LAPACK/BLAS
LAPACK and BLAS linear algebra routines.
la.blas.her2k
Compute Hermitian rank-2k update.
la.blas.her2k(A, B, options?)
la.blas.herk
Compute Hermitian rank-k update C = alpha*A*A^H + beta*C or C = alpha*A^H*A + beta*C.
la.blas.herk(A, options?)
la.blas.matmul
Compute general matrix multiplication C = alpha*op(A)*op(B) + beta*C.
la.blas.matmul(A, B, options?)
la.blas.matmulTriangular
Compute triangular matrix multiplication B = alpha*op(A)*B or B = alpha*B*op(A).
la.blas.matmulTriangular(A, B, options?)
la.blas.solveMatrixTriangular
Solve a triangular matrix system op(A)*X = alpha*B or X*op(A) = alpha*B.
la.blas.solveMatrixTriangular(A, B, options?)
la.blas.syr2k
Compute symmetric rank-2k update.
la.blas.syr2k(A, B, options?)
la.blas.syrk
Compute symmetric rank-k update C = alpha*A*A^T + beta*C or C = alpha*A^T*A + beta*C.
la.blas.syrk(A, options?)
la.blas.ger
Compute rank-1 update A = alpha*x*y^T + A.
la.blas.ger(x, y, options?)
la.blas.her
Compute Hermitian rank-1 update A = alpha*x*x^H + A.
la.blas.her(x, options?)
la.blas.matvec
Compute matrix-vector multiplication y = alpha*op(A)*x + beta*y.
la.blas.matvec(A, x, options?)
la.blas.matvecTriangular
Compute triangular matrix-vector multiplication x = op(A)*x.
la.blas.matvecTriangular(A, x, options?)
la.blas.solveVectorTriangular
Solve triangular system op(A)*x = b.
la.blas.solveVectorTriangular(A, b, options?)
la.blas.syr
Compute symmetric rank-1 update A = alpha*x*x^T + A.
la.blas.syr(x, options?)
la.blas.asum
LAWasm - LAPACK + BLAS WebAssembly Module
la.blas.asum(x)
la.blas.axpy
Compute y = alpha*x + y.
la.blas.axpy(alpha, x, y)
la.blas.copy
Copy vector x to y.
la.blas.copy(x, y?)
la.blas.dot
Compute dot product x^T * y.
la.blas.dot(x, y)
la.blas.dotc
Compute conjugate dot product x^H * y.
la.blas.dotc(x, y)
la.blas.iamax
Find index of element with maximum absolute value.
la.blas.iamax(x)
la.blas.nrm2
Compute Euclidean (L2) norm of a vector.
la.blas.nrm2(x)
la.blas.scal
Compute x = alpha*x.
la.blas.scal(alpha, x)
la.blas.swap
Swap vectors x and y.
la.blas.swap(x, y)
la.eigenvalues.eig
Compute eigenvalues and optionally eigenvectors of a general n×n matrix A.
la.eigenvalues.eig(A, options?)
la.eigenvalues.eigBanded
Compute eigenvalues and eigenvectors of a symmetric banded n×n matrix.
la.eigenvalues.eigBanded(A, n, kd, options?)
la.eigenvalues.eigGeneralized
Solve the generalized eigenvalue problem Ax = λBx.
la.eigenvalues.eigGeneralized(A, B, options?)
la.eigenvalues.eigGeneralizedSymmetric
Solve the symmetric generalized eigenvalue problem.
la.eigenvalues.eigGeneralizedSymmetric(A, B, options?)
la.eigenvalues.eigHermitian
Compute eigenvalues and optionally eigenvectors of a complex Hermitian n×n matrix A.
la.eigenvalues.eigHermitian(A, n, options?)
la.eigenvalues.eigSelect
Compute selected eigenvalues and eigenvectors of a real symmetric n×n matrix A.
la.eigenvalues.eigSelect(A, options?)
la.eigenvalues.eigSymmetric
Compute eigenvalues and optionally eigenvectors of a real symmetric n×n matrix A.
la.eigenvalues.eigSymmetric(A, options?)
la.eigenvalues.eigTridiagonal
Compute eigenvalues and eigenvectors of a symmetric tridiagonal matrix.
la.eigenvalues.eigTridiagonal(d, e, options?)
