Algorithms for Optimization (The MIT Press)

Kochenderfer, Mykel J.

ISBN 10: 0262039427 / ISBN 13: 9780262039420

Published by The MIT Press, 2019

About this Item

Item in good condition and has highlighting/writing on text. Used texts may not contain supplemental items such as CDs, info-trac etc.

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Bibliographic Details

• Title: Algorithms for Optimization (The MIT Press)
• Publisher: The MIT Press
• Publication Date: 2019
• Language: English
• ISBN 10: 0262039427
• ISBN 13: 9780262039420
• Binding: Hardcover
• Condition: Good

About this title

Synopsis

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.

This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

About the Author

Mykel J. Kochenderfer is Assistant Professor in the Department of Aeronautics and Astronautics at Stanford University and the author of Decision Making Under Uncertainty: Theory and Application.

Tim A. Wheeler wrote his PhD thesis on safety validation for autonomous vehicles and is now in industry working on air taxis.