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- """
- Sparse linear algebra (:mod:`scipy.sparse.linalg`)
- ==================================================
- .. currentmodule:: scipy.sparse.linalg
- Abstract linear operators
- -------------------------
- .. autosummary::
- :toctree: generated/
- LinearOperator -- abstract representation of a linear operator
- aslinearoperator -- convert an object to an abstract linear operator
- Matrix Operations
- -----------------
- .. autosummary::
- :toctree: generated/
- inv -- compute the sparse matrix inverse
- expm -- compute the sparse matrix exponential
- expm_multiply -- compute the product of a matrix exponential and a matrix
- funm_multiply_krylov -- use a Krylov method to compute f(A)b for a general f
- matrix_power -- compute the matrix power by raising a matrix to an exponent
- Matrix norms
- ------------
- .. autosummary::
- :toctree: generated/
- norm -- Norm of a sparse matrix
- onenormest -- Estimate the 1-norm of a sparse matrix
- Solving linear problems
- -----------------------
- Direct methods for linear equation systems:
- .. autosummary::
- :toctree: generated/
- spsolve -- Solve the sparse linear system Ax=b
- spsolve_triangular -- Solve sparse linear system Ax=b for a triangular A.
- is_sptriangular -- Check if sparse A is triangular.
- spbandwidth -- Find the bandwidth of a sparse matrix.
- factorized -- Pre-factorize matrix to a function solving a linear system
- MatrixRankWarning -- Warning on exactly singular matrices
- use_solver -- Select direct solver to use
- Iterative methods for linear equation systems:
- .. autosummary::
- :toctree: generated/
- bicg -- Use BIConjugate Gradient iteration to solve Ax = b
- bicgstab -- Use BIConjugate Gradient STABilized iteration to solve Ax = b
- cg -- Use Conjugate Gradient iteration to solve Ax = b
- cgs -- Use Conjugate Gradient Squared iteration to solve Ax = b
- gmres -- Use Generalized Minimal RESidual iteration to solve Ax = b
- lgmres -- Solve a matrix equation using the LGMRES algorithm
- minres -- Use MINimum RESidual iteration to solve Ax = b
- qmr -- Use Quasi-Minimal Residual iteration to solve Ax = b
- gcrotmk -- Solve a matrix equation using the GCROT(m,k) algorithm
- tfqmr -- Use Transpose-Free Quasi-Minimal Residual iteration to solve Ax = b
- Iterative methods for least-squares problems:
- .. autosummary::
- :toctree: generated/
- lsqr -- Find the least-squares solution to a sparse linear equation system
- lsmr -- Find the least-squares solution to a sparse linear equation system
- Matrix factorizations
- ---------------------
- Eigenvalue problems:
- .. autosummary::
- :toctree: generated/
- eigs -- Find k eigenvalues and eigenvectors of the square matrix A
- eigsh -- Find k eigenvalues and eigenvectors of a symmetric matrix
- lobpcg -- Solve symmetric partial eigenproblems with optional preconditioning
- Singular values problems:
- .. autosummary::
- :toctree: generated/
- svds -- Compute k singular values/vectors for a sparse matrix
- The `svds` function supports the following solvers:
- .. toctree::
- sparse.linalg.svds-arpack
- sparse.linalg.svds-lobpcg
- sparse.linalg.svds-propack
- Complete or incomplete LU factorizations
- .. autosummary::
- :toctree: generated/
- splu -- Compute a LU decomposition for a sparse matrix
- spilu -- Compute an incomplete LU decomposition for a sparse matrix
- SuperLU -- Object representing an LU factorization
- Sparse arrays with structure
- ----------------------------
- .. autosummary::
- :toctree: generated/
- LaplacianNd -- Laplacian on a uniform rectangular grid in ``N`` dimensions
- Exceptions
- ----------
- .. autosummary::
- :toctree: generated/
- ArpackNoConvergence
- ArpackError
- """
- from ._isolve import *
- from ._dsolve import *
- from ._interface import *
- from ._eigen import *
- from ._matfuncs import *
- from ._onenormest import *
- from ._norm import *
- from ._expm_multiply import *
- from ._funm_multiply_krylov import *
- from ._special_sparse_arrays import *
- # Deprecated namespaces, to be removed in v2.0.0
- from . import isolve, dsolve, interface, eigen, matfuncs
- __all__ = [s for s in dir() if not s.startswith('_')]
- from scipy._lib._testutils import PytestTester
- test = PytestTester(__name__)
- del PytestTester
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