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Keywords:

  • mathematical optimization;
  • linear programming;
  • calculus of variations;
  • meta-heuristic methods

Abstract

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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