Tessero\Sparse\Linalg\SuperLU

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__construct

__construct(array $A, string $splinalg)

splu static

splu(array $A, string $splinalg): self

Factorise a square matrix for repeated solves (scipy.sparse.linalg.splu).

factorized static

factorized(array $A, string $splinalg): Tessero\Sparse\Linalg\Factorized

Return a pre-factorised solve callable (scipy.sparse.linalg.factorized).

shape

shape(): array

Number of rows/columns: (n, n). @return array{int, int}

solve

solve(array $b): array

Solve A x = b (scipy SuperLU.solve). b may be a vector (n) or a matrix (n x k); the result has the matching shape. Only trans=0 (A x = b) is supported for the real symmetric parity surface here.