matrix `A`.

If `A` is symmetric or Hermitian, its eigendecomposition (`eigen`) is used to compute the inverse tangent. Otherwise, the inverse tangent is determined by using `log`. For the theory and logarithmic formulas used to compute this function, see [AH16_3].

Examples

    julia> atan(tan([0.5 0.1; -0.2 0.3]))
    2×2 Matrix{ComplexF64}:
      0.5+1.38778e-17im  0.1-2.77556e-17im
     -0.2+6.93889e-17im  0.3-4.16334e-17im

source
`Base.Math.asec` — Method

    asec(A::AbstractMatrix)

Compute the inverse matrix secant of `A`.

source
`Base.Math.acsc` — Method

    acsc(A::AbstractMatrix)

Compute the inverse matrix cosecant of `A`.

source
`Base.Math.acot` — Method

    acot(A::AbstractMatrix)

Compute the inverse matrix cotangent of `A`.

source
`Base.acosh` — Method

    acosh(A::AbstractMatrix)

Compute the inverse hyperbolic matrix cosine of a square matrix `A`. For the theory and logarithmic formulas used to compute this function, see [AH16_4].

source
`Base.asinh` — Method

    asinh(A::AbstractMatrix)

Compute the inverse hyperbolic matrix sine of a square matrix `A`. For the theory and logarithmic formulas used to compute this function, see [AH16_5].

source
`Base.atanh` — Method

    atanh(A::AbstractMatrix)

Compute the inverse hyperbolic matrix tangent of a square matrix `A`. For the theory and logarithmic formulas used to compute this function, see [AH16_6].

source
`Base.Math.asech` — Method

    asech(A::AbstractMatrix)

Compute the inverse matrix hyperbolic secant of `A`.

source
`Base.Math.acsch` — Method

    acsch(A::AbstractMatrix)

Compute the inverse matrix hyperbolic cosecant of `A`.

source
`Base.Math.acoth` — Method

    acoth(A::AbstractMatrix)

Compute the inverse matrix hyperbolic cotangent of `A`.

source
`LinearAlgebra.lyap` — Function

    lyap(A, C)

Computes the solution `X` to the continuous Lyapunov equation `AX + XA' + C = 0`, where no eigenvalue of `A` has a zero real part and no two eigenvalues are negative complex conjugates of each other.

Examples

    julia> A = [3. 4.; 5. 6]
    2×2 Matrix{Float64}:
     3.0  4.0
     5.0  6.0

    julia> B = [1. 1.; 1. 2.]
    2×2 Matrix{Float64}:
     1.0  1.0
     1.0  2.0

    julia> X = lyap(A, B)
    2×2 Matrix{Float64}:
      0.5  -0.5
     -0.5   0.25

    julia> A*X + X*A' ≈ -B
    true

source
`LinearAlgebra.sylvester` — Function

    sylvester(A, B, C)

Computes the solution `X` to the Sylvester equation `AX + XB + C = 0`, where `A`, `B` and `C` have compatible dimensions and `A` and `-B` have no eigenvalues with equal real part.

Examples

    julia> A = [3. 4.; 5. 6]
    2×2 Matrix{Float64}:
     3.0  4.0
     5.0  6.0

    julia> B = [1. 1.; 1. 2.]
    2×2 Matrix{Float64}:
     1.0  1.0
     1.0  2.0

    julia> C = [1. 2.; -2. 1]
    2×2 Matrix{Float64}:
      1.0  2.0
     -2.0  1.0

    julia> X = sylvester(A, B, C)
    2×2 Matrix{Float64}:
     -4.46667   1.93333
      3.73333  -1.8

    julia> A*X + X*B ≈ -C
    true

source
`LinearAlgebra.issuccess` — Function

    issuccess(F::Factorization)

Test that a factorization of a matrix succeeded.

Julia 1.6

`issuccess(::CholeskyPivoted)` requires Julia 1.6 or later.

Examples

    julia> F = cholesky([1 0; 0 1]);

    julia> issuccess(F)
    true

source

    issuccess(F::LU; allowsingular = false)

Test that the LU factorization of a matrix succeeded. By default a factorization that produces a valid but rank-deficient U factor is considered a failure. This can be changed by passing `allowsingular = true`.

Julia 1.11

The `allowsingular` keyword argument was added in Julia 1.11.

Examples

    julia> F = lu([1 2; 1 2], check = false);

    julia> issuccess(F)
    false

    julia> issuccess(F, allowsingular = true)
    true

source
`LinearAlgebra.issymmetric` — Function

    issymmetric(A) -> Bool

Test whether a matrix is symmetric.

Examples

    julia> a = [1 2; 2 -1]
    2×2 Matrix{Int64}:
     1   2
     2  -1

    julia> issymmetric(a)
    true

    julia> b = [1 im; -im 1]
    2×2 Matrix{Complex{Int64}}:
     1+0im  0+1im
     0-1im  1+0im

    julia> issymmetric(b)
    false

source
`LinearAlgebra.isposdef` — Function

    isposdef(A) -> Bool

Test whether a matrix is positive definite (and Hermitian) by trying to perform a Cholesky factorization of `A`.

See also `isposdef!`, `cholesky`.

Examples

    julia> A = [1 2; 2 50]
    2×2 Matrix{Int64}:
     1   2
     2  50

    julia> isposdef(A)
    true

source
`LinearAlgebra.isposdef!` — Function

    isposdef!(A) -> Bool

Test whether a matrix is positive definite (and Hermitian) by trying to perform a Cholesky factorization of `A`, overwriting `A` in the process. See also `isposdef`.

Examples

    julia> A = [1. 2.; 2. 50.];

    julia> isposdef!(A)
    true

    julia> A
    2×2 Matrix{Float64}:
     1.0  2.0
     2.0  6.78233

source
`LinearAlgebra.istril` — Function

    istril(A::AbstractMatrix, k::Integer = 0) -> Bool

Test whether `A` is lower triangular starting from the `k`th superdiagonal.

Examples

    julia> a = [1 2; 2 -1]
    2×2 Matrix{Int64}:
     1   2
     2  -1

    julia> istril(a)
    false

    julia> istril(a, 1)
    true

    julia> c = [1 1 0; 1 1 1; 1 1 1]
    3×3 Matrix{Int64}:
     1  1  0
     1  1  1
     1  1  1

    julia> istril(c)
    false

    julia> istril(c, 1)
    true

source
`LinearAlgebra.istriu` — Function

    istriu(A::AbstractMatrix, k::Integer = 0) -> Bool

Test whether `A` is upper triangular starting from the `k`th superdiagonal.

Examples

    julia> a = [1 2; 2 -1]
    2×2 Matrix{Int64}:
     1   2
     2  -1

    julia> istriu(a)
    false

    julia> istriu(a, -1)
    true

    julia> c = [1 1 1; 1 1 1; 0 1 1]
    3×3 Matrix{Int64}:
     1  1  1
     1  1  1
     0  1  1

    julia> istriu(c)
    false

    julia> istriu(c, -1)
    true

source
`LinearAlgebra.isdiag` — Function

    isdiag(A) -> Bool

Test whether a matrix is diagonal in the sense that `iszero(A[i,j])` is true unless `i == j`. Note that it is not necessary for `A` to be square; if you would also like to check that, you need to check that `size(A, 1) == size(A, 2)`.

Examples

    julia> a = [1 2; 2 -1]
    2×2 Matrix{Int64}:
     1   2
     2  -1

    julia> isdiag(a)
    false

    julia> b = [im 0; 0 -im]
    2×2 Matrix{Complex{Int64}}:
     0+1im  0+0im
     0+0im  0-1im

    julia> isdiag(b)
    true

    julia> c = [1 0 0; 0 2 0]
    2×3 Matrix{Int64}:
     1  0  0
     0  2  0

    julia> isdiag(c)
    true

    julia> d = [1 0 0; 0 2 3]
    2×3 Matrix{Int64}:
     1  0  0
     0  2  3

    julia> isdiag(d)
    false

source
`LinearAlgebra.ishermitian` — Function

    ishermitian(A) -> Bool

Test whether a matrix is Hermitian.

Examples

    julia> a = [1 2; 2 -1]
    2×2 Matrix{Int64}:
     1   2
     2  -1

    julia> ishermitian(a)
    true

    julia> b = [1 im; -im 1]
    2×2 Matrix{Complex{Int64}}:
     1+0im  0+1im
     0-1im  1+0im

    julia> ishermitian(b)
    true

source
`Base.transpose` — Function

    transpose(A)

Lazy transpose. Mutating the returned object should appropriately mutate `A`. Often, but not always, yields `Transpose(A)`, where `Transpose` is a lazy transpose wrapper. Note that this operation is recursive.

This operation is intended for linear algebra usage - for general data manipulation see `permutedims`, which is non-recursive.

Examples

    julia> A = [3 2; 0 0]
    2×2 Matrix{Int64}:
     3  2
     0  0

    julia> B = transpose(A)
    2×2 transpose(::Matrix{Int64}) with eltype Int64:
     3  0
     2  0

    julia> B isa Transpose
    true

    julia> transpose(B) === A # the transpose of a transpose unwraps the parent
    true

    julia> Transpose(B) # however, the constructor always wraps its argument
    2×2 transpose(transpose(::Matrix{Int64})) with eltype Int64:
     3  2
     0  0

    julia> B[1,2] = 4; # modifying B will modify A automatically

    julia> A
    2×2 Matrix{Int64}:
     3  2
     4  0

For complex matrices, the `adjoint` operation is equivalent to a conjugate-transpose.

    julia> A = reshape([Complex(x, x) for x in 1:4], 2, 2)
    2×2 Matrix{Complex{Int64}}:
     1+1im  3+3im
     2+2im  4+4im

    julia> adjoint(A) == conj(transpose(A))
    true

The `transpose` of an `AbstractVector` is a row-vector:

    julia> v = [1,2,3]
    3-element Vector{Int64}:
     1
     2
     3

    julia> transpose(v) # returns a row-vector
    1×3 transpose(::Vector{Int64}) with eltype Int64:
     1  2  3

    julia> transpose(v) * v # compute the dot product
    14

For a matrix of matrices, the individual blocks are recursively operated on:

    julia> C = [1 3; 2 4]
    2×2 Matrix{Int64}:
     1  3
     2  4

    julia> D = reshape([C, 2C, 3C, 4C], 2, 2) # construct a block matrix
    2×2 Matrix{Matrix{Int64}}:
     [1 3; 2 4]  [3 9; 6 12]
     [2 6; 4 8]  [4 12; 8 16]

    julia> transpose(D) # blocks are recursively transposed
    2×2 transpose(::Matrix{Matrix{Int64}}) with eltype Transpose{Int64, Matrix{Int64}}:
     [1 2; 3 4]   [2 4; 6 8]
     [3 6; 9 12]  [4 8; 12 16]

source

    transpose(F::Factorization)

Lazy transpose of the factorization `F`. By default, returns a `TransposeFactorization`, except for `Factorization`s with real `eltype`, in which case returns an `AdjointFactorization`.

source
`LinearAlgebra.transpose!` — Function

    transpose!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}

Transpose the matrix `A` and stores it in the matrix `X`. `size(X)` must be equal to `size(transpose(A))`. No additional memory is allocated other than resizing the rowval and nzval of `X`, if needed.

See `halfperm!`

source

    transpose!(dest,src)

Transpose array `src` and store the result in the preallocated array `dest`, which should have a size corresponding to `(size(src,2),size(src,1))`. No in-place transposition is supported and unexpected results will happen if `src` and `dest` have overlapping memory regions.

Examples

    julia> A = [3+2im 9+2im; 8+7im  4+6im]
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     8+7im  4+6im

    julia> B = zeros(Complex{Int64}, 2, 2)
    2×2 Matrix{Complex{Int64}}:
     0+0im  0+0im
     0+0im  0+0im

    julia> transpose!(B, A);

    julia> B
    2×2 Matrix{Complex{Int64}}:
     3+2im  8+7im
     9+2im  4+6im

    julia> A
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     8+7im  4+6im

source
`LinearAlgebra.Transpose` — Type

    Transpose

Lazy wrapper type for a transpose view of the underlying linear algebra object, usually an `AbstractVector`/`AbstractMatrix`. Usually, the `Transpose` constructor should not be called directly, use `transpose` instead. To materialize the view use `copy`.

This type is intended for linear algebra usage - for general data manipulation see `permutedims`.

