.. module:: Base.LinAlg
.. currentmodule:: Base
Linear algebra functions in Julia are largely implemented by calling functions from LAPACK. Sparse factorizations call functions from SuiteSparse.
.. function:: *(A, B) :noindex: Matrix multiplication
.. function:: \\(A, B) :noindex: 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``. A direct solver is used for upper- or lower triangular ``A``. For Hermitian ``A`` (equivalent to symmetric ``A`` for non-complex ``A``) the ``BunchKaufman`` factorization is used. Otherwise an LU factorization is used. For rectangular ``A`` the result is the minimum-norm least squares solution computed by reducing ``A`` to bidiagonal form and solving the bidiagonal least squares problem. For sparse, square ``A`` the LU factorization (from UMFPACK) is used.
.. function:: dot(x, y) Compute the dot product. For complex vectors, the first vector is conjugated.
.. function:: cross(x, y) Compute the cross product of two 3-vectors.
.. function:: rref(A) Compute the reduced row echelon form of the matrix A.
.. function:: factorize(A) Compute a convenient factorization (including LU, Cholesky, Bunch-Kaufman, Triangular) of A, based upon the type of the input matrix. The return value can then be reused for efficient solving of multiple systems. For example: ``A=factorize(A); x=A\\b; y=A\\C``.
.. function:: factorize!(A) ``factorize!`` is the same as :func:`factorize`, but saves space by overwriting the input ``A``, instead of creating a copy.
.. function:: lu(A) -> L, U, p Compute the LU factorization of ``A``, such that ``A[p,:] = L*U``.
.. function:: lufact(A) -> LU Compute the LU factorization of ``A``, returning an ``LU`` object for dense ``A`` or an ``UmfpackLU`` object for sparse ``A``. The individual components of the factorization ``F`` can be accesed by indexing: ``F[:L]``, ``F[:U]``, and ``F[:P]`` (permutation matrix) or ``F[:p]`` (permutation vector). An ``UmfpackLU`` object has additional components ``F[:q]`` (the left permutation vector) and ``Rs`` the vector of scaling factors. The following functions are available for both ``LU`` and ``UmfpackLU`` objects: ``size``, ``\`` and ``det``. For ``LU`` there is also an ``inv`` method. The sparse LU factorization is such that ``L*U`` is equal to``scale(Rs,A)[p,q]``.
.. function:: lufact!(A) -> LU ``lufact!`` is the same as :func:`lufact`, but saves space by overwriting the input A, instead of creating a copy. For sparse ``A`` the ``nzval`` field is not overwritten but the index fields, ``colptr`` and ``rowval`` are decremented in place, converting from 1-based indices to 0-based indices.
.. function:: chol(A, [LU]) -> F Compute the Cholesky factorization of a symmetric positive definite matrix ``A`` and return the matrix ``F``. If ``LU`` is ``:L`` (Lower), ``A = L*L'``. If ``LU`` is ``:U`` (Upper), ``A = R'*R``.
.. function:: cholfact(A, [LU,][pivot=false,][tol=-1.0]) -> Cholesky Compute the Cholesky factorization of a dense symmetric positive (semi)definite matrix ``A`` and return either a ``Cholesky`` if ``pivot=false`` or ``CholeskyPivoted`` if ``pivot=true``. ``LU`` may be ``:L`` for using the lower part or ``:U`` for the upper part. The default is to use ``:U``. The triangular matrix can be obtained from the factorization ``F`` with: ``F[:L]`` and ``F[:U]``. The following functions are available for ``Cholesky`` objects: ``size``, ``\``, ``inv``, ``det``. For ``CholeskyPivoted`` there is also defined a ``rank``. If ``pivot=false`` a ``PosDefException`` exception is thrown in case the matrix is not positive definite. The argument ``tol`` determines the tolerance for determining the rank. For negative values, the tolerance is the machine precision.
