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Optimal Scheduling of the Leaves of a Tree and the SVO Frequencies of Languages Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Maximizing the Eigenvalue-Gap and Promoting Sparsity of Doubly Stochastic Matrices with PSO

Authors : Panos K. Syriopoulos, Nektarios G. Kalampalikis, Michael N. Vrahatis

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

The eigenvalue-gap of doubly stochastic matrices with sparsity constraints is maximized using the unified particle swarm optimizer. This is possible through the use of an iterative normalization procedure that maps the search space of the swarm to the set of doubly stochastic matrices with given sparsity pattern. We extend the method to the problem of finding doubly-stochastic matrices of given dimensions that are as sparse as possible, and attain a given eigenvalue-gap target.

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Metadata
Title
Maximizing the Eigenvalue-Gap and Promoting Sparsity of Doubly Stochastic Matrices with PSO
Authors
Panos K. Syriopoulos
Nektarios G. Kalampalikis
Michael N. Vrahatis
Copyright Year
2022
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-031-24865-8

Electronic ISBN: 978-3-031-24866-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-24866-5_38

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