Roadmap for Optimization
Version of Record online: 13 JUL 2009
Copyright © 2009 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 1, Issue 1, pages 3–17, July/August 2009
How to Cite
Said, Y. and Wegman, E. (2009), Roadmap for Optimization. WIREs Comp Stat, 1: 3–17. doi: 10.1002/wics.16
- Issue online: 13 JUL 2009
- Version of Record online: 13 JUL 2009
- mathematical optimization;
- linear programming;
- calculus of variations;
- meta-heuristic methods
This article, first published online on July 13, 2009 in Wiley Online Library (http://www.wileyonlinelibrary.com), has been revised at the request of the Editors-in-Chief and the Publisher. References and links have been added to aid the reader interested in following up on any technique. Please follow the link to the Supporting Information to view the original version of this article. http://onlinelibrary.wiley.com/doi/10.1002/wics.16/suppinfo
This article is intended as a broad overview of optimization. While often considered as a subset of operations research, optimization is a central concept for statistical theory, e.g., maximum likelihood, least squares, minimum entropy, minimum loss and risk, and so on. As data set sizes become larger, the computational framework of optimization becomes more important. In this article we cover mathematical programming, linear programming, dynamic programming, calculus of variations, and metaheuristic methods. Copyright © 2009 John Wiley & Sons, Inc.
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