Examples

    julia> A = [2 3; 0 0]
    2×2 Matrix{Int64}:
     2  3
     0  0

    julia> Transpose(A)
    2×2 transpose(::Matrix{Int64}) with eltype Int64:
     2  0
     3  0

source
`LinearAlgebra.TransposeFactorization` — Type

    TransposeFactorization

Lazy wrapper type for the transpose of the underlying `Factorization` object. Usually, the `TransposeFactorization` constructor should not be called directly, use `transpose(:: Factorization)` instead.

source
`Base.adjoint` — Function

    A'
    adjoint(A)

Lazy adjoint (conjugate transposition). Note that `adjoint` is applied recursively to elements.

For number types, `adjoint` returns the complex conjugate, and therefore it is equivalent to the identity function for real numbers.

This operation is intended for linear algebra usage - for general data manipulation see `permutedims`.

Examples

    julia> A = [3+2im 9+2im; 0  0]
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     0+0im  0+0im

    julia> B = A' # equivalently adjoint(A)
    2×2 adjoint(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:
     3-2im  0+0im
     9-2im  0+0im

    julia> B isa Adjoint
    true

    julia> adjoint(B) === A # the adjoint of an adjoint unwraps the parent
    true

    julia> Adjoint(B) # however, the constructor always wraps its argument
    2×2 adjoint(adjoint(::Matrix{Complex{Int64}})) with eltype Complex{Int64}:
     3+2im  9+2im
     0+0im  0+0im

    julia> B[1,2] = 4 + 5im; # modifying B will modify A automatically

    julia> A
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     4-5im  0+0im

For real matrices, the `adjoint` operation is equivalent to a `transpose`.

    julia> A = reshape([x for x in 1:4], 2, 2)
    2×2 Matrix{Int64}:
     1  3
     2  4

    julia> A'
    2×2 adjoint(::Matrix{Int64}) with eltype Int64:
     1  2
     3  4

    julia> adjoint(A) == transpose(A)
    true

The adjoint of an `AbstractVector` is a row-vector:

    julia> x = [3, 4im]
    2-element Vector{Complex{Int64}}:
     3 + 0im
     0 + 4im

    julia> x'
    1×2 adjoint(::Vector{Complex{Int64}}) with eltype Complex{Int64}:
     3+0im  0-4im

    julia> x'x # compute the dot product, equivalently x' * x
    25 + 0im

For a matrix of matrices, the individual blocks are recursively operated on:

    julia> A = reshape([x + im*x for x in 1:4], 2, 2)
    2×2 Matrix{Complex{Int64}}:
     1+1im  3+3im
     2+2im  4+4im

    julia> C = reshape([A, 2A, 3A, 4A], 2, 2)
    2×2 Matrix{Matrix{Complex{Int64}}}:
     [1+1im 3+3im; 2+2im 4+4im]  [3+3im 9+9im; 6+6im 12+12im]
     [2+2im 6+6im; 4+4im 8+8im]  [4+4im 12+12im; 8+8im 16+16im]

    julia> C'
    2×2 adjoint(::Matrix{Matrix{Complex{Int64}}}) with eltype Adjoint{Complex{Int64}, Matrix{Complex{Int64}}}:
     [1-1im 2-2im; 3-3im 4-4im]    [2-2im 4-4im; 6-6im 8-8im]
     [3-3im 6-6im; 9-9im 12-12im]  [4-4im 8-8im; 12-12im 16-16im]

source

    adjoint(F::Factorization)

Lazy adjoint of the factorization `F`. By default, returns an `AdjointFactorization` wrapper.

source
`LinearAlgebra.adjoint!` — Function

    adjoint!(X::AbstractSparseMatrixCSC{Tv,Ti}, A::AbstractSparseMatrixCSC{Tv,Ti}) where {Tv,Ti}

Transpose the matrix `A` and stores the adjoint of the elements in the matrix `X`. `size(X)` must be equal to `size(transpose(A))`. No additional memory is allocated other than resizing the rowval and nzval of `X`, if needed.

See `halfperm!`

source

    adjoint!(dest,src)

Conjugate transpose array `src` and store the result in the preallocated array `dest`, which should have a size corresponding to `(size(src,2),size(src,1))`. No in-place transposition is supported and unexpected results will happen if `src` and `dest` have overlapping memory regions.

Examples

    julia> A = [3+2im 9+2im; 8+7im  4+6im]
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     8+7im  4+6im

    julia> B = zeros(Complex{Int64}, 2, 2)
    2×2 Matrix{Complex{Int64}}:
     0+0im  0+0im
     0+0im  0+0im

    julia> adjoint!(B, A);

    julia> B
    2×2 Matrix{Complex{Int64}}:
     3-2im  8-7im
     9-2im  4-6im

    julia> A
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     8+7im  4+6im

source
`LinearAlgebra.Adjoint` — Type

    Adjoint

Lazy wrapper type for an adjoint view of the underlying linear algebra object, usually an `AbstractVector`/`AbstractMatrix`. Usually, the `Adjoint` constructor should not be called directly, use `adjoint` instead. To materialize the view use `copy`.

This type is intended for linear algebra usage - for general data manipulation see `permutedims`.

Examples

    julia> A = [3+2im 9+2im; 0 0]
    2×2 Matrix{Complex{Int64}}:
     3+2im  9+2im
     0+0im  0+0im

    julia> Adjoint(A)
    2×2 adjoint(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:
     3-2im  0+0im
     9-2im  0+0im

source
`LinearAlgebra.AdjointFactorization` — Type

    AdjointFactorization

Lazy wrapper type for the adjoint of the underlying `Factorization` object. Usually, the `AdjointFactorization` constructor should not be called directly, use `adjoint(:: Factorization)` instead.

source
`Base.copy` — Method

    copy(A::Transpose)
    copy(A::Adjoint)

Eagerly evaluate the lazy matrix transpose/adjoint. Note that the transposition is applied recursively to elements.

This operation is intended for linear algebra usage - for general data manipulation see `permutedims`, which is non-recursive.

Examples

    julia> A = [1 2im; -3im 4]
    2×2 Matrix{Complex{Int64}}:
     1+0im  0+2im
     0-3im  4+0im

    julia> T = transpose(A)
    2×2 transpose(::Matrix{Complex{Int64}}) with eltype Complex{Int64}:
     1+0im  0-3im
     0+2im  4+0im

    julia> copy(T)
    2×2 Matrix{Complex{Int64}}:
     1+0im  0-3im
     0+2im  4+0im

source
`LinearAlgebra.stride1` — Function

    stride1(A) -> Int

Return the distance between successive array elements in dimension 1 in units of element size.

Examples

    julia> A = [1,2,3,4]
    4-element Vector{Int64}:
     1
     2
     3
     4

    julia> LinearAlgebra.stride1(A)
    1

    julia> B = view(A, 2:2:4)
    2-element view(::Vector{Int64}, 2:2:4) with eltype Int64:
     2
     4

    julia> LinearAlgebra.stride1(B)
    2

source
`LinearAlgebra.checksquare` — Function

    LinearAlgebra.checksquare(A)

Check that a matrix is square, then return its common dimension. For multiple arguments, return a vector.

Examples

    julia> A = fill(1, (4,4)); B = fill(1, (5,5));

    julia> LinearAlgebra.checksquare(A, B)
    2-element Vector{Int64}:
     4
     5

source
`LinearAlgebra.peakflops` — Function

    LinearAlgebra.peakflops(n::Integer=4096; eltype::DataType=Float64, ntrials::Integer=3, parallel::Bool=false)

`peakflops` computes the peak flop rate of the computer by using double precision `gemm!`. By default, if no arguments are specified, it multiplies two `Float64` matrices of size `n x n`, where `n = 4096`. If the underlying BLAS is using multiple threads, higher flop rates are realized. The number of BLAS threads can be set with `BLAS.set_num_threads(n)`.

If the keyword argument `eltype` is provided, `peakflops` will construct matrices with elements of type `eltype` for calculating the peak flop rate.

By default, `peakflops` will use the best timing from 3 trials. If the `ntrials` keyword argument is provided, `peakflops` will use those many trials for picking the best timing.

If the keyword argument `parallel` is set to `true`, `peakflops` is run in parallel on all the worker processors. The flop rate of the entire parallel computer is returned. When running in parallel, only 1 BLAS thread is used. The argument `n` still refers to the size of the problem that is solved on each processor.

Julia 1.1

This function requires at least Julia 1.1. In Julia 1.0 it is available from the standard library `InteractiveUtils`.

source
`LinearAlgebra.hermitianpart` — Function

    hermitianpart(A::AbstractMatrix, uplo::Symbol=:U) -> Hermitian

Return the Hermitian part of the square matrix `A`, defined as `(A + A') / 2`, as a `Hermitian` matrix. For real matrices `A`, this is also known as the symmetric part of `A`; it is also sometimes called the "operator real part". The optional argument `uplo` controls the corresponding argument of the `Hermitian` view. For real matrices, the latter is equivalent to a `Symmetric` view.

See also `hermitianpart!` for the corresponding in-place operation.

Julia 1.10

This function requires Julia 1.10 or later.

source
`LinearAlgebra.hermitianpart!` — Function

    hermitianpart!(A::AbstractMatrix, uplo::Symbol=:U) -> Hermitian

Overwrite the square matrix `A` in-place with its Hermitian part `(A + A') / 2`, and return `Hermitian(A, uplo)`. For real matrices `A`, this is also known as the symmetric part of `A`.

See also `hermitianpart` for the corresponding out-of-place operation.

Julia 1.10

This function requires Julia 1.10 or later.

source
`LinearAlgebra.copy_adjoint!` — Function

    copy_adjoint!(B::AbstractVecOrMat, ir_dest::AbstractRange{Int}, jr_dest::AbstractRange{Int},
                    A::AbstractVecOrMat, ir_src::AbstractRange{Int}, jr_src::AbstractRange{Int}) -> B

Efficiently copy elements of matrix `A` to `B` with adjunction as follows:

    B[ir_dest, jr_dest] = adjoint(A)[jr_src, ir_src]

The elements `B[ir_dest, jr_dest]` are overwritten. Furthermore, the index range parameters must satisfy `length(ir_dest) == length(jr_src)` and `length(jr_dest) == length(ir_src)`.

source
`LinearAlgebra.copy_transpose!` — Function

    copy_transpose!(B::AbstractVecOrMat, ir_dest::AbstractRange{Int}, jr_dest::AbstractRange{Int},
                    A::AbstractVecOrMat, ir_src::AbstractRange{Int}, jr_src::AbstractRange{Int}) -> B

Efficiently copy elements of matrix `A` to `B` with transposition as follows:

    B[ir_dest, jr_dest] = transpose(A)[jr_src, ir_src]

The elements `B[ir_dest, jr_dest]` are overwritten. Furthermore, the index range parameters must satisfy `length(ir_dest) == length(jr_src)` and `length(jr_dest) == length(ir_src)`.

source

    copy_transpose!(B::AbstractMatrix, ir_dest::AbstractUnitRange, jr_dest::AbstractUnitRange,
                    tM::AbstractChar,
                    M::AbstractVecOrMat, ir_src::AbstractUnitRange, jr_src::AbstractUnitRange) -> B

Efficiently copy elements of matrix `M` to `B` conditioned on the character parameter `tM` as follows:

`tM` | Destination | Source
--- | --- | ---
`'N'` | `B[ir_dest, jr_dest]` | `transpose(M)[jr_src, ir_src]`
`'T'` | `B[ir_dest, jr_dest]` | `M[jr_src, ir_src]`
`'C'` | `B[ir_dest, jr_dest]` | `conj(M)[jr_src, ir_src]`

The elements `B[ir_dest, jr_dest]` are overwritten. Furthermore, the index range parameters must satisfy `length(ir_dest) == length(jr_src)` and `length(jr_dest) == length(ir_src)`.

See also `copyto!` and `copy_adjoint!`.

source

## Low-level matrix operations

In many cases there are in-place versions of matrix operations that allow you to supply a pre-allocated output vector or matrix. This is useful when optimizing critical code in order to avoid the overhead of repeated allocations. These in-place operations are suffixed with `!` below (e.g. `mul!`) according to the usual Julia convention.

`LinearAlgebra.mul!` — Function

    mul!(Y, A, B) -> Y

Calculates the matrix-matrix or matrix-vector product $A B$ and stores the result in `Y`, overwriting the existing value of `Y`. Note that `Y` must not be aliased with either `A` or `B`.