.. function:: cholfact(A, [ll]) -> CholmodFactor Compute the sparse Cholesky factorization of a sparse matrix ``A``. If ``A`` is Hermitian its Cholesky factor is determined. If ``A`` is not Hermitian the Cholesky factor of ``A*A'`` is determined. A fill-reducing permutation is used. Methods for ``size``, ``solve``, ``\``, ``findn_nzs``, ``diag``, ``det`` and ``logdet``. One of the solve methods includes an integer argument that can be used to solve systems involving parts of the factorization only. The optional boolean argument, ``ll`` determines whether the factorization returned is of the ``A[p,p] = L*L'`` form, where ``L`` is lower triangular or ``A[p,p] = scale(L,D)*L'`` form where ``L`` is unit lower triangular and ``D`` is a non-negative vector. The default is LDL.
.. function:: cholfact!(A, [LU,][pivot=false,][tol=-1.0]) -> Cholesky ``cholfact!`` is the same as :func:`cholfact`, but saves space by overwriting the input ``A``, instead of creating a copy.
.. function:: qr(A, [pivot=false,][thin=true]) -> Q, R, [p] Compute the (pivoted) QR factorization of ``A`` such that either ``A = Q*R`` or ``A[:,p] = Q*R``. Also see ``qrfact``. The default is to compute a thin factorization. Note that ``R`` is not extended with zeros when the full ``Q`` is requested.
.. function:: qrfact(A,[pivot=false]) Computes the QR factorization of ``A`` and returns either a ``QR`` type if ``pivot=false`` or ``QRPivoted`` type if ``pivot=true``. From a ``QR`` or ``QRPivoted`` factorization ``F``, an orthogonal matrix ``F[:Q]`` and a triangular matrix ``F[:R]`` can be extracted. For ``QRPivoted`` it is also posiible to extract the permutation vector ``F[:p]`` or matrix ``F[:P]``. The following functions are available for the ``QR`` objects: ``size``, ``\``. When ``A`` is rectangular ``\`` will return a least squares solution and if the soultion is not unique, the one with smallest norm is returned. The orthogonal matrix ``Q=F[:Q]`` is a ``QRPackedQ`` type when ``F`` is a ``QR`` and a ``QRPivotedQ`` then ``F`` is a ``QRPivoted``. Both have the ``*`` operator overloaded to support efficient multiplication by ``Q`` and ``Q'``. Multiplication with respect to either thin or full ``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 ``full`` which has a named argument ``thin``.
.. function:: qrfact!(A,[pivot=false]) ``qrfact!`` is the same as :func:`qrfact`, but saves space by overwriting the input ``A``, instead of creating a copy.
.. function:: bkfact(A) -> BunchKaufman Compute the Bunch-Kaufman factorization of a real symmetric or complex Hermitian matrix ``A`` and return a ``BunchKaufman`` object. The following functions are available for ``BunchKaufman`` objects: ``size``, ``\``, ``inv``, ``issym``, ``ishermitian``.
.. function:: bkfact!(A) -> BunchKaufman ``bkfact!`` is the same as :func:`bkfact`, but saves space by overwriting the input ``A``, instead of creating a copy.
.. function:: sqrtm(A) Compute the matrix square root of ``A``. If ``B = sqrtm(A)``, then ``B*B == A`` within roundoff error. ``sqrtm`` uses a polyalgorithm, computing the matrix square root using Schur factorizations (:func:`schurfact`) unless it detects the matrix to be Hermitian or real symmetric, in which case it computes the matrix square root from an eigendecomposition (:func:`eigfact`). In the latter situation for positive definite matrices, the matrix square root has ``Real`` elements, otherwise it has ``Complex`` elements.
.. function:: eig(A,[permute=true,][scale=true]) -> D, V Wrapper around ``eigfact`` extracting all parts the factorization to a tuple. Direct use of ``eigfact`` is therefore generally more efficient. Computes eigenvalues and eigenvectors of ``A``. See :func:`eigfact` for details on the ``permute`` and ``scale`` keyword arguments.
.. function:: eig(A, B) -> D, V Wrapper around ``eigfact`` extracting all parts the factorization to a tuple. Direct use of ``eigfact`` is therefore generally more efficient. Computes generalized eigenvalues and vectors of ``A`` with respect to ``B``.
