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Derivative-Free and Blackbox Optimization

2017-12-02 Mathematics
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Release : 2017-12-02
Genre : Mathematics
Kind : eBook
Book Rating : 134/5 ( reviews)

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Book Synopsis Derivative-Free and Blackbox Optimization by : Charles Audet

Download or read book Derivative-Free and Blackbox Optimization written by Charles Audet. This book was released on 2017-12-02. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.

Introduction to Derivative-Free Optimization

2009-04-16 Mathematics
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Release : 2009-04-16
Genre : Mathematics
Kind : eBook
Book Rating : 683/5 ( reviews)

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Book Synopsis Introduction to Derivative-Free Optimization by : Andrew R. Conn

Download or read book Introduction to Derivative-Free Optimization written by Andrew R. Conn. This book was released on 2009-04-16. Available in PDF, EPUB and Kindle. Book excerpt: The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics.

Computational Optimization, Methods and Algorithms

2011-06-17 Technology & Engineering
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Release : 2011-06-17
Genre : Technology & Engineering
Kind : eBook
Book Rating : 592/5 ( reviews)

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Book Synopsis Computational Optimization, Methods and Algorithms by : Slawomir Koziel

Download or read book Computational Optimization, Methods and Algorithms written by Slawomir Koziel. This book was released on 2011-06-17. Available in PDF, EPUB and Kindle. Book excerpt: Computational optimization is an important paradigm with a wide range of applications. In virtually all branches of engineering and industry, we almost always try to optimize something - whether to minimize the cost and energy consumption, or to maximize profits, outputs, performance and efficiency. In many cases, this search for optimality is challenging, either because of the high computational cost of evaluating objectives and constraints, or because of the nonlinearity, multimodality, discontinuity and uncertainty of the problem functions in the real-world systems. Another complication is that most problems are often NP-hard, that is, the solution time for finding the optimum increases exponentially with the problem size. The development of efficient algorithms and specialized techniques that address these difficulties is of primary importance for contemporary engineering, science and industry. This book consists of 12 self-contained chapters, contributed from worldwide experts who are working in these exciting areas. The book strives to review and discuss the latest developments concerning optimization and modelling with a focus on methods and algorithms for computational optimization. It also covers well-chosen, real-world applications in science, engineering and industry. Main topics include derivative-free optimization, multi-objective evolutionary algorithms, surrogate-based methods, maximum simulated likelihood estimation, support vector machines, and metaheuristic algorithms. Application case studies include aerodynamic shape optimization, microwave engineering, black-box optimization, classification, economics, inventory optimization and structural optimization. This graduate level book can serve as an excellent reference for lecturers, researchers and students in computational science, engineering and industry.

Review of the Book ``Derivative-Free and Blackbox Optimization'' by C. Audet and W. Hare, Springer Series in Operations Research and Financial Engineering, 2017, 302 Pages, DOI 10.1007/978-3-319-68913-5

2019
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Release : 2019
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Review of the Book ``Derivative-Free and Blackbox Optimization'' by C. Audet and W. Hare, Springer Series in Operations Research and Financial Engineering, 2017, 302 Pages, DOI 10.1007/978-3-319-68913-5 by : Michael Kokkolaras

Download or read book Review of the Book ``Derivative-Free and Blackbox Optimization'' by C. Audet and W. Hare, Springer Series in Operations Research and Financial Engineering, 2017, 302 Pages, DOI 10.1007/978-3-319-68913-5 written by Michael Kokkolaras. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt:

Algorithms for Optimization

2019-03-12 Computers
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Release : 2019-03-12
Genre : Computers
Kind : eBook
Book Rating : 427/5 ( reviews)

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Book Synopsis Algorithms for Optimization by : Mykel J. Kochenderfer

Download or read book Algorithms for Optimization written by Mykel J. Kochenderfer. This book was released on 2019-03-12. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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