Create a Chebyshev spectral differentiation matrix.
Create a Vandermonde-like matrix for the Chebyshev polynomials.
Create a Chow matrix – a singular Toeplitz lower Hessenberg matrix.
Create a tridiagonal matrix with zero diagonal entries.
Create a ‘counterexample’ matrix to a condition estimator.
Create a matrix whose columns repeat cyclically.
Create a diagonally dominant, ill-conditioned, tridiagonal matrix.
Create a (0, 1) matrix whose inverse has large integer entries.
Create a Forsythe matrix (a perturbed Jordan block).
Create a Frank matrix (ill-conditioned eigenvalues).
Create a greatest common divisor matrix.
c is an n-by-n matrix whose values correspond to the
greatest common divisor of its coordinate values, i.e., c(i,j)
correspond gcd (i, j)
.
Create a Gear matrix.
Create a Toeplitz matrix with sensitive eigenvalues.
Create a matrix whose eigenvalues lie on a vertical line in the complex plane.
Create a matrix with random integers in the range [1, imax]. If imin is given then the integers are in the range [imin, imax].
The second input is a matrix of dimensions describing the size of the output. The dimensions can also be input as comma-separated arguments.
The input j is an integer index in the range [0, 2^32-1]. The values of the output matrix are always exactly the same (reproducibility) for a given size input and j index.
The final optional argument determines the class of the resulting matrix.
Possible values for class: "uint8"
, "uint16"
,
"uint32"
, "int8"
, "int16"
, int32", "single"
,
"double"
. The default is "double"
.
Create the inverse of an upper Hessenberg matrix.
Create a Hankel matrix with factorial elements.
Create a Kahan matrix (upper trapezoidal).
Create a Krylov matrix.
Create a Lauchli matrix (rectangular).
Create a Moler matrix (symmetric positive definite).
Create a singular matrix from the discrete Neumann problem (sparse).
Create a matrix with random samples from the standard normal distribution (mean = 0, std = 1).
The first input is a matrix of dimensions describing the size of the output. The dimensions can also be input as comma-separated arguments.
The input j is an integer index in the range [0, 2^32-1]. The values of the output matrix are always exactly the same (reproducibility) for a given size input and j index.
The final optional argument determines the class of the resulting matrix.
Possible values for class: "single"
, "double"
.
The default is "double"
.
Create orthogonal and nearly orthogonal matrices.
Create a Parter matrix (a Toeplitz matrix with singular values near pi).
Create a prolate matrix (symmetric, ill-conditioned Toeplitz matrix).
Create a random matrix with elements -1, 0 or 1.
Create a random matrix with pre-assigned singular values.
Create a zero and ones matrix of Redheffer associated with the Riemann hypothesis.
Create a complex matrix, with a ‘smoke ring’ pseudospectrum.
Create a symmetric positive definite Toeplitz matrix.
Create a pentadiagonal Toeplitz matrix (sparse).
Create a tridiagonal matrix (sparse).
Create an upper triangular matrix discussed by Kahan, Golub, and Wilkinson.
Create a matrix with random samples from the standard uniform distribution (range [0,1]).
The first input is a matrix of dimensions describing the size of the output. The dimensions can also be input as comma-separated arguments.
The input j is an integer index in the range [0, 2^32-1]. The values of the output matrix are always exactly the same (reproducibility) for a given size input and j index.
The final optional argument determines the class of the resulting matrix.
Possible values for class: "single"
, "double"
.
The default is "double"
.
Package: octave