Linear Algebra#

Wrappers for numpy.linalg.

Matrix and vector products#

einsum(dims, *operands[, keep_dims, ...])

Preprocess inputs to call numpy.einsum or numpy.einsum_path.

raw_einsum(subscripts, *operands[, ...])

Wrap numpy.einsum crudely.

einsum_path(dims, *operands[, keep_dims, ...])

Wrap numpy.einsum_path.

matmul(da, db[, dims, out_append])

Wrap numpy.linalg.matmul.

linalg.matrix_transpose(da, dims)

Transpose the underlying matrix without modifying the dimensions.

linalg.matrix_power(da, n[, dims])

Wrap numpy.linalg.matrix_power.


cholesky(da[, dims])

Wrap numpy.linalg.cholesky.

qr(da[, dims, mode, out_append])

Wrap numpy.linalg.qr.

svd(da[, dims, full_matrices, compute_uv, ...])

Wrap numpy.linalg.svd.

Matrix eigenvalues#

eig(da[, dims])

Wrap numpy.linalg.eig.

eigh(da[, dims, UPLO])

Wrap numpy.linalg.eigh.

eigvals(da[, dims])

Wrap numpy.linalg.eigvals.

eigvalsh(da[, dims, UPLO])

Wrap numpy.linalg.eigvalsh.

Norms and other numbers#

norm(da[, dims, ord])

Wrap numpy.linalg.norm.

cond(da[, dims, p])

Wrap numpy.linalg.cond.

det(da[, dims])

Wrap numpy.linalg.det.

matrix_rank(da[, dims, tol, hermitian])

Wrap numpy.linalg.matrix_rank.

slogdet(da[, dims])

Wrap numpy.linalg.slogdet.

trace(da[, dims, offset, dtype, out])

Wrap numpy.trace.

Solving equations and inverting matrices#

solve(da, db[, dims])

Wrap numpy.linalg.solve.

inv(da[, dims])

Wrap numpy.linalg.inv.

Convenience functions#

get_default_dims(da1_dims[, d2_dims])

Get the dimensions corresponding to the matrices.