.. function:: eigvals(A) Returns the eigenvalues of ``A``.
.. function:: eigmax(A) Returns the largest eigenvalue of ``A``.
.. function:: eigmin(A) Returns the smallest eigenvalue of ``A``.
.. function:: eigvecs(A, [eigvals,][permute=true,][scale=true]) Returns the eigenvectors of ``A``. The ``permute`` and ``scale`` keywords are the same as for :func:`eigfact`. For ``SymTridiagonal`` matrices, if the optional vector of eigenvalues ``eigvals`` is specified, returns the specific corresponding eigenvectors.
.. function:: eigfact(A,[permute=true,][scale=true]) Compute the eigenvalue decomposition of ``A`` and return an ``Eigen`` object. If ``F`` is the factorization object, the eigenvalues can be accessed with ``F[:values]`` and the eigenvectors with ``F[:vectors]``. The following functions are available for ``Eigen`` objects: ``inv``, ``det``. For general non-symmetric 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.
.. function:: eigfact(A, B) Compute the generalized eigenvalue decomposition of ``A`` and ``B`` and return an ``GeneralizedEigen`` object. If ``F`` is the factorization object, the eigenvalues can be accessed with ``F[:values]`` and the eigenvectors with ``F[:vectors]``.
.. function:: eigfact!(A, [B]) ``eigfact!`` is the same as :func:`eigfact`, but saves space by overwriting the input A (and B), instead of creating a copy.
.. function:: hessfact(A) 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]`` 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 ``full``.
.. function:: hessfact!(A) ``hessfact!`` is the same as :func:`hessfact`, but saves space by overwriting the input A, instead of creating a copy.
.. function:: schurfact(A) -> 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 unitary/orthogonal 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]``.
.. function:: schurfact!(A) Computer the Schur factorization of ``A``, overwriting ``A`` in the process. See :func:`schurfact`
.. function:: schur(A) -> Schur[:T], Schur[:Z], Schur[:values] See :func:`schurfact`
.. function:: schurfact(A, B) -> 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[:alpha]./F[:beta]``.
.. function:: schur(A,B) -> GeneralizedSchur[:S], GeneralizedSchur[:T], GeneralizedSchur[:Q], GeneralizedSchur[:Z] See :func:`schurfact`
.. function:: svdfact(A, [thin=true]) -> 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*diagm(S)*Vt``. If ``thin`` is ``true``, an economy mode decomposition is returned. The algorithm produces ``Vt`` and hence ``Vt`` is more efficient to extract than ``V``. The default is to produce a thin decomposition.
.. function:: svdfact!(A, [thin=true]) -> SVD ``svdfact!`` is the same as :func:`svdfact`, but saves space by overwriting the input A, instead of creating a copy. If ``thin`` is ``true``, an economy mode decomposition is returned. The default is to produce a thin decomposition.
.. function:: svd(A, [thin=true]) -> U, S, V Wrapper around ``svdfact`` extracting all parts the factorization to a tuple. Direct use of ``svdfact`` is therefore generally more efficient. Computes the SVD of A, returning ``U``, vector ``S``, and ``V`` such that ``A == U*diagm(S)*V'``. If ``thin`` is ``true``, an economy mode decomposition is returned. The default is to produce a thin decomposition.
.. function:: svdvals(A) Returns the singular values of ``A``.
.. function:: svdvals!(A) Returns the singular values of ``A``, while saving space by overwriting the input.
.. function:: svdfact(A, B) -> GeneralizedSVD Compute the generalized SVD of ``A`` and ``B``, returning a ``GeneralizedSVD`` Factorization object ``F``, such that ``A = F[:U]*F[:D1]*F[:R0]*F[:Q]'`` and ``B = F[:V]*F[:D2]*F[:R0]*F[:Q]'``.
.. function:: svd(A, B) -> U, V, Q, D1, D2, R0 Wrapper around ``svdfact`` extracting all parts the factorization to a tuple. Direct use of ``svdfact`` is therefore generally more efficient. The function returns the generalized SVD of ``A`` and ``B``, returning ``U``, ``V``, ``Q``, ``D1``, ``D2``, and ``R0`` such that ``A = U*D1*R0*Q'`` and ``B = V*D2*R0*Q'``.
