src.array.linalg package

Submodules

src.array.linalg.eigen_decomp module

src.array.linalg.eigen_decomp.eigen_decomp(arr, ncomp=None, eigen_solver='dense', is_symmetric=False, largest=True, **kwargs)

Compute the eigenvalue decomposition of a matrix using various solvers.

Parameters:

  • arr (DenseArray) – Array of input points. Can be sparse or dense.

  • ncomp (int | None) – Optional number of eigenvalues and eigenvectors to return. If None, all are returned. (default: None)

  • eigen_solver (Literal['dense', 'arpack', 'lobpcg', 'amg']) – Which solver to use. One of {“dense”, “arpack”, “amg”, “lobpcg”}. (default: “dense”)

  • is_symmetric (bool) – Whether the matrix is symmetric/Hermitian (uses optimized solvers if True). (default: False)

  • largest (bool) – If True, return the largest eigenvalues. Otherwise, return the smallest. (default: True)

  • kwargs (Any) – Additional keyword arguments passed to the underlying solver functions.

Returns:

Tuple of eigenvalues and eigenvectors.

Return type:

Tuple[DenseArray, DenseArray]

Module contents

src.array.linalg.eigen_decomp(arr, ncomp=None, eigen_solver='dense', is_symmetric=False, largest=True, **kwargs)

Compute the eigenvalue decomposition of a matrix using various solvers.

Parameters:

  • arr (DenseArray) – Array of input points. Can be sparse or dense.

  • ncomp (int | None) – Optional number of eigenvalues and eigenvectors to return. If None, all are returned. (default: None)

  • eigen_solver (Literal['dense', 'arpack', 'lobpcg', 'amg']) – Which solver to use. One of {“dense”, “arpack”, “amg”, “lobpcg”}. (default: “dense”)

  • is_symmetric (bool) – Whether the matrix is symmetric/Hermitian (uses optimized solvers if True). (default: False)

  • largest (bool) – If True, return the largest eigenvalues. Otherwise, return the smallest. (default: True)

  • kwargs (Any) – Additional keyword arguments passed to the underlying solver functions.

Returns:

Tuple of eigenvalues and eigenvectors.

Return type:

Tuple[DenseArray, DenseArray]