Linear algebra (cupyx.scipy.linalg)

Hint

SciPy API Reference: Linear algebra (scipy.linalg)

Basics

solve_triangular(a, b[, trans, lower, ...])	Solve the equation a x = b for x, assuming a is a triangular matrix.
tril(m[, k])	Make a copy of a matrix with elements above the k-th diagonal zeroed.
triu(m[, k])	Make a copy of a matrix with elements below the k-th diagonal zeroed.

Matrix Functions

expm(a)

Compute the matrix exponential.

Decompositions

lu(a[, permute_l, overwrite_a, check_finite])	LU decomposition.
lu_factor(a[, overwrite_a, check_finite])	LU decomposition.
lu_solve(lu_and_piv, b[, trans, ...])	Solve an equation system, a * x = b, given the LU factorization of a

Special Matrices

block_diag(*arrs)	Create a block diagonal matrix from provided arrays.
circulant(c)	Construct a circulant matrix.
companion(a)	Create a companion matrix.
convolution_matrix(a, n[, mode])	Construct a convolution matrix.
dft(n[, scale])	Discrete Fourier transform matrix.
fiedler(a)	Returns a symmetric Fiedler matrix
fiedler_companion(a)	Returns a Fiedler companion matrix
hadamard(n[, dtype])	Construct an Hadamard matrix.
hankel(c[, r])	Construct a Hankel matrix.
helmert(n[, full])	Create an Helmert matrix of order n.
hilbert(n)	Create a Hilbert matrix of order n.
kron(a, b)	Kronecker product.
leslie(f, s)	Create a Leslie matrix.
toeplitz(c[, r])	Construct a Toeplitz matrix.
tri(N[, M, k, dtype])	Construct (N, M) matrix filled with ones at and below the k-th diagonal.