LinearOperator(shape, matvec[, rmatvec, ...]) | Common interface for performing matrix vector products |
aslinearoperator(A) | Return A as a LinearOperator. |
inv(A) | Compute the inverse of a sparse matrix .. |
expm(A) | Compute the matrix exponential using Pade approximation. |
Direct methods for linear equation systems:
spsolve(A, b[, permc_spec, use_umfpack]) | Solve the sparse linear system Ax=b, where b may be a vector or a matrix. |
factorized(A) | Return a fuction for solving a sparse linear system, with A pre-factorized. |
Iterative methods for linear equation systems:
bicg(A, b[, x0, tol, maxiter, xtype, M, ...]) | Use BIConjugate Gradient iteration to solve A x = b |
bicgstab(A, b[, x0, tol, maxiter, xtype, M, ...]) | Use BIConjugate Gradient STABilized iteration to solve A x = b |
cg(A, b[, x0, tol, maxiter, xtype, M, callback]) | Use Conjugate Gradient iteration to solve A x = b |
cgs(A, b[, x0, tol, maxiter, xtype, M, callback]) | Use Conjugate Gradient Squared iteration to solve A x = b |
gmres(A, b[, x0, tol, restart, maxiter, ...]) | Use Generalized Minimal RESidual iteration to solve A x = b. |
lgmres(A, b[, x0, tol, maxiter, M, ...]) | Solve a matrix equation using the LGMRES algorithm. |
minres(A, b[, x0, shift, tol, maxiter, ...]) | Use MINimum RESidual iteration to solve Ax=b |
qmr(A, b[, x0, tol, maxiter, xtype, M1, M2, ...]) | Use Quasi-Minimal Residual iteration to solve A x = b |
Iterative methods for least-squares problems:
lsqr(A, b[, damp, atol, btol, conlim, ...]) | Find the least-squares solution to a large, sparse, linear system of equations. |
lsmr(A, b[, damp, atol, btol, conlim, ...]) | Iterative solver for least-squares problems. |
Eigenvalue problems:
eigs(A[, k, M, sigma, which, v0, ncv, ...]) | Find k eigenvalues and eigenvectors of the square matrix A. |
eigsh(A[, k, M, sigma, which, v0, ncv, ...]) | Find k eigenvalues and eigenvectors of the real symmetric square matrix |
lobpcg(A, X[, B, M, Y, tol, maxiter, ...]) | Solve symmetric partial eigenproblems with optional preconditioning |
Singular values problems:
svds(A[, k, ncv, tol, which, v0, maxiter, ...]) | Compute the largest k singular values/vectors for a sparse matrix. |
Complete or incomplete LU factorizations
splu(A[, permc_spec, diag_pivot_thresh, ...]) | Compute the LU decomposition of a sparse, square matrix. |
spilu(A[, drop_tol, fill_factor, drop_rule, ...]) | Compute an incomplete LU decomposition for a sparse, square matrix. |
ArpackNoConvergence(msg, eigenvalues, ...) | ARPACK iteration did not converge |
ArpackError(info[, infodict]) | ARPACK error |