Matrix Permutation Optimization

Matrix Permutation Optimization is a computational method that belongs to the class of matrix reorganization techniques, specifically distinguished by its objective to rearrange the rows and columns of an adjacency matrix through permutation operations in order to optimize a defined structural or visual quality criterion.

32

Publications

2.9K

Citations

90

Authors

58

Institutions

Publications per year

2017–2026

7

Authors

90

  1. JK
    Jacko Koster

    Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique, France

    2 publications · 361 citations

    2

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  2. MJ
    Michael J. Brusco

    Florida State University, United States

    2 publications · 91 citations

    2

    h-index

  3. ID
    Iain Duff

    Rutherford Appleton Laboratory, United Kingdom

    2 publications · 664 citations

    2

    h-index

  4. CC
    Călin-Adrian Comes

    Institute of National Economy, Romania

    1 publications · 19 citations

    1

    h-index

  5. GK
    G.J. Knudsen

    UNSW Sydney, Australia

    1 publications · 18 citations

    1

    h-index

1–5 of 90

Venues

  1. Signal Processing

    2 publications · 127 citations

    2

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  2. Discrete Applied Mathematics

    2 publications · 55 citations

    2

    h-index

  3. Mathematics of Operations Research

    1 publications · 39 citations

    1

    h-index

  4. Journal of Parallel and Distributed Computing

    1 publications · 39 citations

    1

    h-index

  5. BMC Bioinformatics

    1 publications · 63 citations

    1

    h-index

1–5