la.eigenvalues.eigvals
Compute only the eigenvalues of a general n×n matrix A.
la.eigenvalues.eigvals(A, options?)
la.factorizations.cholesky
Compute the Cholesky factorization of a symmetric positive definite matrix A.
la.factorizations.cholesky(A, options?)
la.factorizations.hessenberg
Reduce a general n×n matrix A to upper Hessenberg form.
la.factorizations.hessenberg(A, options?)
la.factorizations.ldl
Compute the LDL^T factorization of a symmetric n×n matrix A.
la.factorizations.ldl(A, options?)
la.factorizations.lq
Compute the LQ factorization of a general m×n matrix A.
la.factorizations.lq(A, options?)
la.factorizations.lu
Compute the LU factorization of a general m×n matrix A.
la.factorizations.lu(A, options?)
la.factorizations.qr
Compute the QR factorization of a general m×n matrix A.
la.factorizations.qr(A, options?)
la.factorizations.qrPivoted
Compute the QR factorization with column pivoting of a general m×n matrix A.
la.factorizations.qrPivoted(A, options?)
la.factorizations.schur
Compute the Schur decomposition of a general n×n matrix A.
la.factorizations.schur(A, options?)
la.inverses.inv
Compute the inverse of a general n×n matrix A.
la.inverses.inv(A, options?)
la.inverses.invSymmetric
Compute the inverse of a symmetric positive definite n×n matrix A.
la.inverses.invSymmetric(A, options?)
la.inverses.invTriangular
Compute the inverse of a triangular n×n matrix A.
la.inverses.invTriangular(A, options?)
la.inverses.pinv
Compute the Moore-Penrose pseudoinverse of an m×n matrix A.
la.inverses.pinv(A, options?)
la.leastSquares.constrainedLstSq
Solve an equality-constrained least squares problem.
la.leastSquares.constrainedLstSq(A, b, C, d, _options?)
la.leastSquares.generalizedLstSq
Solve a generalized least squares problem.
la.leastSquares.generalizedLstSq(A, B, d, _options?)
la.leastSquares.lstsq
Solve a least squares problem using QR factorization.
la.leastSquares.lstsq(A, b, options?)
la.leastSquares.lstsqGelsy
Solve a least squares problem using QR with column pivoting.
la.leastSquares.lstsqGelsy(A, b, options?)
la.leastSquares.lstsqSVD
Solve a least squares problem using SVD.
la.leastSquares.lstsqSVD(A, b, options?)
la.linearSolvers.solve
Solve a system of linear equations Ax = b for a general matrix A.
la.linearSolvers.solve(A, b, options?)
la.linearSolvers.solveBanded
Solve a banded system Ax = b where A is a general banded matrix.
la.linearSolvers.solveBanded(AB, b, n, kl, ku)
la.linearSolvers.solveHermitian
Solve a complex Hermitian positive definite system Ax = b.
la.linearSolvers.solveHermitian(A, b, n, options?)
la.linearSolvers.solveSymmetric
Solve a symmetric positive definite system Ax = b.
la.linearSolvers.solveSymmetric(A, b, options?)
la.linearSolvers.solveTriangular
Solve a triangular system Ax = b where A is upper or lower triangular.
la.linearSolvers.solveTriangular(A, b, options?)
la.linearSolvers.solveTridiagonal
Solve a tridiagonal system Ax = b where A is tridiagonal.
la.linearSolvers.solveTridiagonal(dl, d, du, b)
la.matrixFunctions.expm
Compute the matrix exponential e^A.
la.matrixFunctions.expm(A)
la.matrixFunctions.funm
Compute a general matrix function f(A).
la.matrixFunctions.funm(A, f)
la.matrixFunctions.logm
Compute the principal matrix logarithm log(A).
la.matrixFunctions.logm(A)
la.matrixFunctions.powm
Compute matrix power A^p.
la.matrixFunctions.powm(A, p)
la.matrixFunctions.sqrtm
Compute the principal matrix square root.
la.matrixFunctions.sqrtm(A)
la.matrixFunctions.csd
Compute the Cosine-Sine Decomposition of a partitioned unitary matrix.
la.matrixFunctions.csd(X, p, q)
la.matrixFunctions.polarDecomposition
Compute the polar decomposition A = U*P.
