| aslinearoperator(A) |
Return A as a LinearOperator. [extrait de aslinearoperator.__doc__] |
| bicg(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None) |
Use BIConjugate Gradient iteration to solve ``Ax = b``. [extrait de bicg.__doc__] |
| bicgstab(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None) |
Use BIConjugate Gradient STABilized iteration to solve ``Ax = b``. [extrait de bicgstab.__doc__] |
| cg(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None) |
Use Conjugate Gradient iteration to solve ``Ax = b``. [extrait de cg.__doc__] |
| cgs(A, b, x0=None, tol=1e-05, maxiter=None, M=None, callback=None, atol=None) |
Use Conjugate Gradient Squared iteration to solve ``Ax = b``. [extrait de cgs.__doc__] |
| eigs(A, k=6, M=None, sigma=None, which='LM', v0=None, ncv=None, maxiter=None, tol=0, return_eigenvectors=True, Minv=None, OPinv=None, OPpart=None) |
|
| eigsh(A, k=6, M=None, sigma=None, which='LM', v0=None, ncv=None, maxiter=None, tol=0, return_eigenvectors=True, Minv=None, OPinv=None, mode='normal') |
|
| expm(A) |
|
| expm_multiply(A, B, start=None, stop=None, num=None, endpoint=None) |
|
| factorized(A) |
|
| gcrotmk(A, b, x0=None, tol=1e-05, maxiter=1000, M=None, callback=None, m=20, k=None, CU=None, discard_C=False, truncate='oldest', atol=None) |
|
| gmres(A, b, x0=None, tol=1e-05, restart=None, maxiter=None, M=None, callback=None, restrt=None, atol=None, callback_type=None) |
|
| inv(A) |
|
| lgmres(A, b, x0=None, tol=1e-05, maxiter=1000, M=None, callback=None, inner_m=30, outer_k=3, outer_v=None, store_outer_Av=True, prepend_outer_v=False, atol=None) |
|
| lobpcg(A, X, B=None, M=None, Y=None, tol=None, maxiter=None, largest=True, verbosityLevel=0, retLambdaHistory=False, retResidualNormsHistory=False) |
Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG) [extrait de lobpcg.__doc__] |
| lsmr(A, b, damp=0.0, atol=1e-06, btol=1e-06, conlim=100000000.0, maxiter=None, show=False, x0=None) |
Iterative solver for least-squares problems. [extrait de lsmr.__doc__] |
| lsqr(A, b, damp=0.0, atol=1e-08, btol=1e-08, conlim=100000000.0, iter_lim=None, show=False, calc_var=False, x0=None) |
Find the least-squares solution to a large, sparse, linear system [extrait de lsqr.__doc__] |
| minres(A, b, x0=None, shift=0.0, tol=1e-05, maxiter=None, M=None, callback=None, show=False, check=False) |
|
| norm(x, ord=None, axis=None) |
|
| onenormest(A, t=2, itmax=5, compute_v=False, compute_w=False) |
|
| qmr(A, b, x0=None, tol=1e-05, maxiter=None, M1=None, M2=None, callback=None, atol=None) |
Use Quasi-Minimal Residual iteration to solve ``Ax = b``. [extrait de qmr.__doc__] |
| spilu(A, drop_tol=None, fill_factor=None, drop_rule=None, permc_spec=None, diag_pivot_thresh=None, relax=None, panel_size=None, options=None) |
|
| splu(A, permc_spec=None, diag_pivot_thresh=None, relax=None, panel_size=None, options={}) |
|
| spsolve(A, b, permc_spec=None, use_umfpack=True) |
Solve the sparse linear system Ax=b, where b may be a vector or a matrix. [extrait de spsolve.__doc__] |
| spsolve_triangular(A, b, lower=True, overwrite_A=False, overwrite_b=False, unit_diagonal=False) |
|
| svds(A, k=6, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack') |
Compute the largest or smallest k singular values/vectors for a sparse matrix. The order of the singular values is not guaranteed. [extrait de svds.__doc__] |
| test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, tests=None, parallel=None) |
|
| use_solver(**kwargs) |
|
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