Examples

    julia> A = [1.0 2.0; 3.0 4.0]; B = [1.0 1.0; 1.0 1.0]; Y = similar(B);

    julia> mul!(Y, A, B) === Y
    true

    julia> Y
    2×2 Matrix{Float64}:
     3.0  3.0
     7.0  7.0

    julia> Y == A * B
    true

Implementation

For custom matrix and vector types, it is recommended to implement 5-argument `mul!` rather than implementing 3-argument `mul!` directly if possible.

source

    mul!(C, A, B, α, β) -> C

Combined inplace matrix-matrix or matrix-vector multiply-add $A B α + C β$. The result is stored in `C` by overwriting it. Note that `C` must not be aliased with either `A` or `B`.

Julia 1.3

Five-argument `mul!` requires at least Julia 1.3.

Examples

    julia> A = [1.0 2.0; 3.0 4.0]; B = [1.0 1.0; 1.0 1.0]; C = [1.0 2.0; 3.0 4.0];

    julia> α, β = 100.0, 10.0;

    julia> mul!(C, A, B, α, β) === C
    true

    julia> C
    2×2 Matrix{Float64}:
     310.0  320.0
     730.0  740.0

    julia> C_original = [1.0 2.0; 3.0 4.0]; # A copy of the original value of C

    julia> C == A * B * α + C_original * β
    true

source
`LinearAlgebra.lmul!` — Function

    lmul!(a::Number, B::AbstractArray)

Scale an array `B` by a scalar `a` overwriting `B` in-place. Use `rmul!` to multiply scalar from right. The scaling operation respects the semantics of the multiplication `*` between `a` and an element of `B`. In particular, this also applies to multiplication involving non-finite numbers such as `NaN` and `±Inf`.

Julia 1.1

Prior to Julia 1.1, `NaN` and `±Inf` entries in `B` were treated inconsistently.

Examples

    julia> B = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> lmul!(2, B)
    2×2 Matrix{Int64}:
     2  4
     6  8

    julia> lmul!(0.0, [Inf])
    1-element Vector{Float64}:
     NaN

source

    lmul!(A, B)

Calculate the matrix-matrix product $AB$, overwriting `B`, and return the result. Here, `A` must be of special matrix type, like, e.g., `Diagonal`, `UpperTriangular` or `LowerTriangular`, or of some orthogonal type, see `QR`.

Examples

    julia> B = [0 1; 1 0];

    julia> A = UpperTriangular([1 2; 0 3]);

    julia> lmul!(A, B);

    julia> B
    2×2 Matrix{Int64}:
     2  1
     3  0

    julia> B = [1.0 2.0; 3.0 4.0];

    julia> F = qr([0 1; -1 0]);

    julia> lmul!(F.Q, B)
    2×2 Matrix{Float64}:
     3.0  4.0
     1.0  2.0

source
`LinearAlgebra.rmul!` — Function

    rmul!(A::AbstractArray, b::Number)

Scale an array `A` by a scalar `b` overwriting `A` in-place. Use `lmul!` to multiply scalar from left. The scaling operation respects the semantics of the multiplication `*` between an element of `A` and `b`. In particular, this also applies to multiplication involving non-finite numbers such as `NaN` and `±Inf`.

Julia 1.1

Prior to Julia 1.1, `NaN` and `±Inf` entries in `A` were treated inconsistently.

Examples

    julia> A = [1 2; 3 4]
    2×2 Matrix{Int64}:
     1  2
     3  4

    julia> rmul!(A, 2)
    2×2 Matrix{Int64}:
     2  4
     6  8

    julia> rmul!([NaN], 0.0)
    1-element Vector{Float64}:
     NaN

source

    rmul!(A, B)

Calculate the matrix-matrix product $AB$, overwriting `A`, and return the result. Here, `B` must be of special matrix type, like, e.g., `Diagonal`, `UpperTriangular` or `LowerTriangular`, or of some orthogonal type, see `QR`.

Examples

    julia> A = [0 1; 1 0];

    julia> B = UpperTriangular([1 2; 0 3]);

    julia> rmul!(A, B);

    julia> A
    2×2 Matrix{Int64}:
     0  3
     1  2

    julia> A = [1.0 2.0; 3.0 4.0];

    julia> F = qr([0 1; -1 0]);

    julia> rmul!(A, F.Q)
    2×2 Matrix{Float64}:
     2.0  1.0
     4.0  3.0

source
`LinearAlgebra.ldiv!` — Function

    ldiv!(Y, A, B) -> Y

Compute `A \ B` in-place and store the result in `Y`, returning the result.

The argument `A` should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by `factorize` or `cholesky`). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done in-place via, e.g., `lu!`), and performance-critical situations requiring `ldiv!` usually also require fine-grained control over the factorization of `A`.

Note

Certain structured matrix types, such as `Diagonal` and `UpperTriangular`, are permitted, as these are already in a factorized form

Examples

    julia> A = [1 2.2 4; 3.1 0.2 3; 4 1 2];

    julia> B = [1, 2.5, 3];

    julia> Y = similar(B); # use similar since there is no need to read from it

    julia> ldiv!(Y, qr(A), B); # you may also try qr!(A) to further reduce allocation

    julia> Y ≈ A \ B
    true

source

    ldiv!(A, B)

Compute `A \ B` in-place and overwriting `B` to store the result.

The argument `A` should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by `factorize` or `cholesky`). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done in-place via, e.g., `lu!`), and performance-critical situations requiring `ldiv!` usually also require fine-grained control over the factorization of `A`.

Note

Certain structured matrix types, such as `Diagonal` and `UpperTriangular`, are permitted, as these are already in a factorized form

Examples

    julia> A = [1 2.2 4; 3.1 0.2 3; 4 1 2];

    julia> B = [1, 2.5, 3];

    julia> B0 = copy(B); # a backup copy to facilitate testing

    julia> ldiv!(lu(A), B); # you may also try lu!(A) to further reduce allocation

    julia> B ≈ A \ B0
    true

source

    ldiv!(a::Number, B::AbstractArray)

Divide each entry in an array `B` by a scalar `a` overwriting `B` in-place. Use `rdiv!` to divide scalar from right.

Examples

    julia> B = [1.0 2.0; 3.0 4.0]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> ldiv!(2.0, B)
    2×2 Matrix{Float64}:
     0.5  1.0
     1.5  2.0

source

    ldiv!(A::Tridiagonal, B::AbstractVecOrMat) -> B

Compute `A \ B` in-place by Gaussian elimination with partial pivoting and store the result in `B`, returning the result. In the process, the diagonals of `A` are overwritten as well.

Julia 1.11

`ldiv!` for `Tridiagonal` left-hand sides requires at least Julia 1.11.

source
`LinearAlgebra.rdiv!` — Function

    rdiv!(A, B)

Compute `A / B` in-place and overwriting `A` to store the result.

The argument `B` should not be a matrix. Rather, instead of matrices it should be a factorization object (e.g. produced by `factorize` or `cholesky`). The reason for this is that factorization itself is both expensive and typically allocates memory (although it can also be done in-place via, e.g., `lu!`), and performance-critical situations requiring `rdiv!` usually also require fine-grained control over the factorization of `B`.

Note

Certain structured matrix types, such as `Diagonal` and `UpperTriangular`, are permitted, as these are already in a factorized form

source

    rdiv!(A::AbstractArray, b::Number)

Divide each entry in an array `A` by a scalar `b` overwriting `A` in-place. Use `ldiv!` to divide scalar from left.

Examples

    julia> A = [1.0 2.0; 3.0 4.0]
    2×2 Matrix{Float64}:
     1.0  2.0
     3.0  4.0

    julia> rdiv!(A, 2.0)
    2×2 Matrix{Float64}:
     0.5  1.0
     1.5  2.0

source

## BLAS functions

In Julia (as in much of scientific computation), dense linear-algebra operations are based on the LAPACK library, which in turn is built on top of basic linear-algebra building-blocks known as the BLAS. There are highly optimized implementations of BLAS available for every computer architecture, and sometimes in high-performance linear algebra routines it is useful to call the BLAS functions directly.

`LinearAlgebra.BLAS` provides wrappers for some of the BLAS functions. Those BLAS functions that overwrite one of the input arrays have names ending in `'!'`. Usually, a BLAS function has four methods defined, for `Float32`, `Float64`, `ComplexF32`, and `ComplexF64` arrays.

### BLAS character arguments

Many BLAS functions accept arguments that determine whether to transpose an argument (`trans`), which triangle of a matrix to reference (`uplo` or `ul`), whether the diagonal of a triangular matrix can be assumed to be all ones (`dA`) or which side of a matrix multiplication the input argument belongs on (`side`). The possibilities are:

#### Multiplication order

`side` | Meaning
--- | ---
`'L'` | The argument goes on the left side of a matrix-matrix operation.
`'R'` | The argument goes on the right side of a matrix-matrix operation.

#### Triangle referencing

`uplo`/`ul` | Meaning
--- | ---
`'U'` | Only the upper triangle of the matrix will be used.
`'L'` | Only the lower triangle of the matrix will be used.

#### Transposition operation

`trans`/`tX` | Meaning
--- | ---
`'N'` | The input matrix `X` is not transposed or conjugated.
`'T'` | The input matrix `X` will be transposed.
`'C'` | The input matrix `X` will be conjugated and transposed.

#### Unit diagonal

`diag`/`dX` | Meaning
--- | ---
`'N'` | The diagonal values of the matrix `X` will be read.
`'U'` | The diagonal of the matrix `X` is assumed to be all ones.

`LinearAlgebra.BLAS` — Module

Interface to BLAS subroutines.

source
`LinearAlgebra.BLAS.set_num_threads` — Function

    set_num_threads(n::Integer)
    set_num_threads(::Nothing)

Set the number of threads the BLAS library should use equal to `n::Integer`.

Also accepts `nothing`, in which case julia tries to guess the default number of threads. Passing `nothing` is discouraged and mainly exists for historical reasons.

source
`LinearAlgebra.BLAS.get_num_threads` — Function

    get_num_threads()

Get the number of threads the BLAS library is using.

Julia 1.6

`get_num_threads` requires at least Julia 1.6.

source

BLAS functions can be divided into three groups, also called three levels, depending on when they were first proposed, the type of input parameters, and the complexity of the operation.

### Level 1 BLAS functions

The level 1 BLAS functions were first proposed in (Lawson, 1979) and define operations between scalars and vectors.

`LinearAlgebra.BLAS.rot!` — Function

    rot!(n, X, incx, Y, incy, c, s)

Overwrite `X` with `c*X + s*Y` and `Y` with `-conj(s)*X + c*Y` for the first `n` elements of array `X` with stride `incx` and first `n` elements of array `Y` with stride `incy`. Returns `X` and `Y`.

Julia 1.5

`rot!` requires at least Julia 1.5.

source
`LinearAlgebra.BLAS.scal!` — Function

    scal!(n, a, X, incx)
    scal!(a, X)

Overwrite `X` with `a*X` for the first `n` elements of array `X` with stride `incx`. Returns `X`.

If `n` and `incx` are not provided, `length(X)` and `stride(X,1)` are used.

source
`LinearAlgebra.BLAS.scal` — Function

    scal(n, a, X, incx)
    scal(a, X)

Return `X` scaled by `a` for the first `n` elements of array `X` with stride `incx`.

If `n` and `incx` are not provided, `length(X)` and `stride(X,1)` are used.

source
`LinearAlgebra.BLAS.blascopy!` — Function

    blascopy!(n, X, incx, Y, incy)

Copy `n` elements of array `X` with stride `incx` to array `Y` with stride `incy`. Returns `Y`.

source
`LinearAlgebra.BLAS.dot` — Function

    dot(n, X, incx, Y, incy)

Dot product of two vectors consisting of `n` elements of array `X` with stride `incx` and `n` elements of array `Y` with stride `incy`.

Examples

    julia> BLAS.dot(10, fill(1.0, 10), 1, fill(1.0, 20), 2)
    10.0

source
`LinearAlgebra.BLAS.dotu` — Function

    dotu(n, X, incx, Y, incy)

Dot function for two complex vectors consisting of `n` elements of array `X` with stride `incx` and `n` elements of array `Y` with stride `incy`.

Examples

    julia> BLAS.dotu(10, fill(1.0im, 10), 1, fill(1.0+im, 20), 2)
    -10.0 + 10.0im

source
`LinearAlgebra.BLAS.dotc` — Function

    dotc(n, X, incx, U, incy)

Dot function for two complex vectors, consisting of `n` elements of array `X` with stride `incx` and `n` elements of array `U` with stride `incy`, conjugating the first vector.

Examples

    julia> BLAS.dotc(10, fill(1.0im, 10), 1, fill(1.0+im, 20), 2)
    10.0 - 10.0im

source
`LinearAlgebra.BLAS.nrm2` — Function

    nrm2(n, X, incx)

2-norm of a vector consisting of `n` elements of array `X` with stride `incx`.

