Sparse linear algebra (`cupyx.scipy.sparse.linalg`)#

Hint

Abstract linear operators#

`LinearOperator`(shape, matvec[, rmatvec, ...])	Common interface for performing matrix vector products
`aslinearoperator`(A)	Return A as a LinearOperator.

norm(x[, ord, axis])

Norm of a cupy.scipy.spmatrix

Direct methods for linear equation systems:

`spsolve`(A, b)	Solves a sparse linear system `A x = b`
`spsolve_triangular`(A, b[, lower, ...])	Solves a sparse triangular system `A x = b`.
`factorized`(A)	Return a function for solving a sparse linear system, with A pre-factorized.

Iterative methods for linear equation systems:

`cg`(A, b[, x0, rtol, atol, maxiter, M, callback])	Uses Conjugate Gradient iteration to solve `Ax = b`.
`gmres`(A, b[, x0, rtol, atol, restart, ...])	Uses Generalized Minimal RESidual iteration to solve `Ax = b`.
`cgs`(A, b[, x0, rtol, atol, maxiter, M, callback])	Use Conjugate Gradient Squared iteration to solve `Ax = b`.
`minres`(A, b[, x0, shift, tol, maxiter, M, ...])	Uses MINimum RESidual iteration to solve `Ax = b`.

Iterative methods for least-squares problems:

`lsqr`(A, b)	Solves linear system with QR decomposition.
`lsmr`(A, b[, x0, damp, atol, btol, conlim, ...])	Iterative solver for least-squares problems.

Eigenvalue problems:

`eigsh`(a[, k, which, v0, ncv, maxiter, tol, ...])	Find `k` eigenvalues and eigenvectors of the real symmetric square matrix or complex Hermitian matrix `A`.
`lobpcg`(A, X[, B, M, Y, tol, maxiter, ...])	Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG)

Singular values problems:

svds(a[, k, ncv, tol, which, maxiter, ...])

Finds the largest k singular values/vectors for a sparse matrix.

Complete or incomplete LU factorizations:

`splu`(A[, permc_spec, diag_pivot_thresh, ...])	Computes the LU decomposition of a sparse square matrix.
`spilu`(A[, drop_tol, fill_factor, drop_rule, ...])	Computes the incomplete LU decomposition of a sparse square matrix.
`SuperLU`(obj)