.. function:: svdvals(A, B) Return only the singular values from the generalized singular value decomposition of ``A`` and ``B``.
.. function:: triu(M) Upper triangle of a matrix.
.. function:: triu!(M) Upper triangle of a matrix, overwriting ``M`` in the process.
.. function:: tril(M) Lower triangle of a matrix.
.. function:: tril!(M) Lower triangle of a matrix, overwriting ``M`` in the process.
.. function:: diagind(M[, k]) A ``Range`` giving the indices of the ``k``-th diagonal of the matrix ``M``.
.. function:: diag(M[, k]) The ``k``-th diagonal of a matrix, as a vector.
.. function:: diagm(v[, k]) Construct a diagonal matrix and place ``v`` on the ``k``-th diagonal.
.. function:: scale(A, b), scale(b, A) Scale an array ``A`` by a scalar ``b``, returning a new array. If ``A`` is a matrix and ``b`` is a vector, then ``scale!(A,b)`` scales each column ``i`` of ``A`` by ``b[i]`` (similar to ``A*diagm(b)``), while ``scale!(b,A)`` scales each row ``i`` of ``A`` by ``b[i]`` (similar to ``diagm(b)*A``), returning a new array. Note: for large ``A``, ``scale`` can be much faster than ``A .* b`` or ``b .* A``, due to the use of BLAS.
.. function:: scale!(A, b), scale!(b, A) Scale an array ``A`` by a scalar ``b``, similar to ``scale`` but overwriting ``A`` in-place. If ``A`` is a matrix and ``b`` is a vector, then ``scale!(A,b)`` scales each column ``i`` of ``A`` by ``b[i]`` (similar to ``A*diagm(b)``), while ``scale!(b,A)`` scales each row ``i`` of ``A`` by ``b[i]`` (similar to ``diagm(b)*A``), again operating in-place on ``A``.
.. function:: Tridiagonal(dl, d, du) Construct a tridiagonal matrix from the lower diagonal, diagonal, and upper diagonal, respectively. The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with ``full``.
.. function:: Bidiagonal(dv, ev, isupper) Constructs an upper (``isupper=true``) or lower (``isupper=false``) 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 ``full``.
.. function:: SymTridiagonal(d, du) Construct a real symmetric tridiagonal matrix from the diagonal and upper diagonal, respectively. The result is of type ``SymTridiagonal`` and provides efficient specialized eigensolvers, but may be converted into a regular matrix with ``full``.
.. function:: Woodbury(A, U, C, V) Construct a matrix in a form suitable for applying the Woodbury matrix identity.
.. function:: rank(M) Compute the rank of a matrix.
.. function:: norm(A, [p]) Compute the ``p``-norm of a vector or the operator norm of a matrix ``A``, defaulting to the ``p=2``-norm. For vectors, ``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. For matrices, valid values of ``p`` are ``1``, ``2``, or ``Inf``. Use :func:`vecnorm` to compute the Frobenius norm.
.. function:: vecnorm(A, [p]) For any iterable container ``A`` (including arrays of any dimension) of numbers, compute the ``p``-norm (defaulting to ``p=2``) as if ``A`` were a vector of the corresponding length. For example, if ``A`` is a matrix and ``p=2``, then this is equivalent to the Frobenius norm.
.. function:: cond(M, [p]) Condition number of the matrix ``M``, computed using the operator ``p``-norm. Valid values for ``p`` are ``1``, ``2`` (default), or ``Inf``.
.. function:: condskeel(M, [x, p])
.. math::
\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) & = \left\Vert \left\vert M \right\vert \left\vert M^{-1} \right\vert \left\vert x \right\vert \right\Vert_p
Skeel condition number :math:`\kappa_S` of the matrix ``M``, optionally with respect to the vector ``x``, as computed using the operator ``p``-norm. ``p`` is ``Inf`` by default, if not provided. Valid values for ``p`` are ``1``, ``2``, or ``Inf``.