Examples

    julia> BLAS.nrm2(4, fill(1.0, 8), 2)
    2.0

    julia> BLAS.nrm2(1, fill(1.0, 8), 2)
    1.0

source
`LinearAlgebra.BLAS.asum` — Function

    asum(n, X, incx)

Sum of the magnitudes of the first `n` elements of array `X` with stride `incx`.

For a real array, the magnitude is the absolute value. For a complex array, the magnitude is the sum of the absolute value of the real part and the absolute value of the imaginary part.

Examples

    julia> BLAS.asum(5, fill(1.0im, 10), 2)
    5.0

    julia> BLAS.asum(2, fill(1.0im, 10), 5)
    2.0

source
`LinearAlgebra.BLAS.iamax` — Function

    iamax(n, dx, incx)
    iamax(dx)

Find the index of the element of `dx` with the maximum absolute value. `n` is the length of `dx`, and `incx` is the stride. If `n` and `incx` are not provided, they assume default values of `n=length(dx)` and `incx=stride1(dx)`.

source

### Level 2 BLAS functions

The level 2 BLAS functions were published in (Dongarra, 1988) and define matrix-vector operations.

return a vector

`LinearAlgebra.BLAS.gemv!` — Function

    gemv!(tA, alpha, A, x, beta, y)

Update the vector `y` as `alpha*A*x + beta*y` or `alpha*A'x + beta*y` according to `tA`. `alpha` and `beta` are scalars. Return the updated `y`.

source
`LinearAlgebra.BLAS.gemv` — Method

    gemv(tA, alpha, A, x)

Return `alpha*A*x` or `alpha*A'x` according to `tA`. `alpha` is a scalar.

source
`LinearAlgebra.BLAS.gemv` — Method

    gemv(tA, A, x)

Return `A*x` or `A'x` according to `tA`.

source
`LinearAlgebra.BLAS.gbmv!` — Function

    gbmv!(trans, m, kl, ku, alpha, A, x, beta, y)

Update vector `y` as `alpha*A*x + beta*y` or `alpha*A'*x + beta*y` according to `trans`. The matrix `A` is a general band matrix of dimension `m` by `size(A,2)` with `kl` sub-diagonals and `ku` super-diagonals. `alpha` and `beta` are scalars. Return the updated `y`.

source
`LinearAlgebra.BLAS.gbmv` — Function

    gbmv(trans, m, kl, ku, alpha, A, x)

Return `alpha*A*x` or `alpha*A'*x` according to `trans`. The matrix `A` is a general band matrix of dimension `m` by `size(A,2)` with `kl` sub-diagonals and `ku` super-diagonals, and `alpha` is a scalar.

source
`LinearAlgebra.BLAS.hemv!` — Function

    hemv!(ul, alpha, A, x, beta, y)

Update the vector `y` as `alpha*A*x + beta*y`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used. `alpha` and `beta` are scalars. Return the updated `y`.

source
`LinearAlgebra.BLAS.hemv` — Method

    hemv(ul, alpha, A, x)

Return `alpha*A*x`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used. `alpha` is a scalar.

source
`LinearAlgebra.BLAS.hemv` — Method

    hemv(ul, A, x)

Return `A*x`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.hpmv!` — Function

    hpmv!(uplo, α, AP, x, β, y)

Update vector `y` as `α*A*x + β*y`, where `A` is a Hermitian matrix provided in packed format `AP`.

With `uplo = 'U'`, the array AP must contain the upper triangular part of the Hermitian matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[1, 2]` and `A[2, 2]` respectively, and so on.

With `uplo = 'L'`, the array AP must contain the lower triangular part of the Hermitian matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[2, 1]` and `A[3, 1]` respectively, and so on.

The scalar inputs `α` and `β` must be complex or real numbers.

The array inputs `x`, `y` and `AP` must all be of `ComplexF32` or `ComplexF64` type.

Return the updated `y`.

Julia 1.5

`hpmv!` requires at least Julia 1.5.

source
`LinearAlgebra.BLAS.symv!` — Function

    symv!(ul, alpha, A, x, beta, y)

Update the vector `y` as `alpha*A*x + beta*y`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used. `alpha` and `beta` are scalars. Return the updated `y`.

source
`LinearAlgebra.BLAS.symv` — Method

    symv(ul, alpha, A, x)

Return `alpha*A*x`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used. `alpha` is a scalar.

source
`LinearAlgebra.BLAS.symv` — Method

    symv(ul, A, x)

Return `A*x`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.sbmv!` — Function

    sbmv!(uplo, k, alpha, A, x, beta, y)

Update vector `y` as `alpha*A*x + beta*y` where `A` is a symmetric band matrix of order `size(A,2)` with `k` super-diagonals stored in the argument `A`. The storage layout for `A` is described the reference BLAS module, level-2 BLAS at https://www.netlib.org/lapack/explore-html/. Only the `uplo` triangle of `A` is used.

Return the updated `y`.

source
`LinearAlgebra.BLAS.sbmv` — Method

    sbmv(uplo, k, alpha, A, x)

Return `alpha*A*x` where `A` is a symmetric band matrix of order `size(A,2)` with `k` super-diagonals stored in the argument `A`. Only the `uplo` triangle of `A` is used.

source
`LinearAlgebra.BLAS.sbmv` — Method

    sbmv(uplo, k, A, x)

Return `A*x` where `A` is a symmetric band matrix of order `size(A,2)` with `k` super-diagonals stored in the argument `A`. Only the `uplo` triangle of `A` is used.

source
`LinearAlgebra.BLAS.spmv!` — Function

    spmv!(uplo, α, AP, x, β, y)

Update vector `y` as `α*A*x + β*y`, where `A` is a symmetric matrix provided in packed format `AP`.

With `uplo = 'U'`, the array AP must contain the upper triangular part of the symmetric matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[1, 2]` and `A[2, 2]` respectively, and so on.

With `uplo = 'L'`, the array AP must contain the lower triangular part of the symmetric matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[2, 1]` and `A[3, 1]` respectively, and so on.

The scalar inputs `α` and `β` must be real.

The array inputs `x`, `y` and `AP` must all be of `Float32` or `Float64` type.

Return the updated `y`.

Julia 1.5

`spmv!` requires at least Julia 1.5.

source
`LinearAlgebra.BLAS.trmv!` — Function

    trmv!(ul, tA, dA, A, b)

Return `op(A)*b`, where `op` is determined by `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones. The multiplication occurs in-place on `b`.

source
`LinearAlgebra.BLAS.trmv` — Function

    trmv(ul, tA, dA, A, b)

Return `op(A)*b`, where `op` is determined by `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones.

source
`LinearAlgebra.BLAS.trsv!` — Function

    trsv!(ul, tA, dA, A, b)

Overwrite `b` with the solution to `A*x = b` or one of the other two variants determined by `tA` and `ul`. `dA` determines if the diagonal values are read or are assumed to be all ones. Return the updated `b`.

source
`LinearAlgebra.BLAS.trsv` — Function

    trsv(ul, tA, dA, A, b)

Return the solution to `A*x = b` or one of the other two variants determined by `tA` and `ul`. `dA` determines if the diagonal values are read or are assumed to be all ones.

source

return a matrix

`LinearAlgebra.BLAS.ger!` — Function

    ger!(alpha, x, y, A)

Rank-1 update of the matrix `A` with vectors `x` and `y` as `alpha*x*y' + A`.

source
`LinearAlgebra.BLAS.geru!` — Function

    geru!(alpha, x, y, A)

Rank-1 update of the matrix `A` with vectors `x` and `y` as `alpha*x*transpose(y) + A`.

source
`LinearAlgebra.BLAS.her!` — Function

    her!(uplo, alpha, x, A)

Methods for complex arrays only. Rank-1 update of the Hermitian matrix `A` with vector `x` as `alpha*x*x' + A`. `uplo` controls which triangle of `A` is updated. Returns `A`.

source
`LinearAlgebra.BLAS.syr!` — Function

    syr!(uplo, alpha, x, A)

Rank-1 update of the symmetric matrix `A` with vector `x` as `alpha*x*transpose(x) + A`. `uplo` controls which triangle of `A` is updated. Returns `A`.

source
`LinearAlgebra.BLAS.spr!` — Function

    spr!(uplo, α, x, AP)

Update matrix `A` as `A+α*x*x'`, where `A` is a symmetric matrix provided in packed format `AP` and `x` is a vector.

With `uplo = 'U'`, the array AP must contain the upper triangular part of the symmetric matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[1, 2]` and `A[2, 2]` respectively, and so on.

With `uplo = 'L'`, the array AP must contain the lower triangular part of the symmetric matrix packed sequentially, column by column, so that `AP[1]` contains `A[1, 1]`, `AP[2]` and `AP[3]` contain `A[2, 1]` and `A[3, 1]` respectively, and so on.

The scalar input `α` must be real.

The array inputs `x` and `AP` must all be of `Float32` or `Float64` type. Return the updated `AP`.

Julia 1.8

`spr!` requires at least Julia 1.8.

source

### Level 3 BLAS functions

The level 3 BLAS functions were published in (Dongarra, 1990) and define matrix-matrix operations.

`LinearAlgebra.BLAS.gemmt!` — Function

    gemmt!(uplo, tA, tB, alpha, A, B, beta, C)

Update the lower or upper triangular part specified by `uplo` of `C` as `alpha*A*B + beta*C` or the other variants according to `tA` and `tB`. Return the updated `C`.

Julia 1.11

`gemmt!` requires at least Julia 1.11.

source
`LinearAlgebra.BLAS.gemmt` — Method

    gemmt(uplo, tA, tB, alpha, A, B)

Return the lower or upper triangular part specified by `uplo` of `A*B` or the other three variants according to `tA` and `tB`.

Julia 1.11

`gemmt` requires at least Julia 1.11.

source
`LinearAlgebra.BLAS.gemmt` — Method

    gemmt(uplo, tA, tB, A, B)

Return the lower or upper triangular part specified by `uplo` of `A*B` or the other three variants according to `tA` and `tB`.

Julia 1.11

`gemmt` requires at least Julia 1.11.

source
`LinearAlgebra.BLAS.gemm!` — Function

    gemm!(tA, tB, alpha, A, B, beta, C)

Update `C` as `alpha*A*B + beta*C` or the other three variants according to `tA` and `tB`. Return the updated `C`.

source
`LinearAlgebra.BLAS.gemm` — Method

    gemm(tA, tB, alpha, A, B)

Return `alpha*A*B` or the other three variants according to `tA` and `tB`.

source
`LinearAlgebra.BLAS.gemm` — Method

    gemm(tA, tB, A, B)

Return `A*B` or the other three variants according to `tA` and `tB`.

source
`LinearAlgebra.BLAS.symm!` — Function

    symm!(side, ul, alpha, A, B, beta, C)

Update `C` as `alpha*A*B + beta*C` or `alpha*B*A + beta*C` according to `side`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used. Return the updated `C`.

source
`LinearAlgebra.BLAS.symm` — Method

    symm(side, ul, alpha, A, B)

Return `alpha*A*B` or `alpha*B*A` according to `side`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.symm` — Method

    symm(side, ul, A, B)

Return `A*B` or `B*A` according to `side`. `A` is assumed to be symmetric. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.hemm!` — Function

    hemm!(side, ul, alpha, A, B, beta, C)

Update `C` as `alpha*A*B + beta*C` or `alpha*B*A + beta*C` according to `side`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used. Return the updated `C`.

source
`LinearAlgebra.BLAS.hemm` — Method

    hemm(side, ul, alpha, A, B)

Return `alpha*A*B` or `alpha*B*A` according to `side`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.hemm` — Method

    hemm(side, ul, A, B)

Return `A*B` or `B*A` according to `side`. `A` is assumed to be Hermitian. Only the `ul` triangle of `A` is used.

source
`LinearAlgebra.BLAS.syrk!` — Function

    syrk!(uplo, trans, alpha, A, beta, C)

Rank-k update of the symmetric matrix `C` as `alpha*A*transpose(A) + beta*C` or `alpha*transpose(A)*A + beta*C` according to `trans`. Only the `uplo` triangle of `C` is used. Return `C`.

source
`LinearAlgebra.BLAS.syrk` — Function

    syrk(uplo, trans, alpha, A)

Return either the upper triangle or the lower triangle of `A`, according to `uplo`, of `alpha*A*transpose(A)` or `alpha*transpose(A)*A`, according to `trans`.

source
`LinearAlgebra.BLAS.herk!` — Function

    herk!(uplo, trans, alpha, A, beta, C)

