Includes bibliographical references and index.

A short history of optimization -- Numerical models and solvers -- Unconstrained gradient-based optimization -- Constrained gradient-based optimization -- Computing derivatives -- Gradient-free optimization -- Discrete optimization -- Multiobjective optimization -- Surrogate-based optimization -- Convex optimization -- Optimization under uncertainty -- Multidisciplinary design optimization.

"Based on course-tested material, this rigorous yet accessible graduate textbook covers both fundamental and advanced optimization theory and algorithms. It covers a wide range of numerical methods and topics, including both gradient-based and gradient-free algorithms, multidisciplinary design optimization, and uncertainty, with instruction on how to determine which algorithm should be used for a given application. It also provides an overview of models and how to prepare them for use with numerical optimization, including derivative computation. Over 200 high-quality visualizations and numerous examples facilitate understanding of the theory, and practical tips address common issues encountered in practical engineering design optimization and how to address them. Numerous end-of-chapter homework problems, progressing in difficulty, help put knowledge into practice"--

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