This quantity is also known in the literature as the Bauer condition number, relative condition number, or componentwise relative condition number.
.. function:: trace(M) Matrix trace
.. function:: det(M) Matrix determinant
.. function:: logdet(M) Log of matrix determinant. Equivalent to ``log(det(M))``, but may provide increased accuracy and/or speed.
.. function:: inv(M) Matrix inverse
.. function:: pinv(M) Moore-Penrose pseudoinverse
.. function:: null(M) Basis for nullspace of ``M``.
.. function:: repmat(A, n, m) Construct a matrix by repeating the given matrix ``n`` times in dimension 1 and ``m`` times in dimension 2.
.. function:: repeat(A, inner = Int[], outer = Int[]) Construct an array by repeating the entries of ``A``. The i-th element of ``inner`` specifies the number of times that the individual entries of the i-th dimension of ``A`` should be repeated. The i-th element of ``outer`` specifies the number of times that a slice along the i-th dimension of ``A`` should be repeated.
.. function:: kron(A, B) Kronecker tensor product of two vectors or two matrices.
.. function:: blkdiag(A...) Concatenate matrices block-diagonally. Currently only implemented for sparse matrices.
.. function:: linreg(x, y) Determine parameters ``[a, b]`` that minimize the squared error between ``y`` and ``a+b*x``.
.. function:: linreg(x, y, w) Weighted least-squares linear regression.
.. function:: expm(A) Matrix exponential.
.. function:: issym(A) Test whether a matrix is symmetric.
.. function:: isposdef(A) Test whether a matrix is positive definite.
.. function:: isposdef!(A) Test whether a matrix is positive definite, overwriting ``A`` in the processes.
.. function:: istril(A) Test whether a matrix is lower triangular.
.. function:: istriu(A) Test whether a matrix is upper triangular.
.. function:: ishermitian(A) Test whether a matrix is Hermitian.
.. function:: transpose(A) The transposition operator (``.'``).
.. function:: ctranspose(A) The conjugate transposition operator (``'``).
.. function:: eigs(A; nev=6, which="LM", tol=0.0, maxiter=1000, sigma=nothing, ritzvec=true, op_part=:real,v0=zeros((0,))) -> (d,[v,],nconv,niter,nmult,resid)
``eigs`` computes eigenvalues ``d`` of ``A`` using Lanczos or Arnoldi iterations for real symmetric or general nonsymmetric matrices respectively. The following keyword arguments are supported:
* ``nev``: Number of eigenvalues
* ``which``: type of eigenvalues to compute. See the note below.
========= ======================================================================================================================
``which`` type of eigenvalues
--------- ----------------------------------------------------------------------------------------------------------------------
``"LM"`` eigenvalues of largest magnitude
``"SM"`` eigenvalues of smallest magnitude
``"LA"`` largest algebraic eigenvalues (real symmetric ``A`` only)
``"SA"`` smallest algebraic eigenvalues (real symmetric ``A`` only)
``"BE"`` compute half of the eigenvalues from each end of the spectrum, biased in favor of the high end. (symmetric ``A`` only)
``"LR"`` eigenvalues of largest real part (nonsymmetric ``A`` only)
``"SR"`` eigenvalues of smallest real part (nonsymmetric ``A`` only)
``"LI"`` eigenvalues of largest imaginary part (nonsymmetric ``A`` only)
``"SI"`` eigenvalues of smallest imaginary part (nonsymmetric ``A`` only)
========= ======================================================================================================================
* ``tol``: tolerance (:math:`tol \le 0.0` defaults to ``DLAMCH('EPS')``)
* ``maxiter``: Maximum number of iterations
* ``sigma``: Specifies the level shift used in inverse iteration. If ``nothing`` (default), defaults to ordinary (forward) iterations. Otherwise, find eigenvalues close to ``sigma`` using shift and invert iterations.