la.matrixFunctions.polarDecomposition(A)
la.matrixFunctions.rrqr
Compute rank-revealing QR decomposition with column pivoting.
la.matrixFunctions.rrqr(A, options?)
la.matrixFunctions.choleskyHermitian
Compute Cholesky factorization of a Hermitian positive definite matrix.
la.matrixFunctions.choleskyHermitian(A, options?)
la.norms.cond
Compute the condition number of a matrix.
la.norms.cond(A, options?)
la.norms.condEst
Compute a fast estimate of the condition number.
la.norms.condEst(A, options?)
la.norms.det
Compute the determinant of a square matrix.
la.norms.det(A, options?)
la.norms.logdet
Compute the natural logarithm of the absolute value of the determinant.
la.norms.logdet(A, options?)
la.norms.norm
Compute a matrix norm.
la.norms.norm(A, options?)
la.norms.rank
Compute the numerical rank of a matrix.
la.norms.rank(A, options?)
la.norms.rcond
Compute the reciprocal of the condition number of a matrix.
la.norms.rcond(A, options?)
la.norms.slogdet
Compute the sign and natural logarithm of the absolute value of the determinant.
la.norms.slogdet(A, options?)
la.svdModule.svd
Compute the Singular Value Decomposition of a general m×n matrix A.
la.svdModule.svd(A, options?)
la.svdModule.svdCompact
Compute the compact (economy-size) SVD of a general m×n matrix A.
la.svdModule.svdCompact(A, algorithm?)
la.svdModule.svdRank
Estimate the numerical rank of a matrix using SVD.
la.svdModule.svdRank(A, options?)
la.svdModule.svdvals
Compute only the singular values of a general m×n matrix A.
la.svdModule.svdvals(A, options?)
la.utilities.balance
Balance a matrix to improve eigenvalue computation.
la.utilities.balance(A)
la.utilities.conjugate
Compute the complex conjugate of a matrix.
la.utilities.conjugate(A)
la.utilities.diag
Extract the diagonal from a matrix, or create a diagonal matrix from a vector.
la.utilities.diag(A, k?)
la.utilities.hermitian
Compute the conjugate transpose (Hermitian transpose) of a matrix.
la.utilities.hermitian(A)
la.utilities.trace
Compute the trace of a matrix (sum of diagonal elements).
la.utilities.trace(A)
la.utilities.transpose
Compute the transpose of a matrix.
la.utilities.transpose(A)
la.utilities.tril
Extract the lower triangular part of a matrix.
la.utilities.tril(A, options?)
la.utilities.triu
Extract the upper triangular part of a matrix.
la.utilities.triu(A, options?)
la.utilities.isHermitian
Test if a matrix is Hermitian (A = A^H).
la.utilities.isHermitian(A, options?)
la.utilities.isOrthogonal
Test if a matrix is orthogonal (Q^T * Q = I).
la.utilities.isOrthogonal(A, options?)
la.utilities.isPositiveDefinite
Test if a matrix is positive definite.
la.utilities.isPositiveDefinite(A)
la.utilities.isSingular
Test if a matrix is singular (non-invertible).
la.utilities.isSingular(A, options?)
la.utilities.isSymmetric
Test if a matrix is symmetric (A = A^T).
la.utilities.isSymmetric(A, options?)
la.utilities.isUnitary
Test if a matrix is unitary (Q^H * Q = I).
la.utilities.isUnitary(A, options?)
la.configureLAPACK
configureLAPACK
la.configureLAPACK(config)
la.fromColumnMajor
Convert a column-major 1D array to row-major 2D array.
la.fromColumnMajor(data, m, n)
la.getLAPACKModule
getLAPACKModule
la.getLAPACKModule()
la.isLAPACKLoaded
isLAPACKLoaded
la.isLAPACKLoaded()
la.loadLAPACKModule
loadLAPACKModule
la.loadLAPACKModule()
la.prepareMatrix
Prepare a matrix for LAPACK: convert to column-major Float64Array.
la.prepareMatrix(matrix)
la.prepareVector
Prepare a vector for LAPACK.
la.prepareVector(vector)
la.resetLAPACKModule
resetLAPACKModule
la.resetLAPACKModule()
la.toColumnMajor
Convert a row-major 2D array to column-major 1D array (Fortran order).
la.toColumnMajor(matrix)