Parallel Evolutionary Computation in Very Large Scale Eigenvalue Problems

The history of research on eigenvalue problems is rich with many outstanding contributions. Nonetheless, the rapidly increasing size of data sets requires new algorithms for old problems in the context of extremely large matrix dimensions. This paper reports on a new method for finding eigenvalues o...

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Bibliographic Details
Published in:arXiv.org 2013-12
Main Authors: Dashti, Hesam T, Siahpirani, Alireza F, Wang, Liya, Kloc, Mary, Assadi, Amir H
Format: Article
Language:English
Subjects:
Algorithms
Eigenvalues
Evolutionary algorithms
Evolutionary computation
Parallel programming
Very large scale
Online Access:Get full text
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Summary:The history of research on eigenvalue problems is rich with many outstanding contributions. Nonetheless, the rapidly increasing size of data sets requires new algorithms for old problems in the context of extremely large matrix dimensions. This paper reports on a new method for finding eigenvalues of very large matrices by a synthesis of evolutionary computation, parallel programming, and empirical stochastic search. The direct design of our method has the added advantage that it could be adapted to extend many algorithmic variants of solutions of generalized eigenvalue problems to improve the accuracy of our algorithms. The preliminary evaluation results are encouraging and demonstrate the method's efficiency and practicality.
ISSN:2331-8422