Generate Test Matrices for Numerical Experiments
Create binomial matrix
Create Cauchy matrix
Create Chebyshev spectral differentiation matrix
Creating Vandermonde-like matrix for the Chebyshev polynomials
Creating singular Toeplitz lower Hessenberg matrix
Create circulant matrix
Create Clement tridiagonal matrix with zero diagonal entries
Create comparison matrix A
Create matrix A whose columns repeat cyclically
Create Dorr matrix
Create anti-Hadamard matrix A
Create Fiedler matrix
Create Forsythe matrix or perturbed Jordan block
Frank matrix of order N
Create Toeplitz matrix with sensitive eigenvalues
Hanowa matrix
Involutory matrix (a matrix that is its own inverse)
Create Jordan block matrix
Create Lauchli Matrix
Create Lehmer matrix
Create Leslie population model matrix
Symmetric positive definite matrix MIN(i,j)
Create sparse diagonal matrix
Create sparse tridiagonal matrix
Generates a variety of structured test matrices commonly used in numerical linear algebra and computational experiments. Includes well-known matrices for benchmarking and testing the performance, stability, and accuracy of linear algebra algorithms. Inspired by 'MATLAB' 'gallery' functions.