Methods for complex arrays only. Rank-k update of the Hermitian matrix `C` as `alpha*A*A' + beta*C` or `alpha*A'*A + beta*C` according to `trans`. Only the `uplo` triangle of `C` is updated. Returns `C`.

source
`LinearAlgebra.BLAS.herk` — Function

    herk(uplo, trans, alpha, A)

Methods for complex arrays only. Returns the `uplo` triangle of `alpha*A*A'` or `alpha*A'*A`, according to `trans`.

source
`LinearAlgebra.BLAS.syr2k!` — Function

    syr2k!(uplo, trans, alpha, A, B, beta, C)

Rank-2k update of the symmetric matrix `C` as `alpha*A*transpose(B) + alpha*B*transpose(A) + beta*C` or `alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C` according to `trans`. Only the `uplo` triangle of `C` is used. Returns `C`.

source
`LinearAlgebra.BLAS.syr2k` — Function

    syr2k(uplo, trans, alpha, A, B)

Returns the `uplo` triangle of `alpha*A*transpose(B) + alpha*B*transpose(A)` or `alpha*transpose(A)*B + alpha*transpose(B)*A`, according to `trans`.

source

    syr2k(uplo, trans, A, B)

Return the `uplo` triangle of `A*transpose(B) + B*transpose(A)` or `transpose(A)*B + transpose(B)*A`, according to `trans`.

source
`LinearAlgebra.BLAS.her2k!` — Function

    her2k!(uplo, trans, alpha, A, B, beta, C)

Rank-2k update of the Hermitian matrix `C` as `alpha*A*B' + alpha*B*A' + beta*C` or `alpha*A'*B + alpha*B'*A + beta*C` according to `trans`. The scalar `beta` has to be real. Only the `uplo` triangle of `C` is used. Return `C`.

source
`LinearAlgebra.BLAS.her2k` — Function

    her2k(uplo, trans, alpha, A, B)

Return the `uplo` triangle of `alpha*A*B' + alpha*B*A'` or `alpha*A'*B + alpha*B'*A`, according to `trans`.

source

    her2k(uplo, trans, A, B)

Return the `uplo` triangle of `A*B' + B*A'` or `A'*B + B'*A`, according to `trans`.

source
`LinearAlgebra.BLAS.trmm!` — Function

    trmm!(side, ul, tA, dA, alpha, A, B)

Update `B` as `alpha*A*B` or one of the other three variants determined by `side` and `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones. Return the updated `B`.

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`LinearAlgebra.BLAS.trmm` — Function

    trmm(side, ul, tA, dA, alpha, A, B)

Return `alpha*A*B` or one of the other three variants determined by `side` and `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones.

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`LinearAlgebra.BLAS.trsm!` — Function

    trsm!(side, ul, tA, dA, alpha, A, B)

Overwrite `B` with the solution to `A*X = alpha*B` or one of the other three variants determined by `side` and `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones. Returns the updated `B`.

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`LinearAlgebra.BLAS.trsm` — Function

    trsm(side, ul, tA, dA, alpha, A, B)

Return the solution to `A*X = alpha*B` or one of the other three variants determined by determined by `side` and `tA`. Only the `ul` triangle of `A` is used. `dA` determines if the diagonal values are read or are assumed to be all ones.

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## LAPACK functions

`LinearAlgebra.LAPACK` provides wrappers for some of the LAPACK functions for linear algebra. Those functions that overwrite one of the input arrays have names ending in `'!'`.

Usually a function has 4 methods defined, one each for `Float64`, `Float32`, `ComplexF64` and `ComplexF32` arrays.

Note that the LAPACK API provided by Julia can and will change in the future. Since this API is not user-facing, there is no commitment to support/deprecate this specific set of functions in future releases.

`LinearAlgebra.LAPACK` — Module

Interfaces to LAPACK subroutines.

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`LinearAlgebra.LAPACK.gbtrf!` — Function

    gbtrf!(kl, ku, m, AB) -> (AB, ipiv)

Compute the LU factorization of a banded matrix `AB`. `kl` is the first subdiagonal containing a nonzero band, `ku` is the last superdiagonal containing one, and `m` is the first dimension of the matrix `AB`. Returns the LU factorization in-place and `ipiv`, the vector of pivots used.

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`LinearAlgebra.LAPACK.gbtrs!` — Function

    gbtrs!(trans, kl, ku, m, AB, ipiv, B)

Solve the equation `AB * X = B`. `trans` determines the orientation of `AB`. It may be `N` (no transpose), `T` (transpose), or `C` (conjugate transpose). `kl` is the first subdiagonal containing a nonzero band, `ku` is the last superdiagonal containing one, and `m` is the first dimension of the matrix `AB`. `ipiv` is the vector of pivots returned from `gbtrf!`. Returns the vector or matrix `X`, overwriting `B` in-place.

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`LinearAlgebra.LAPACK.gebal!` — Function

    gebal!(job, A) -> (ilo, ihi, scale)

Balance the matrix `A` before computing its eigensystem or Schur factorization. `job` can be one of `N` (`A` will not be permuted or scaled), `P` (`A` will only be permuted), `S` (`A` will only be scaled), or `B` (`A` will be both permuted and scaled). Modifies `A` in-place and returns `ilo`, `ihi`, and `scale`. If permuting was turned on, `A[i,j] = 0` if `j > i` and `1 < j < ilo` or `j > ihi`. `scale` contains information about the scaling/permutations performed.

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`LinearAlgebra.LAPACK.gebak!` — Function

    gebak!(job, side, ilo, ihi, scale, V)

Transform the eigenvectors `V` of a matrix balanced using `gebal!` to the unscaled/unpermuted eigenvectors of the original matrix. Modifies `V` in-place. `side` can be `L` (left eigenvectors are transformed) or `R` (right eigenvectors are transformed).

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`LinearAlgebra.LAPACK.gebrd!` — Function

    gebrd!(A) -> (A, d, e, tauq, taup)

Reduce `A` in-place to bidiagonal form `A = QBP'`. Returns `A`, containing the bidiagonal matrix `B`; `d`, containing the diagonal elements of `B`; `e`, containing the off-diagonal elements of `B`; `tauq`, containing the elementary reflectors representing `Q`; and `taup`, containing the elementary reflectors representing `P`.

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`LinearAlgebra.LAPACK.gelqf!` — Function

    gelqf!(A, tau)

Compute the `LQ` factorization of `A`, `A = LQ`. `tau` contains scalars which parameterize the elementary reflectors of the factorization. `tau` must have length greater than or equal to the smallest dimension of `A`.

Returns `A` and `tau` modified in-place.

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    gelqf!(A) -> (A, tau)

Compute the `LQ` factorization of `A`, `A = LQ`.

Returns `A`, modified in-place, and `tau`, which contains scalars which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.geqlf!` — Function

    geqlf!(A, tau)

Compute the `QL` factorization of `A`, `A = QL`. `tau` contains scalars which parameterize the elementary reflectors of the factorization. `tau` must have length greater than or equal to the smallest dimension of `A`.

Returns `A` and `tau` modified in-place.

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    geqlf!(A) -> (A, tau)

Compute the `QL` factorization of `A`, `A = QL`.

Returns `A`, modified in-place, and `tau`, which contains scalars which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.geqrf!` — Function

    geqrf!(A, tau)

Compute the `QR` factorization of `A`, `A = QR`. `tau` contains scalars which parameterize the elementary reflectors of the factorization. `tau` must have length greater than or equal to the smallest dimension of `A`.

Returns `A` and `tau` modified in-place.

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    geqrf!(A) -> (A, tau)

Compute the `QR` factorization of `A`, `A = QR`.

Returns `A`, modified in-place, and `tau`, which contains scalars which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.geqp3!` — Function

    geqp3!(A, [jpvt, tau]) -> (A, tau, jpvt)

Compute the pivoted `QR` factorization of `A`, `AP = QR` using BLAS level 3. `P` is a pivoting matrix, represented by `jpvt`. `tau` stores the elementary reflectors. The arguments `jpvt` and `tau` are optional and allow for passing preallocated arrays. When passed, `jpvt` must have length greater than or equal to `n` if `A` is an `(m x n)` matrix and `tau` must have length greater than or equal to the smallest dimension of `A`. On entry, if `jpvt[j]` does not equal zero then the `j`th column of `A` is permuted to the front of `AP`.

`A`, `jpvt`, and `tau` are modified in-place.

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`LinearAlgebra.LAPACK.gerqf!` — Function

    gerqf!(A, tau)

Compute the `RQ` factorization of `A`, `A = RQ`. `tau` contains scalars which parameterize the elementary reflectors of the factorization. `tau` must have length greater than or equal to the smallest dimension of `A`.

Returns `A` and `tau` modified in-place.

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    gerqf!(A) -> (A, tau)

Compute the `RQ` factorization of `A`, `A = RQ`.

Returns `A`, modified in-place, and `tau`, which contains scalars which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.geqrt!` — Function

    geqrt!(A, T)

Compute the blocked `QR` factorization of `A`, `A = QR`. `T` contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization. The first dimension of `T` sets the block size and it must be between 1 and `n`. The second dimension of `T` must equal the smallest dimension of `A`.

Returns `A` and `T` modified in-place.

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    geqrt!(A, nb) -> (A, T)

Compute the blocked `QR` factorization of `A`, `A = QR`. `nb` sets the block size and it must be between 1 and `n`, the second dimension of `A`.

Returns `A`, modified in-place, and `T`, which contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.geqrt3!` — Function

    geqrt3!(A, T)

Recursively computes the blocked `QR` factorization of `A`, `A = QR`. `T` contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization. The first dimension of `T` sets the block size and it must be between 1 and `n`. The second dimension of `T` must equal the smallest dimension of `A`.

Returns `A` and `T` modified in-place.

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    geqrt3!(A) -> (A, T)

Recursively computes the blocked `QR` factorization of `A`, `A = QR`.

Returns `A`, modified in-place, and `T`, which contains upper triangular block reflectors which parameterize the elementary reflectors of the factorization.

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`LinearAlgebra.LAPACK.getrf!` — Function

    getrf!(A, ipiv) -> (A, ipiv, info)

Compute the pivoted `LU` factorization of `A`, `A = LU`. `ipiv` contains the pivoting information and `info` a code which indicates success (`info = 0`), a singular value in `U` (`info = i`, in which case `U[i,i]` is singular), or an error code (`info < 0`).

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    getrf!(A) -> (A, ipiv, info)

Compute the pivoted `LU` factorization of `A`, `A = LU`.

Returns `A`, modified in-place, `ipiv`, the pivoting information, and an `info` code which indicates success (`info = 0`), a singular value in `U` (`info = i`, in which case `U[i,i]` is singular), or an error code (`info < 0`).

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`LinearAlgebra.LAPACK.tzrzf!` — Function

    tzrzf!(A) -> (A, tau)

Transforms the upper trapezoidal matrix `A` to upper triangular form in-place. Returns `A` and `tau`, the scalar parameters for the elementary reflectors of the transformation.

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`LinearAlgebra.LAPACK.ormrz!` — Function

    ormrz!(side, trans, A, tau, C)

Multiplies the matrix `C` by `Q` from the transformation supplied by `tzrzf!`. Depending on `side` or `trans` the multiplication can be left-sided (`side = L, Q*C`) or right-sided (`side = R, C*Q`) and `Q` can be unmodified (`trans = N`), transposed (`trans = T`), or conjugate transposed (`trans = C`). Returns matrix `C` which is modified in-place with the result of the multiplication.

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`LinearAlgebra.LAPACK.gels!` — Function

    gels!(trans, A, B) -> (F, B, ssr)

Solves the linear equation `A * X = B`, `transpose(A) * X = B`, or `adjoint(A) * X = B` using a QR or LQ factorization. Modifies the matrix/vector `B` in place with the solution. `A` is overwritten with its `QR` or `LQ` factorization. `trans` may be one of `N` (no modification), `T` (transpose), or `C` (conjugate transpose). `gels!` searches for the minimum norm/least squares solution. `A` may be under or over determined. The solution is returned in `B`.

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`LinearAlgebra.LAPACK.gesv!` — Function

    gesv!(A, B) -> (B, A, ipiv)

Solves the linear equation `A * X = B` where `A` is a square matrix using the `LU` factorization of `A`. `A` is overwritten with its `LU` factorization and `B` is overwritten with the solution `X`. `ipiv` contains the pivoting information for the `LU` factorization of `A`.

