Linear algebra (cupy.linalg)

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NumPy API Reference: Linear algebra (numpy.linalg)

See also

Linear algebra (cupyx.scipy.linalg)

Matrix and vector products

dot(a, b[, out])	Returns a dot product of two arrays.
vdot(a, b)	Returns the dot product of two vectors.
inner(a, b)	Returns the inner product of two arrays.
outer(a, b[, out])	Returns the outer product of two vectors.
matmul	matmul(x1, x2, /, out=None, **kwargs)
tensordot(a, b[, axes])	Returns the tensor dot product of two arrays along specified axes.
einsum(subscripts, *operands[, dtype, optimize])	Evaluates the Einstein summation convention on the operands.
linalg.matrix_power(M, n)	Raise a square matrix to the (integer) power n.
kron(a, b)	Returns the kronecker product of two arrays.

Decompositions

linalg.cholesky(a)	Cholesky decomposition.
linalg.qr(a[, mode])	QR decomposition.
linalg.svd(a[, full_matrices, compute_uv])	Singular Value Decomposition.

Matrix eigenvalues

linalg.eigh(a[, UPLO])	Return the eigenvalues and eigenvectors of a complex Hermitian (conjugate symmetric) or a real symmetric matrix.
linalg.eigvalsh(a[, UPLO])	Compute the eigenvalues of a complex Hermitian or real symmetric matrix.

Norms and other numbers

linalg.norm(x[, ord, axis, keepdims])	Returns one of matrix norms specified by ord parameter.
linalg.det(a)	Returns the determinant of an array.
linalg.matrix_rank(M[, tol])	Return matrix rank of array using SVD method
linalg.slogdet(a)	Returns sign and logarithm of the determinant of an array.
trace(a[, offset, axis1, axis2, dtype, out])	Returns the sum along the diagonals of an array.

Solving equations and inverting matrices

linalg.solve(a, b)	Solves a linear matrix equation.
linalg.tensorsolve(a, b[, axes])	Solves tensor equations denoted by ax = b.
linalg.lstsq(a, b[, rcond])	Return the least-squares solution to a linear matrix equation.
linalg.inv(a)	Computes the inverse of a matrix.
linalg.pinv(a[, rcond])	Compute the Moore-Penrose pseudoinverse of a matrix.
linalg.tensorinv(a[, ind])	Computes the inverse of a tensor.