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