* ``ritzvec``: Returns the Ritz vectors ``v`` (eigenvectors) if ``true``
* ``op_part``: which part of linear operator to use for real ``A`` (``:real``, ``:imag``)
* ``v0``: starting vector from which to start the iterations
``eigs`` returns the ``nev`` requested eigenvalues in ``d``, the corresponding Ritz vectors ``v`` (only if ``ritzvec=true``), the number of converged eigenvalues ``nconv``, the number of iterations ``niter`` and the number of matrix vector multiplications ``nmult``, as well as the final residual vector ``resid``.
.. note:: The ``sigma`` and ``which`` keywords interact: the description of eigenvalues searched for by ``which`` do _not_ necessarily refer to the eigenvalues of ``A``, but rather the linear operator constructed by the specification of the iteration mode implied by ``sigma``.
=============== ================================== ==================================
``sigma`` iteration mode ``which`` refers to eigenvalues of
--------------- ---------------------------------- ----------------------------------
``nothing`` ordinary (forward) :math:`A`
real or complex inverse with level shift ``sigma`` :math:`(A - \sigma I )^{-1}`
=============== ================================== ==================================
.. function:: svds(A; nev=6, which="LA", tol=0.0, maxiter=1000, ritzvec=true)
``svds`` computes the singular values of A using Arnoldi factorization. The following keyword arguments are supported:
* ``nsv``: Number of singular values
* ``which``: type of singular values ("LA")
* ``tol``: tolerance (:math:`tol \le 0.0` defaults to ``DLAMCH('EPS')``)
* ``maxiter``: Maximum number of iterations
* ``ritzvec``: Returns the singular vectors if ``true``
.. function:: peakflops(n; parallel=false) ``peakflops`` computes the peak flop rate of the computer by using BLAS double precision :func:`gemm!`. By default, if no arguments are specified, it multiplies a matrix of size ``n x n``, where ``n = 2000``. 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 ``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.
.. module:: Base.LinAlg.BLAS
This module provides wrappers for some of the BLAS functions for
linear algebra. Those BLAS functions that overwrite one of the input
arrays have names ending in '!'.
Usually a function has 4 methods defined, one each for Float64,
Float32, Complex128 and Complex64 arrays.
.. currentmodule:: Base.LinAlg.BLAS
.. 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``.
.. function:: dotu(n, X, incx, Y, incy) Dot function for two complex vectors.
.. function:: dotc(n, X, incx, U, incy) Dot function for two complex vectors conjugating the first vector.
.. function:: blascopy!(n, X, incx, Y, incy) Copy ``n`` elements of array ``X`` with stride ``incx`` to array ``Y`` with stride ``incy``. Returns ``Y``.
.. function:: nrm2(n, X, incx) 2-norm of a vector consisting of ``n`` elements of array ``X`` with stride ``incx``.
.. function:: asum(n, X, incx) sum of the absolute values of the first ``n`` elements of array ``X`` with stride ``incx``.
.. function:: axpy!(n, a, X, incx, Y, incy) Overwrite ``Y`` with ``a*X + Y``. Returns ``Y``.
.. function:: scal!(n, a, X, incx) Overwrite ``X`` with ``a*X``. Returns ``X``.
.. function:: scal(n, a, X, incx) Returns ``a*X``.
.. function:: syrk!(uplo, trans, alpha, A, beta, C)
Rank-k update of the symmetric matrix ``C`` as ``alpha*A*A.' +
beta*C`` or ``alpha*A.'*A + beta*C`` according to whether ``trans``
is 'N' or 'T'. When ``uplo`` is 'U' the upper triangle of ``C`` is
updated ('L' for lower triangle). Returns ``C``.
.. function:: syrk(uplo, trans, alpha, A)
Returns either the upper triangle or the lower triangle, according
to ``uplo`` ('U' or 'L'), of ``alpha*A*A.'`` or ``alpha*A.'*A``,
according to ``trans`` ('N' or 'T').
.. 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 whether ``trans`` is 'N' or 'T'. When ``uplo`` is 'U'
the upper triangle of ``C`` is updated ('L' for lower triangle).
Returns ``C``.
