Tessero\Sparse\Linalg\LaplacianNd

__construct(array $gridShape, string $boundaryConditions = 'neumann')

shape(): array

ndim(): int

boundaryConditions(): string

gridShape(): array

toarray(): array

Dense array of the Laplacian (Kronecker sum of 1-D Laplacians). Values are exact integers represented as floats.

tosparse(): array

Dense array of the sparse representation. The LaplacianNd sparse form is identical to its dense form, so tosparse()->toarray() == toarray().

eigenvalues(?int $m = null): array

Closed-form eigenvalues in ascending order. If $m is given, the $m largest (least negative) eigenvalues are returned (scipy returns the m smallest in absolute value, i.e. the largest algebraically, still ascending).

matvec(array $x): array

Matrix-vector product L x via the dense array.

rmatvec(array $y): array

Adjoint == matvec (L is symmetric).

matmat(array $X): array

Matrix-matrix product L X (X row-major, shape (p, k)).

dot(array $x): array

asLinearOperator(): Tessero\Sparse\Linalg\LinearOperator

Return this Laplacian as a generic LinearOperator (wrapping its dense array).