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Algorithms for Optimization (The MIT Press)

Book Name: Algorithms for Optimization (The MIT Press)
Author: Mykel J. Kochenderfer, Tim A. Wheeler
Publisher: The MIT Press
ISBN-10: 0262039427
Year: 2019
Pages: 520
Language: English
File format: PDF

Algorithms for Optimization (The MIT Press) Pdf Book Description:

This book provides a detailed introduction to optimization with a focus on algorithms that are practical. The publication approaches optimization in the engineering standpoint, where the purpose is to design a system which optimizes a set of metrics subject to limitations. Clients will learn about computational approaches for a range of challenges, such as hunting high-dimensional spaces, managing issues where there are numerous competing goals, and adapting doubt in the metrics. The text offers concrete implementations from the Julia programming language.

Topics covered include their generalization to multiple measurements; neighborhood descent and original – and – second-order procedures that inform neighborhood descent; stochastic procedures, which introduce randomness to the optimization procedure; linear constrained optimization, even when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate versions, and utilizing probabilistic surrogate versions to direct optimization; optimization under uncertainty; doubt propagation; term optimization; and multidisciplinary design optimisation. Appendixes provide an introduction into the Julia terminology, evaluation functions for assessing algorithm functionality, and mathematical theories utilized in the derivation and evaluation of the optimization techniques discussed within the text. The book may be used by advanced undergraduates and graduate students in math, statistics, computer science, any technology discipline, (including electric engineering and aerospace technology ), and operations research, and also a reference for professionals.

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