scipy.sparse.linalg¶
Sparse Linear Algebra¶
- The submodules of sparse.linalg:
- eigen: sparse eigenvalue problem solvers
- isolve: iterative methods for solving linear systems
- dsolve: direct factorization methods for solving linear systems
Examples¶
Functions
| aslinearoperator(A) | Return A as a LinearOperator. |
| 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 |
| 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 A. |
| factorized(A) | Return a fuction for solving a sparse linear system, with A pre-factorized. |
| gmres(A, b[, x0, tol, restart, maxiter, ...]) | Use Generalized Minimal RESidual iteration to solve A x = b. |
| isspmatrix(x) | |
| lgmres(A, b[, x0, tol, maxiter, M, ...]) | Solve a matrix equation using the LGMRES algorithm. |
| lobpcg(A, X[, B, M, Y, tol, maxiter, ...]) | Solve symmetric partial eigenproblems with optional preconditioning |
| lsqr(A, b[, damp, atol, btol, conlim, ...]) | Find the least-squares solution to a large, sparse, linear system of equations. |
| 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 |
| spilu(A[, drop_tol, fill_factor, drop_rule, ...]) | Compute an incomplete LU decomposition for a sparse, square matrix A. |
| splu(A[, permc_spec, diag_pivot_thresh, ...]) | Compute the LU decomposition of a sparse, square matrix. |
| spsolve(A, b[, permc_spec, use_umfpack]) | Solve the sparse linear system Ax=b |
| svds(A[, k, ncv, tol]) | Compute k singular values/vectors for a sparse matrix using ARPACK. |
| use_solver(**kwargs) | Valid keyword arguments with defaults (other ignored): |
Classes
| LinearOperator(shape, matvec[, rmatvec, ...]) | Common interface for performing matrix vector products |
| Tester | Nose test runner. |
| csc_matrix(arg1[, shape, dtype, copy]) | Compressed Sparse Column matrix |
| csr_matrix(arg1[, shape, dtype, copy]) | Compressed Sparse Row matrix |
Exceptions
| ArpackError(info[, infodict]) | ARPACK error |
| ArpackNoConvergence(msg, eigenvalues, ...) | ARPACK iteration did not converge |
