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3 results

  • 8 - Special Matrices, Markov Chains, and PageRank

  • Jeffrey A. Fessler, University of Michigan, Ann Arbor, Raj Rao Nadakuditi, University of Michigan, Ann Arbor
  • Book:
    Linear Algebra for Data Science, Machine Learning, and Signal Processing
    Published online:
    01 November 2024
    Print publication:
    16 May 2024, pp 283-334

  • Summary

    This chapter contains topics related to matrices with special structures that arise in many applications. It discusses companion matrices that are a classic linear algebra topic. It constructs circulant matrices from a particular companion matrix and describes their signal processing applications. It discusses the closely related family of Toeplitz matrices. It describes the power iteration that is used later in the chapter for Markov chains. It discusses nonnegative matrices and their relationships to graphs, leading to the analysis of Markov chains. The chapter ends with two applications: Google’s PageRank method and spectral clustering using graph Laplacians.

  • 14 - Normal Matrices and the Spectral Theorem

  • Stephan Ramon Garcia, Pomona College, California, Roger A. Horn, University of Utah
  • Book:
    Matrix Mathematics
    Published online:
    29 September 2023
    Print publication:
    25 May 2023, pp 277-300

  • Summary

    Chapter 14: Every square complex matrix is unitarily similar to an upper triangular matrix, but which matrices are unitarily similar to a diagonal matrix? The answer is the main result of this chapter: the spectral theorem for normal matrices. Hermitian, skew-Hermitian, unitary, and circulant matrices are unitarily diagonalizable. As a consequence, they have special properties that we investigate in this and following chapters.