SciPy

Linear algebra (scipy.linalg)

Linear algebra functions.

See also

numpy.linalg for more linear algebra functions. Note that although scipy.linalg imports most of them, identically named functions from scipy.linalg may offer more or slightly differing functionality.

Basics

inv(a[, overwrite_a, check_finite]) Compute the inverse of a matrix.
solve(a, b[, sym_pos, lower, overwrite_a, ...]) Solve the equation a x = b for x.
solve_banded(l_and_u, ab, b[, overwrite_ab, ...]) Solve the equation a x = b for x, assuming a is banded matrix.
solveh_banded(ab, b[, overwrite_ab, ...]) Solve equation a x = b.
solve_triangular(a, b[, trans, lower, ...]) Solve the equation a x = b for x, assuming a is a triangular matrix.
det(a[, overwrite_a, check_finite]) Compute the determinant of a matrix
norm(a[, ord]) Matrix or vector norm.
lstsq(a, b[, cond, overwrite_a, ...]) Compute least-squares solution to equation Ax = b.
pinv(a[, cond, rcond, return_rank, check_finite]) Compute the (Moore-Penrose) pseudo-inverse of a matrix.
pinv2(a[, cond, rcond, return_rank, ...]) Compute the (Moore-Penrose) pseudo-inverse of a matrix.
pinvh(a[, cond, rcond, lower, return_rank, ...]) Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.
kron(a, b) Kronecker product.
tril(m[, k]) Make a copy of a matrix with elements above the k-th diagonal zeroed.
triu(m[, k]) Make a copy of a matrix with elements below the k-th diagonal zeroed.

Eigenvalue Problems

eig(a[, b, left, right, overwrite_a, ...]) Solve an ordinary or generalized eigenvalue problem of a square matrix.
eigvals(a[, b, overwrite_a, check_finite]) Compute eigenvalues from an ordinary or generalized eigenvalue problem.
eigh(a[, b, lower, eigvals_only, ...]) Solve an ordinary or generalized eigenvalue problem for a complex
eigvalsh(a[, b, lower, overwrite_a, ...]) Solve an ordinary or generalized eigenvalue problem for a complex
eig_banded(a_band[, lower, eigvals_only, ...]) Solve real symmetric or complex hermitian band matrix eigenvalue problem.
eigvals_banded(a_band[, lower, ...]) Solve real symmetric or complex hermitian band matrix eigenvalue problem.

Decompositions

lu(a[, permute_l, overwrite_a, check_finite]) Compute pivoted LU decompostion of a matrix.
lu_factor(a[, overwrite_a, check_finite]) Compute pivoted LU decomposition of a matrix.
lu_solve(lu_and_piv, b[, trans, ...]) Solve an equation system, a x = b, given the LU factorization of a
svd(a[, full_matrices, compute_uv, ...]) Singular Value Decomposition.
svdvals(a[, overwrite_a, check_finite]) Compute singular values of a matrix.
diagsvd(s, M, N) Construct the sigma matrix in SVD from singular values and size M, N.
orth(A) Construct an orthonormal basis for the range of A using SVD
cholesky(a[, lower, overwrite_a, check_finite]) Compute the Cholesky decomposition of a matrix.
cholesky_banded(ab[, overwrite_ab, lower, ...]) Cholesky decompose a banded Hermitian positive-definite matrix
cho_factor(a[, lower, overwrite_a, check_finite]) Compute the Cholesky decomposition of a matrix, to use in cho_solve
cho_solve(c_and_lower, b[, overwrite_b, ...]) Solve the linear equations A x = b, given the Cholesky factorization of A.
cho_solve_banded(cb_and_lower, b[, ...]) Solve the linear equations A x = b, given the Cholesky factorization of A.
polar(a[, side]) Compute the polar decomposition.
qr(a[, overwrite_a, lwork, mode, pivoting, ...]) Compute QR decomposition of a matrix.
qr_multiply(a, c[, mode, pivoting, ...]) Calculate the QR decomposition and multiply Q with a matrix.
qz(A, B[, output, lwork, sort, overwrite_a, ...]) QZ decompostion for generalized eigenvalues of a pair of matrices.
schur(a[, output, lwork, overwrite_a, sort, ...]) Compute Schur decomposition of a matrix.
rsf2csf(T, Z[, check_finite]) Convert real Schur form to complex Schur form.
hessenberg(a[, calc_q, overwrite_a, ...]) Compute Hessenberg form of a matrix.

See also

scipy.linalg.interpolative – Interpolative matrix decompositions

Matrix Functions

expm(A[, q]) Compute the matrix exponential using Pade approximation.
logm(A[, disp]) Compute matrix logarithm.
cosm(A) Compute the matrix cosine.
sinm(A) Compute the matrix sine.
tanm(A) Compute the matrix tangent.
coshm(A) Compute the hyperbolic matrix cosine.
sinhm(A) Compute the hyperbolic matrix sine.
tanhm(A) Compute the hyperbolic matrix tangent.
signm(A[, disp]) Matrix sign function.
sqrtm(A[, disp, blocksize]) Matrix square root.
funm(A, func[, disp]) Evaluate a matrix function specified by a callable.
expm_frechet(A, E[, method, compute_expm, ...]) Frechet derivative of the matrix exponential of A in the direction E.
expm_cond(A[, check_finite]) Relative condition number of the matrix exponential in the Frobenius norm.
fractional_matrix_power(A, t)

Matrix Equation Solvers

solve_sylvester(a, b, q) Computes a solution (X) to the Sylvester equation (AX + XB = Q).
solve_continuous_are(a, b, q, r) Solves the continuous algebraic Riccati equation, or CARE, defined
solve_discrete_are(a, b, q, r) Solves the disctrete algebraic Riccati equation, or DARE, defined as
solve_discrete_lyapunov(a, q) Solves the Discrete Lyapunov Equation (A’XA-X=-Q) directly.
solve_lyapunov(a, q) Solves the continuous Lyapunov equation (AX + XA^H = Q) given the values

Special Matrices

block_diag(*arrs) Create a block diagonal matrix from provided arrays.
circulant(c) Construct a circulant matrix.
companion(a) Create a companion matrix.
dft(n[, scale]) Discrete Fourier transform matrix.
hadamard(n[, dtype]) Construct a Hadamard matrix.
hankel(c[, r]) Construct a Hankel matrix.
hilbert(n) Create a Hilbert matrix of order n.
invhilbert(n[, exact]) Compute the inverse of the Hilbert matrix of order n.
leslie(f, s) Create a Leslie matrix.
pascal(n[, kind, exact]) Returns the n x n Pascal matrix.
toeplitz(c[, r]) Construct a Toeplitz matrix.
tri(N[, M, k, dtype]) Construct (N, M) matrix filled with ones at and below the k-th diagonal.

Low-level routines

get_blas_funcs(names[, arrays, dtype]) Return available BLAS function objects from names.
get_lapack_funcs(names[, arrays, dtype]) Return available LAPACK function objects from names.
find_best_blas_type([arrays, dtype]) Find best-matching BLAS/LAPACK type.

See also

scipy.linalg.blas – Low-level BLAS functions

scipy.linalg.lapack – Low-level LAPACK functions