# Linear algebra (cupyx.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.

## 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 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.