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`LinearAlgebra.LAPACK.getrs!` — Function

    getrs!(trans, A, ipiv, B)

Solves the linear equation `A * X = B`, `transpose(A) * X = B`, or `adjoint(A) * X = B` for square `A`. Modifies the matrix/vector `B` in place with the solution. `A` is the `LU` factorization from `getrf!`, with `ipiv` the pivoting information. `trans` may be one of `N` (no modification), `T` (transpose), or `C` (conjugate transpose).

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`LinearAlgebra.LAPACK.getri!` — Function

    getri!(A, ipiv)

Computes the inverse of `A`, using its `LU` factorization found by `getrf!`. `ipiv` is the pivot information output and `A` contains the `LU` factorization of `getrf!`. `A` is overwritten with its inverse.

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`LinearAlgebra.LAPACK.gesvx!` — Function

    gesvx!(fact, trans, A, AF, ipiv, equed, R, C, B) -> (X, equed, R, C, B, rcond, ferr, berr, work)

Solves the linear equation `A * X = B` (`trans = N`), `transpose(A) * X = B` (`trans = T`), or `adjoint(A) * X = B` (`trans = C`) using the `LU` factorization of `A`. `fact` may be `E`, in which case `A` will be equilibrated and copied to `AF`; `F`, in which case `AF` and `ipiv` from a previous `LU` factorization are inputs; or `N`, in which case `A` will be copied to `AF` and then factored. If `fact = F`, `equed` may be `N`, meaning `A` has not been equilibrated; `R`, meaning `A` was multiplied by `Diagonal(R)` from the left; `C`, meaning `A` was multiplied by `Diagonal(C)` from the right; or `B`, meaning `A` was multiplied by `Diagonal(R)` from the left and `Diagonal(C)` from the right. If `fact = F` and `equed = R` or `B` the elements of `R` must all be positive. If `fact = F` and `equed = C` or `B` the elements of `C` must all be positive.

Returns the solution `X`; `equed`, which is an output if `fact` is not `N`, and describes the equilibration that was performed; `R`, the row equilibration diagonal; `C`, the column equilibration diagonal; `B`, which may be overwritten with its equilibrated form `Diagonal(R)*B` (if `trans = N` and `equed = R,B`) or `Diagonal(C)*B` (if `trans = T,C` and `equed = C,B`); `rcond`, the reciprocal condition number of `A` after equilbrating; `ferr`, the forward error bound for each solution vector in `X`; `berr`, the forward error bound for each solution vector in `X`; and `work`, the reciprocal pivot growth factor.

source

    gesvx!(A, B)

The no-equilibration, no-transpose simplification of `gesvx!`.

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`LinearAlgebra.LAPACK.gelsd!` — Function

    gelsd!(A, B, rcond) -> (B, rnk)

Computes the least norm solution of `A * X = B` by finding the `SVD` factorization of `A`, then dividing-and-conquering the problem. `B` is overwritten with the solution `X`. Singular values below `rcond` will be treated as zero. Returns the solution in `B` and the effective rank of `A` in `rnk`.

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`LinearAlgebra.LAPACK.gelsy!` — Function

    gelsy!(A, B, rcond) -> (B, rnk)

Computes the least norm solution of `A * X = B` by finding the full `QR` factorization of `A`, then dividing-and-conquering the problem. `B` is overwritten with the solution `X`. Singular values below `rcond` will be treated as zero. Returns the solution in `B` and the effective rank of `A` in `rnk`.

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`LinearAlgebra.LAPACK.gglse!` — Function

    gglse!(A, c, B, d) -> (X,res)

Solves the equation `A * x = c` where `x` is subject to the equality constraint `B * x = d`. Uses the formula `||c - A*x||^2 = 0` to solve. Returns `X` and the residual sum-of-squares.

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`LinearAlgebra.LAPACK.geev!` — Function

    geev!(jobvl, jobvr, A) -> (W, VL, VR)

Finds the eigensystem of `A`. If `jobvl = N`, the left eigenvectors of `A` aren't computed. If `jobvr = N`, the right eigenvectors of `A` aren't computed. If `jobvl = V` or `jobvr = V`, the corresponding eigenvectors are computed. Returns the eigenvalues in `W`, the right eigenvectors in `VR`, and the left eigenvectors in `VL`.

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`LinearAlgebra.LAPACK.gesdd!` — Function

    gesdd!(job, A) -> (U, S, VT)

Finds the singular value decomposition of `A`, `A = U * S * V'`, using a divide and conquer approach. If `job = A`, all the columns of `U` and the rows of `V'` are computed. If `job = N`, no columns of `U` or rows of `V'` are computed. If `job = O`, `A` is overwritten with the columns of (thin) `U` and the rows of (thin) `V'`. If `job = S`, the columns of (thin) `U` and the rows of (thin) `V'` are computed and returned separately.

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`LinearAlgebra.LAPACK.gesvd!` — Function

    gesvd!(jobu, jobvt, A) -> (U, S, VT)

Finds the singular value decomposition of `A`, `A = U * S * V'`. If `jobu = A`, all the columns of `U` are computed. If `jobvt = A` all the rows of `V'` are computed. If `jobu = N`, no columns of `U` are computed. If `jobvt = N` no rows of `V'` are computed. If `jobu = O`, `A` is overwritten with the columns of (thin) `U`. If `jobvt = O`, `A` is overwritten with the rows of (thin) `V'`. If `jobu = S`, the columns of (thin) `U` are computed and returned separately. If `jobvt = S` the rows of (thin) `V'` are computed and returned separately. `jobu` and `jobvt` can't both be `O`.

Returns `U`, `S`, and `Vt`, where `S` are the singular values of `A`.

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`LinearAlgebra.LAPACK.ggsvd!` — Function

    ggsvd!(jobu, jobv, jobq, A, B) -> (U, V, Q, alpha, beta, k, l, R)

Finds the generalized singular value decomposition of `A` and `B`, `U'*A*Q = D1*R` and `V'*B*Q = D2*R`. `D1` has `alpha` on its diagonal and `D2` has `beta` on its diagonal. If `jobu = U`, the orthogonal/unitary matrix `U` is computed. If `jobv = V` the orthogonal/unitary matrix `V` is computed. If `jobq = Q`, the orthogonal/unitary matrix `Q` is computed. If `jobu`, `jobv` or `jobq` is `N`, that matrix is not computed. This function is only available in LAPACK versions prior to 3.6.0.

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`LinearAlgebra.LAPACK.ggsvd3!` — Function

    ggsvd3!(jobu, jobv, jobq, A, B) -> (U, V, Q, alpha, beta, k, l, R)

Finds the generalized singular value decomposition of `A` and `B`, `U'*A*Q = D1*R` and `V'*B*Q = D2*R`. `D1` has `alpha` on its diagonal and `D2` has `beta` on its diagonal. If `jobu = U`, the orthogonal/unitary matrix `U` is computed. If `jobv = V` the orthogonal/unitary matrix `V` is computed. If `jobq = Q`, the orthogonal/unitary matrix `Q` is computed. If `jobu`, `jobv`, or `jobq` is `N`, that matrix is not computed. This function requires LAPACK 3.6.0.

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`LinearAlgebra.LAPACK.geevx!` — Function

    geevx!(balanc, jobvl, jobvr, sense, A) -> (A, w, VL, VR, ilo, ihi, scale, abnrm, rconde, rcondv)

Finds the eigensystem of `A` with matrix balancing. If `jobvl = N`, the left eigenvectors of `A` aren't computed. If `jobvr = N`, the right eigenvectors of `A` aren't computed. If `jobvl = V` or `jobvr = V`, the corresponding eigenvectors are computed. If `balanc = N`, no balancing is performed. If `balanc = P`, `A` is permuted but not scaled. If `balanc = S`, `A` is scaled but not permuted. If `balanc = B`, `A` is permuted and scaled. If `sense = N`, no reciprocal condition numbers are computed. If `sense = E`, reciprocal condition numbers are computed for the eigenvalues only. If `sense = V`, reciprocal condition numbers are computed for the right eigenvectors only. If `sense = B`, reciprocal condition numbers are computed for the right eigenvectors and the eigenvectors. If `sense = E,B`, the right and left eigenvectors must be computed.

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`LinearAlgebra.LAPACK.ggev!` — Function

    ggev!(jobvl, jobvr, A, B) -> (alpha, beta, vl, vr)

Finds the generalized eigendecomposition of `A` and `B`. If `jobvl = N`, the left eigenvectors aren't computed. If `jobvr = N`, the right eigenvectors aren't computed. If `jobvl = V` or `jobvr = V`, the corresponding eigenvectors are computed.

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`LinearAlgebra.LAPACK.ggev3!` — Function

    ggev3!(jobvl, jobvr, A, B) -> (alpha, beta, vl, vr)

Finds the generalized eigendecomposition of `A` and `B` using a blocked algorithm. If `jobvl = N`, the left eigenvectors aren't computed. If `jobvr = N`, the right eigenvectors aren't computed. If `jobvl = V` or `jobvr = V`, the corresponding eigenvectors are computed. This function requires LAPACK 3.6.0.

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`LinearAlgebra.LAPACK.gtsv!` — Function

    gtsv!(dl, d, du, B)

Solves the equation `A * X = B` where `A` is a tridiagonal matrix with `dl` on the subdiagonal, `d` on the diagonal, and `du` on the superdiagonal.

Overwrites `B` with the solution `X` and returns it.

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`LinearAlgebra.LAPACK.gttrf!` — Function

    gttrf!(dl, d, du) -> (dl, d, du, du2, ipiv)

Finds the `LU` factorization of a tridiagonal matrix with `dl` on the subdiagonal, `d` on the diagonal, and `du` on the superdiagonal.

Modifies `dl`, `d`, and `du` in-place and returns them and the second superdiagonal `du2` and the pivoting vector `ipiv`.

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`LinearAlgebra.LAPACK.gttrs!` — Function

    gttrs!(trans, dl, d, du, du2, ipiv, B)

Solves the equation `A * X = B` (`trans = N`), `transpose(A) * X = B` (`trans = T`), or `adjoint(A) * X = B` (`trans = C`) using the `LU` factorization computed by `gttrf!`. `B` is overwritten with the solution `X`.

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`LinearAlgebra.LAPACK.orglq!` — Function

    orglq!(A, tau, k = length(tau))

Explicitly finds the matrix `Q` of a `LQ` factorization after calling `gelqf!` on `A`. Uses the output of `gelqf!`. `A` is overwritten by `Q`.

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`LinearAlgebra.LAPACK.orgqr!` — Function

    orgqr!(A, tau, k = length(tau))

Explicitly finds the matrix `Q` of a `QR` factorization after calling `geqrf!` on `A`. Uses the output of `geqrf!`. `A` is overwritten by `Q`.

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`LinearAlgebra.LAPACK.orgql!` — Function

    orgql!(A, tau, k = length(tau))

Explicitly finds the matrix `Q` of a `QL` factorization after calling `geqlf!` on `A`. Uses the output of `geqlf!`. `A` is overwritten by `Q`.

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`LinearAlgebra.LAPACK.orgrq!` — Function

    orgrq!(A, tau, k = length(tau))

Explicitly finds the matrix `Q` of a `RQ` factorization after calling `gerqf!` on `A`. Uses the output of `gerqf!`. `A` is overwritten by `Q`.

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`LinearAlgebra.LAPACK.ormlq!` — Function

    ormlq!(side, trans, A, tau, C)

Computes `Q * C` (`trans = N`), `transpose(Q) * C` (`trans = T`), `adjoint(Q) * C` (`trans = C`) for `side = L` or the equivalent right-sided multiplication for `side = R` using `Q` from a `LQ` factorization of `A` computed using `gelqf!`. `C` is overwritten.

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`LinearAlgebra.LAPACK.ormqr!` — Function

    ormqr!(side, trans, A, tau, C)

Computes `Q * C` (`trans = N`), `transpose(Q) * C` (`trans = T`), `adjoint(Q) * C` (`trans = C`) for `side = L` or the equivalent right-sided multiplication for `side = R` using `Q` from a `QR` factorization of `A` computed using `geqrf!`. `C` is overwritten.

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`LinearAlgebra.LAPACK.ormql!` — Function

    ormql!(side, trans, A, tau, C)

Computes `Q * C` (`trans = N`), `transpose(Q) * C` (`trans = T`), `adjoint(Q) * C` (`trans = C`) for `side = L` or the equivalent right-sided multiplication for `side = R` using `Q` from a `QL` factorization of `A` computed using `geqlf!`. `C` is overwritten.

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`LinearAlgebra.LAPACK.ormrq!` — Function

    ormrq!(side, trans, A, tau, C)

Computes `Q * C` (`trans = N`), `transpose(Q) * C` (`trans = T`), `adjoint(Q) * C` (`trans = C`) for `side = L` or the equivalent right-sided multiplication for `side = R` using `Q` from a `RQ` factorization of `A` computed using `gerqf!`. `C` is overwritten.