.. function:: herk(uplo, trans, alpha, A)
Methods for complex arrays only. Returns either the upper triangle
or the lower triangle, according to ``uplo`` ('U' or 'L'), of
``alpha*A*A'`` or ``alpha*A'*A``, according to ``trans`` ('N' or 'T').
.. 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`` ('N' or 'T'). The matrix ``A`` is
a general band matrix of dimension ``m`` by ``size(A,2)`` with
``kl`` sub-diagonals and ``ku`` super-diagonals. Returns the
updated ``y``.
.. function:: gbmv(trans, m, kl, ku, alpha, A, x, beta, y)
Returns ``alpha*A*x`` or ``alpha*A'*x`` according to ``trans`` ('N'
or 'T'). The matrix ``A`` is a general band matrix of dimension
``m`` by ``size(A,2)`` with ``kl`` sub-diagonals and
``ku`` super-diagonals.
.. function:: sbmv!(uplo, k, alpha, A, x, beta, y) Update vector ``y`` as ``alpha*A*x + beta*y`` where ``A`` is a 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 http://www.netlib.org/lapack/explore-html/. Returns the updated ``y``.
.. function:: sbmv(uplo, k, alpha, A, x) Returns ``alpha*A*x`` where ``A`` is a symmetric band matrix of order ``size(A,2)`` with ``k`` super-diagonals stored in the argument ``A``.
.. function:: sbmv(uplo, k, A, x) Returns ``A*x`` where ``A`` is a symmetric band matrix of order ``size(A,2)`` with ``k`` super-diagonals stored in the argument ``A``.
.. 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`` (transpose ``A``) and ``tB``. Returns the updated ``C``.
.. function:: gemm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: gemm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: gemv!(tA, alpha, A, x, beta, y) Update the vector ``y`` as ``alpha*A*x + beta*x`` or ``alpha*A'x + beta*x`` according to ``tA`` (transpose ``A``). Returns the updated ``y``.
.. function:: gemv(tA, alpha, A, x) Returns ``alpha*A*x`` or ``alpha*A'x`` according to ``tA`` (transpose ``A``).
.. function:: gemv(tA, alpha, A, x) Returns ``A*x`` or ``A'x`` according to ``tA`` (transpose ``A``).
.. 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. Returns the updated ``C``.
.. function:: symm(side, ul, alpha, A, B) Returns ``alpha*A*B`` or ``alpha*B*A`` according to ``side``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symm(side, ul, A, B) Returns ``A*B`` or ``B*A`` according to ``side``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symm(tA, tB, alpha, A, B) Returns ``alpha*A*B`` or the other three variants according to ``tA`` (transpose ``A``) and ``tB``.
.. function:: symv!(ul, alpha, A, x, beta, y) Update the vector ``y`` as ``alpha*A*y + beta*y``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used. Returns the updated ``y``.
.. function:: symv(ul, alpha, A, x) Returns ``alpha*A*x``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. function:: symv(ul, A, x) Returns ``A*x``. ``A`` is assumed to be symmetric. Only the ``ul`` triangle of ``A`` is used.
.. 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`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``B``.
.. function:: trmm(side, ul, tA, dA, alpha, A, B) Returns ``alpha*A*B`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. 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`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``B``.
.. function:: trsm(side, ul, tA, dA, alpha, A, B) Returns the solution to ``A*X = alpha*B`` or one of the other three variants determined by ``side`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: trmv!(side, ul, tA, dA, alpha, A, b) Update ``b`` as ``alpha*A*b`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``b``.
.. function:: trmv(side, ul, tA, dA, alpha, A, b) Returns ``alpha*A*b`` or one of the other three variants determined by ``side`` (A on left or right) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: trsv!(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`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones). Returns the updated ``b``.
.. function:: trsv(side, ul, tA, dA, alpha, A, b) Returns the solution to ``A*X = alpha*b`` or one of the other three variants determined by ``side`` (A on left or right of ``X``) and ``tA`` (transpose A). Only the ``ul`` triangle of ``A`` is used. ``dA`` indicates if ``A`` is unit-triangular (the diagonal is assumed to be all ones).
.. function:: blas_set_num_threads(n) Set the number of threads the BLAS library should use.