web_matrix


web_matrix, a Python code which stores sample matrices describing a web page network. These matrices are typically very sparse, and the examples here are stored using the sparse triplet (ST) format. They can be used to demonstrate pagerank and other graph algorithms.

Licensing:

The information on this web page is distributed under the MIT license.

Languages:

web_matrix is available in a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

chebyshev_matrix, a Python code which defines the Chebyshev differentiation matrix.

jordan_matrix, a Python code which returns a random matrix in Jordan canonical form.

levenshtein_matrix, a Python code which returns the Levenshtein distance matrix defined by two strings.

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risk_matrix, a Python code which computes the transition and adjacency matrix for the game of RISK.

snakes_matrix, a Python code which computes the transition matrix for Snakes and Ladders.

sparse_test, a Python code which illustrates the use of sparse matrix utilities;

st, a data directory which illustrates the sparse triplet (ST) format for storing sparse matrices.

tennis_matrix, a Python code which computes the transition matrix for a game of tennis, which has 17 distinct states.

test_matrix, a Python code which defines test matrices for which the condition number, determinant, eigenvalues, eigenvectors, inverse, null vectors, P*L*U factorization or linear system solution are known. Examples include the Fibonacci, Hilbert, Redheffer, Vandermonde, Wathen and Wilkinson matrices.

usa_matrix, a Python code which defines the adjacency matrix for US states, using a variety of matrix formats.

wathen_matrix, a Python code which compares storage schemes (full, banded, sparse triplet, sparse) and solution strategies (A\x, linpack, conjugate gradient (CG)) for linear systems involving the Wathen matrix, which can arise when solving a problem using the finite element method (FEM).

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Reference:

  1. John MacCormick,
    Nine Algorithms That Changed the Future: The Ingenious Ideas that Drive Today's Computers,
    Princeton University Press,
    ISBN-13: 978-0691158198.
  2. Cleve Moler,
    Experiments with Matlab,
    Chapter 7: Google PageRank
    https://www.mathworks.com/moler/exm/chapters/pagerank.pdf
  3. Xindong Wu, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey McLachlan, Angus Ng, Bing Lu, Philip Yu, Zhi-Hua Zhou, Michael Steinbach, David Hand, Dan Steinberg,
    Top 10 algorithms in data mining,
    Knowledge and Information Systems,
    Volume 14, Number 1, January 2008, pages 1-37.

Source Code:


Last modified on 27 September 2022.