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`LinearAlgebra.LAPACK.gemqrt!` — Function

    gemqrt!(side, trans, V, T, C)

Computes `Q * C` (`trans = N`), `transpose(Q) * C` (`trans = T`), `adjoint(Q) * C` (`trans = C`) for `side = L` or the equivalent right-sided multiplication for `side = R` using `Q` from a `QR` factorization of `A` computed using `geqrt!`. `C` is overwritten.

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`LinearAlgebra.LAPACK.posv!` — Function

    posv!(uplo, A, B) -> (A, B)

Finds the solution to `A * X = B` where `A` is a symmetric or Hermitian positive definite matrix. If `uplo = U` the upper Cholesky decomposition of `A` is computed. If `uplo = L` the lower Cholesky decomposition of `A` is computed. `A` is overwritten by its Cholesky decomposition. `B` is overwritten with the solution `X`.

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`LinearAlgebra.LAPACK.potrf!` — Function

    potrf!(uplo, A)

Computes the Cholesky (upper if `uplo = U`, lower if `uplo = L`) decomposition of positive-definite matrix `A`. `A` is overwritten and returned with an info code.

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`LinearAlgebra.LAPACK.potri!` — Function

    potri!(uplo, A)

Computes the inverse of positive-definite matrix `A` after calling `potrf!` to find its (upper if `uplo = U`, lower if `uplo = L`) Cholesky decomposition.

`A` is overwritten by its inverse and returned.

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`LinearAlgebra.LAPACK.potrs!` — Function

    potrs!(uplo, A, B)

Finds the solution to `A * X = B` where `A` is a symmetric or Hermitian positive definite matrix whose Cholesky decomposition was computed by `potrf!`. If `uplo = U` the upper Cholesky decomposition of `A` was computed. If `uplo = L` the lower Cholesky decomposition of `A` was computed. `B` is overwritten with the solution `X`.

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`LinearAlgebra.LAPACK.pstrf!` — Function

    pstrf!(uplo, A, tol) -> (A, piv, rank, info)

Computes the (upper if `uplo = U`, lower if `uplo = L`) pivoted Cholesky decomposition of positive-definite matrix `A` with a user-set tolerance `tol`. `A` is overwritten by its Cholesky decomposition.

Returns `A`, the pivots `piv`, the rank of `A`, and an `info` code. If `info = 0`, the factorization succeeded. If `info = i > 0`, then `A` is indefinite or rank-deficient.

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`LinearAlgebra.LAPACK.ptsv!` — Function

    ptsv!(D, E, B)

Solves `A * X = B` for positive-definite tridiagonal `A`. `D` is the diagonal of `A` and `E` is the off-diagonal. `B` is overwritten with the solution `X` and returned.

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`LinearAlgebra.LAPACK.pttrf!` — Function

    pttrf!(D, E)

Computes the LDLt factorization of a positive-definite tridiagonal matrix with `D` as diagonal and `E` as off-diagonal. `D` and `E` are overwritten and returned.

source
`LinearAlgebra.LAPACK.pttrs!` — Function

    pttrs!(D, E, B)

Solves `A * X = B` for positive-definite tridiagonal `A` with diagonal `D` and off-diagonal `E` after computing `A`'s LDLt factorization using `pttrf!`. `B` is overwritten with the solution `X`.

source
`LinearAlgebra.LAPACK.trtri!` — Function

    trtri!(uplo, diag, A)

Finds the inverse of (upper if `uplo = U`, lower if `uplo = L`) triangular matrix `A`. If `diag = N`, `A` has non-unit diagonal elements. If `diag = U`, all diagonal elements of `A` are one. `A` is overwritten with its inverse.

source
`LinearAlgebra.LAPACK.trtrs!` — Function

    trtrs!(uplo, trans, diag, A, B)

Solves `A * X = B` (`trans = N`), `transpose(A) * X = B` (`trans = T`), or `adjoint(A) * X = B` (`trans = C`) for (upper if `uplo = U`, lower if `uplo = L`) triangular matrix `A`. If `diag = N`, `A` has non-unit diagonal elements. If `diag = U`, all diagonal elements of `A` are one. `B` is overwritten with the solution `X`.

source
`LinearAlgebra.LAPACK.trcon!` — Function

    trcon!(norm, uplo, diag, A)

Finds the reciprocal condition number of (upper if `uplo = U`, lower if `uplo = L`) triangular matrix `A`. If `diag = N`, `A` has non-unit diagonal elements. If `diag = U`, all diagonal elements of `A` are one. If `norm = I`, the condition number is found in the infinity norm. If `norm = O` or `1`, the condition number is found in the one norm.

source
`LinearAlgebra.LAPACK.trevc!` — Function

    trevc!(side, howmny, select, T, VL = similar(T), VR = similar(T))

Finds the eigensystem of an upper triangular matrix `T`. If `side = R`, the right eigenvectors are computed. If `side = L`, the left eigenvectors are computed. If `side = B`, both sets are computed. If `howmny = A`, all eigenvectors are found. If `howmny = B`, all eigenvectors are found and backtransformed using `VL` and `VR`. If `howmny = S`, only the eigenvectors corresponding to the values in `select` are computed.

source
`LinearAlgebra.LAPACK.trrfs!` — Function

    trrfs!(uplo, trans, diag, A, B, X, Ferr, Berr) -> (Ferr, Berr)

Estimates the error in the solution to `A * X = B` (`trans = N`), `transpose(A) * X = B` (`trans = T`), `adjoint(A) * X = B` (`trans = C`) for `side = L`, or the equivalent equations a right-handed `side = R` `X * A` after computing `X` using `trtrs!`. If `uplo = U`, `A` is upper triangular. If `uplo = L`, `A` is lower triangular. If `diag = N`, `A` has non-unit diagonal elements. If `diag = U`, all diagonal elements of `A` are one. `Ferr` and `Berr` are optional inputs. `Ferr` is the forward error and `Berr` is the backward error, each component-wise.

source
`LinearAlgebra.LAPACK.stev!` — Function

    stev!(job, dv, ev) -> (dv, Zmat)

Computes the eigensystem for a symmetric tridiagonal matrix with `dv` as diagonal and `ev` as off-diagonal. If `job = N` only the eigenvalues are found and returned in `dv`. If `job = V` then the eigenvectors are also found and returned in `Zmat`.

source
`LinearAlgebra.LAPACK.stebz!` — Function

    stebz!(range, order, vl, vu, il, iu, abstol, dv, ev) -> (dv, iblock, isplit)

Computes the eigenvalues for a symmetric tridiagonal matrix with `dv` as diagonal and `ev` as off-diagonal. If `range = A`, all the eigenvalues are found. If `range = V`, the eigenvalues in the half-open interval `(vl, vu]` are found. If `range = I`, the eigenvalues with indices between `il` and `iu` are found. If `order = B`, eigvalues are ordered within a block. If `order = E`, they are ordered across all the blocks. `abstol` can be set as a tolerance for convergence.

source
`LinearAlgebra.LAPACK.stegr!` — Function

    stegr!(jobz, range, dv, ev, vl, vu, il, iu) -> (w, Z)

Computes the eigenvalues (`jobz = N`) or eigenvalues and eigenvectors (`jobz = V`) for a symmetric tridiagonal matrix with `dv` as diagonal and `ev` as off-diagonal. If `range = A`, all the eigenvalues are found. If `range = V`, the eigenvalues in the half-open interval `(vl, vu]` are found. If `range = I`, the eigenvalues with indices between `il` and `iu` are found. The eigenvalues are returned in `w` and the eigenvectors in `Z`.

source
`LinearAlgebra.LAPACK.stein!` — Function

    stein!(dv, ev_in, w_in, iblock_in, isplit_in)

Computes the eigenvectors for a symmetric tridiagonal matrix with `dv` as diagonal and `ev_in` as off-diagonal. `w_in` specifies the input eigenvalues for which to find corresponding eigenvectors. `iblock_in` specifies the submatrices corresponding to the eigenvalues in `w_in`. `isplit_in` specifies the splitting points between the submatrix blocks.

source
`LinearAlgebra.LAPACK.syconv!` — Function

    syconv!(uplo, A, ipiv) -> (A, work)

Converts a symmetric matrix `A` (which has been factorized into a triangular matrix) into two matrices `L` and `D`. If `uplo = U`, `A` is upper triangular. If `uplo = L`, it is lower triangular. `ipiv` is the pivot vector from the triangular factorization. `A` is overwritten by `L` and `D`.

source
`LinearAlgebra.LAPACK.sysv!` — Function

    sysv!(uplo, A, B) -> (B, A, ipiv)

Finds the solution to `A * X = B` for symmetric matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `B` is overwritten by the solution `X`. `A` is overwritten by its Bunch-Kaufman factorization. `ipiv` contains pivoting information about the factorization.

source
`LinearAlgebra.LAPACK.sytrf!` — Function

    sytrf!(uplo, A) -> (A, ipiv, info)

Computes the Bunch-Kaufman factorization of a symmetric matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored.

Returns `A`, overwritten by the factorization, a pivot vector `ipiv`, and the error code `info` which is a non-negative integer. If `info` is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position `info`.

source

    sytrf!(uplo, A, ipiv) -> (A, ipiv, info)

Computes the Bunch-Kaufman factorization of a symmetric matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored.

Returns `A`, overwritten by the factorization, the pivot vector `ipiv`, and the error code `info` which is a non-negative integer. If `info` is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position `info`.

source
`LinearAlgebra.LAPACK.sytri!` — Function

    sytri!(uplo, A, ipiv)

Computes the inverse of a symmetric matrix `A` using the results of `sytrf!`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `A` is overwritten by its inverse.

source
`LinearAlgebra.LAPACK.sytrs!` — Function

    sytrs!(uplo, A, ipiv, B)

Solves the equation `A * X = B` for a symmetric matrix `A` using the results of `sytrf!`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `B` is overwritten by the solution `X`.

source
`LinearAlgebra.LAPACK.hesv!` — Function

    hesv!(uplo, A, B) -> (B, A, ipiv)

Finds the solution to `A * X = B` for Hermitian matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `B` is overwritten by the solution `X`. `A` is overwritten by its Bunch-Kaufman factorization. `ipiv` contains pivoting information about the factorization.

source
`LinearAlgebra.LAPACK.hetrf!` — Function

    hetrf!(uplo, A) -> (A, ipiv, info)

Computes the Bunch-Kaufman factorization of a Hermitian matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored.

Returns `A`, overwritten by the factorization, a pivot vector `ipiv`, and the error code `info` which is a non-negative integer. If `info` is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position `info`.

source

    hetrf!(uplo, A, ipiv) -> (A, ipiv, info)

Computes the Bunch-Kaufman factorization of a Hermitian matrix `A`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored.

Returns `A`, overwritten by the factorization, the pivot vector `ipiv`, and the error code `info` which is a non-negative integer. If `info` is positive the matrix is singular and the diagonal part of the factorization is exactly zero at position `info`.

source
`LinearAlgebra.LAPACK.hetri!` — Function

    hetri!(uplo, A, ipiv)

Computes the inverse of a Hermitian matrix `A` using the results of `sytrf!`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `A` is overwritten by its inverse.

source
`LinearAlgebra.LAPACK.hetrs!` — Function

    hetrs!(uplo, A, ipiv, B)

Solves the equation `A * X = B` for a Hermitian matrix `A` using the results of `sytrf!`. If `uplo = U`, the upper half of `A` is stored. If `uplo = L`, the lower half is stored. `B` is overwritten by the solution `X`.

source
`LinearAlgebra.LAPACK.syev!` — Function

    syev!(jobz, uplo, A)

Finds the eigenvalues (`jobz = N`) or eigenvalues and eigenvectors (`jobz = V`) of a symmetric matrix `A`. If `uplo = U`, the upper triangle of `A` is used. If `uplo = L`, the lower triangle of `A` is used.

source
`LinearAlgebra.LAPACK.syevr!` — Function

    syevr!(jobz, range, uplo, A, vl, vu, il, iu, abstol) -> (W, Z)

Finds the eigenvalues (`jobz = N`) or eigenvalues and eigenvectors (`jobz = V`) of a symmetric matrix `A`. If `uplo = U`, the upper triangle of `A` is used. If `uplo = L`, the lower triangle of `A` is used. If `range = A`, all the eigenvalues are found. If `range = V`, the eigenvalues in the half-open interval `(vl, vu]` are found. If `range = I`, the eigenvalues with indices between `il` and `iu` are found. `abstol` can be set as a tolerance for convergence.

