Roadmap for Optimization
Yasmin H. Said, Edward J. Wegman · Wiley Interdisciplinary Reviews Computational Statistics · 2009
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. This article is categorized under: Algorithms and Computational Methods > Least Squares Algorithms and Computational Methods > Convex Optimization Algorithms and Computational Methods > Linear Programming Algorithms and Computational Methods > Dynamic Programming Algorithms and Computational Methods > Quadratic and Nonlinear Programming Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing Algorithms and Computational Methods > Simulated Annealing Algorithms and Computational Methods > Maximum Likelihood Methods