This introduction to optimization methods and theory is aimed at the senior undergraduate and beginning graduate. Supplemented with worked examples to illustrate both theory and algorithms, it describes such topics as unconstrained optimization, linear programming and constrained optimization.
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Provides readers with a working knowledge of optimization theory and techniques to equip them with sufficient background for further study of advanced topics in this subject. Reviews basic definitions, notations and relations from linear algebra, geometry and calculus, followed by unconstrained optimization problems. The last two segments deal with constrained optimization. Features scores of worked examples that illustrate the theory and algorithms discussed in the text.
Edwin K. P. Chong received his PhD from Princeton University, where he held an IBM Graduate Fellowship. He is currently on the faculty of the School of Electrical Engineering at Purdue University. Stanislaw H. Zak received his PhD from the Technical University of Warsaw, Poland. An Associate Editor of Dynamics and Control and the IEEE Transactions on Neural Networks, he is currently on the faculty of the School of Electrical Engineering at Purdue University.
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