Parallel Evolutionary Computation in Very Large Scale Eigenvalue Problems
Abstract
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.
Cite
@article{arxiv.1008.5391,
title = {Parallel Evolutionary Computation in Very Large Scale Eigenvalue Problems},
author = {Hesam T. Dashti and Alireza F. Siahpirani and Liya Wang and Mary Kloc and Amir H. Assadi},
journal= {arXiv preprint arXiv:1008.5391},
year = {2013}
}
Comments
Proceedings of the 2008 International Conference on Scientific Computing, CSC 2008