# 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.

## Matrix Functions#

 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. Construct a circulant matrix. Create a companion matrix. `convolution_matrix`(a, n[, mode]) Construct a convolution matrix. `dft`(n[, scale]) Discrete Fourier transform matrix. 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`. 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.