The eigenvalues are returned in `W` and the eigenvectors in `Z`.

source
`LinearAlgebra.LAPACK.syevd!` — Function

    syevd!(jobz, uplo, A)

Finds the eigenvalues (`jobz = N`) or eigenvalues and eigenvectors (`jobz = V`) of a symmetric matrix `A`. If `uplo = U`, the upper triangle of `A` is used. If `uplo = L`, the lower triangle of `A` is used.

source
`LinearAlgebra.LAPACK.sygvd!` — Function

    sygvd!(itype, jobz, uplo, A, B) -> (w, A, B)

Finds the generalized eigenvalues (`jobz = N`) or eigenvalues and eigenvectors (`jobz = V`) of a symmetric matrix `A` and symmetric positive-definite matrix `B`. If `uplo = U`, the upper triangles of `A` and `B` are used. If `uplo = L`, the lower triangles of `A` and `B` are used. If `itype = 1`, the problem to solve is `A * x = lambda * B * x`. If `itype = 2`, the problem to solve is `A * B * x = lambda * x`. If `itype = 3`, the problem to solve is `B * A * x = lambda * x`.

source
`LinearAlgebra.LAPACK.bdsqr!` — Function

    bdsqr!(uplo, d, e_, Vt, U, C) -> (d, Vt, U, C)

Computes the singular value decomposition of a bidiagonal matrix with `d` on the diagonal and `e_` on the off-diagonal. If `uplo = U`, `e_` is the superdiagonal. If `uplo = L`, `e_` is the subdiagonal. Can optionally also compute the product `Q' * C`.

Returns the singular values in `d`, and the matrix `C` overwritten with `Q' * C`.

source
`LinearAlgebra.LAPACK.bdsdc!` — Function

    bdsdc!(uplo, compq, d, e_) -> (d, e, u, vt, q, iq)

Computes the singular value decomposition of a bidiagonal matrix with `d` on the diagonal and `e_` on the off-diagonal using a divide and conqueq method. If `uplo = U`, `e_` is the superdiagonal. If `uplo = L`, `e_` is the subdiagonal. If `compq = N`, only the singular values are found. If `compq = I`, the singular values and vectors are found. If `compq = P`, the singular values and vectors are found in compact form. Only works for real types.

Returns the singular values in `d`, and if `compq = P`, the compact singular vectors in `iq`.

source
`LinearAlgebra.LAPACK.gecon!` — Function

    gecon!(normtype, A, anorm)

Finds the reciprocal condition number of matrix `A`. If `normtype = I`, the condition number is found in the infinity norm. If `normtype = O` or `1`, the condition number is found in the one norm. `A` must be the result of `getrf!` and `anorm` is the norm of `A` in the relevant norm.

source
`LinearAlgebra.LAPACK.gehrd!` — Function

    gehrd!(ilo, ihi, A) -> (A, tau)

Converts a matrix `A` to Hessenberg form. If `A` is balanced with `gebal!` then `ilo` and `ihi` are the outputs of `gebal!`. Otherwise they should be `ilo = 1` and `ihi = size(A,2)`. `tau` contains the elementary reflectors of the factorization.

source
`LinearAlgebra.LAPACK.orghr!` — Function

    orghr!(ilo, ihi, A, tau)

Explicitly finds `Q`, the orthogonal/unitary matrix from `gehrd!`. `ilo`, `ihi`, `A`, and `tau` must correspond to the input/output to `gehrd!`.

source
`LinearAlgebra.LAPACK.gees!` — Function

    gees!(jobvs, A) -> (A, vs, w)

Computes the eigenvalues (`jobvs = N`) or the eigenvalues and Schur vectors (`jobvs = V`) of matrix `A`. `A` is overwritten by its Schur form.

Returns `A`, `vs` containing the Schur vectors, and `w`, containing the eigenvalues.

source
`LinearAlgebra.LAPACK.gges!` — Function

    gges!(jobvsl, jobvsr, A, B) -> (A, B, alpha, beta, vsl, vsr)

Computes the generalized eigenvalues, generalized Schur form, left Schur vectors (`jobsvl = V`), or right Schur vectors (`jobvsr = V`) of `A` and `B`.

The generalized eigenvalues are returned in `alpha` and `beta`. The left Schur vectors are returned in `vsl` and the right Schur vectors are returned in `vsr`.

source
`LinearAlgebra.LAPACK.gges3!` — Function

    gges3!(jobvsl, jobvsr, A, B) -> (A, B, alpha, beta, vsl, vsr)

Computes the generalized eigenvalues, generalized Schur form, left Schur vectors (`jobsvl = V`), or right Schur vectors (`jobvsr = V`) of `A` and `B` using a blocked algorithm. This function requires LAPACK 3.6.0.

The generalized eigenvalues are returned in `alpha` and `beta`. The left Schur vectors are returned in `vsl` and the right Schur vectors are returned in `vsr`.

source
`LinearAlgebra.LAPACK.trexc!` — Function

    trexc!(compq, ifst, ilst, T, Q) -> (T, Q)
    trexc!(ifst, ilst, T, Q) -> (T, Q)

Reorder the Schur factorization `T` of a matrix, such that the diagonal block of `T` with row index `ifst` is moved to row index `ilst`. If `compq = V`, the Schur vectors `Q` are reordered. If `compq = N` they are not modified. The 4-arg method calls the 5-arg method with `compq = V`.

source
`LinearAlgebra.LAPACK.trsen!` — Function

    trsen!(job, compq, select, T, Q) -> (T, Q, w, s, sep)
    trsen!(select, T, Q) -> (T, Q, w, s, sep)

Reorder the Schur factorization of a matrix and optionally finds reciprocal condition numbers. If `job = N`, no condition numbers are found. If `job = E`, only the condition number for this cluster of eigenvalues is found. If `job = V`, only the condition number for the invariant subspace is found. If `job = B` then the condition numbers for the cluster and subspace are found. If `compq = V` the Schur vectors `Q` are updated. If `compq = N` the Schur vectors are not modified. `select` determines which eigenvalues are in the cluster. The 3-arg method calls the 5-arg method with `job = N` and `compq = V`.

Returns `T`, `Q`, reordered eigenvalues in `w`, the condition number of the cluster of eigenvalues `s`, and the condition number of the invariant subspace `sep`.

source
`LinearAlgebra.LAPACK.tgsen!` — Function

    tgsen!(select, S, T, Q, Z) -> (S, T, alpha, beta, Q, Z)

Reorders the vectors of a generalized Schur decomposition. `select` specifies the eigenvalues in each cluster.

source
`LinearAlgebra.LAPACK.trsyl!` — Function

    trsyl!(transa, transb, A, B, C, isgn=1) -> (C, scale)

Solves the Sylvester matrix equation `A * X +/- X * B = scale*C` where `A` and `B` are both quasi-upper triangular. If `transa = N`, `A` is not modified. If `transa = T`, `A` is transposed. If `transa = C`, `A` is conjugate transposed. Similarly for `transb` and `B`. If `isgn = 1`, the equation `A * X + X * B = scale * C` is solved. If `isgn = -1`, the equation `A * X - X * B = scale * C` is solved.

Returns `X` (overwriting `C`) and `scale`.

source
`LinearAlgebra.LAPACK.hseqr!` — Function

    hseqr!(job, compz, ilo, ihi, H, Z) -> (H, Z, w)

Computes all eigenvalues and (optionally) the Schur factorization of a matrix reduced to Hessenberg form. If `H` is balanced with `gebal!` then `ilo` and `ihi` are the outputs of `gebal!`. Otherwise they should be `ilo = 1` and `ihi = size(H,2)`. `tau` contains the elementary reflectors of the factorization.

source

## Developer Documentation

`LinearAlgebra.matprod_dest` — Function

    matprod_dest(A, B, T)

Return an appropriate `AbstractArray` with element type `T` that may be used to store the result of `A * B`.

Compat

This function requires at least Julia 1.11

source
`LinearAlgebra.haszero` — Function

    haszero(T::Type)

Return whether a type `T` has a unique zero element defined using `zero(T)`. If a type `M` specializes `zero(M)`, it may also choose to set `haszero(M)` to `true`. By default, `haszero` is assumed to be `false`, in which case the zero elements are deduced from values rather than the type.

Note

`haszero` is a conservative check that is used to dispatch to optimized paths. Extending it is optional, but encouraged.

source

- ACM832Davis, Timothy A. (2004b). Algorithm 832: UMFPACK V4.3–-an Unsymmetric-Pattern Multifrontal Method. ACM Trans. Math. Softw., 30(2), 196–199. doi:10.1145/992200.992206
- ACM887Chen, Y., Davis, T. A., Hager, W. W., & Rajamanickam, S. (2008). Algorithm 887: CHOLMOD, Supernodal Sparse Cholesky Factorization and Update/Downdate. ACM Trans. Math. Softw., 35(3). doi:10.1145/1391989.1391995
- DavisHager2009Davis, Timothy A., & Hager, W. W. (2009). Dynamic Supernodes in Sparse Cholesky Update/Downdate and Triangular Solves. ACM Trans. Math. Softw., 35(4). doi:10.1145/1462173.1462176
- Bischof1987C Bischof and C Van Loan, "The WY representation for products of Householder matrices", SIAM J Sci Stat Comput 8 (1987), s2-s13. doi:10.1137/0908009
- Schreiber1989R Schreiber and C Van Loan, "A storage-efficient WY representation for products of Householder transformations", SIAM J Sci Stat Comput 10 (1989), 53-57. doi:10.1137/0910005
- ACM933Foster, L. V., & Davis, T. A. (2013). Algorithm 933: Reliable Calculation of Numerical Rank, Null Space Bases, Pseudoinverse Solutions, and Basic Solutions Using SuitesparseQR. ACM Trans. Math. Softw., 40(1). doi:10.1145/2513109.2513116
- Bunch1977J R Bunch and L Kaufman, Some stable methods for calculating inertia and solving symmetric linear systems, Mathematics of Computation 31:137 (1977), 163-179. url.
- issue8859Issue 8859, "Fix least squares", https://github.com/JuliaLang/julia/pull/8859
- B96Åke Björck, "Numerical Methods for Least Squares Problems", SIAM Press, Philadelphia, 1996, "Other Titles in Applied Mathematics", Vol. 51. doi:10.1137/1.9781611971484
- S84G. W. Stewart, "Rank Degeneracy", SIAM Journal on Scientific and Statistical Computing, 5(2), 1984, 403-413. doi:10.1137/0905030
- KY88Konstantinos Konstantinides and Kung Yao, "Statistical analysis of effective singular values in matrix rank determination", IEEE Transactions on Acoustics, Speech and Signal Processing, 36(5), 1988, 757-763. doi:10.1109/29.1585
- H05Nicholas J. Higham, "The squaring and scaling method for the matrix exponential revisited", SIAM Journal on Matrix Analysis and Applications, 26(4), 2005, 1179-1193. doi:10.1137/090768539
- AH12Awad H. Al-Mohy and Nicholas J. Higham, "Improved inverse scaling and squaring algorithms for the matrix logarithm", SIAM Journal on Scientific Computing, 34(4), 2012, C153-C169. doi:10.1137/110852553
- AHR13Awad H. Al-Mohy, Nicholas J. Higham and Samuel D. Relton, "Computing the Fréchet derivative of the matrix logarithm and estimating the condition number", SIAM Journal on Scientific Computing, 35(4), 2013, C394-C410. doi:10.1137/120885991
- BH83Åke Björck and Sven Hammarling, "A Schur method for the square root of a matrix", Linear Algebra and its Applications, 52-53, 1983, 127-140. doi:10.1016/0024-3795(83)80010-X
- H87Nicholas J. Higham, "Computing real square roots of a real matrix", Linear Algebra and its Applications, 88-89, 1987, 405-430. doi:10.1016/0024-3795(87)90118-2
- S03Matthew I. Smith, "A Schur Algorithm for Computing Matrix pth Roots", SIAM Journal on Matrix Analysis and Applications, vol. 24, 2003, pp. 971–989. doi:10.1137/S0895479801392697
- AH16_1Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
- AH16_2Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
- AH16_3Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
- AH16_4Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
- AH16_5Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577
- AH16_6Mary Aprahamian and Nicholas J. Higham, "Matrix Inverse Trigonometric and Inverse Hyperbolic Functions: Theory and Algorithms", MIMS EPrint: 2016.4. https://doi.org/10.1137/16M1057577

Theme

This document was generated with Documenter.jl version 1.16.0 on Thursday 20 November 2025. Using Julia version 1.12.2.