Algorithms For Optimization

Algorithms For Optimization Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Algorithms For Optimization book. This book definitely worth reading, it is an incredibly well-written.

Algorithms for Optimization

Mykel J. Kochenderfer,Tim A. Wheeler
Algorithms for Optimization Book PDF

Author : Mykel J. Kochenderfer,Tim A. Wheeler
Publisher : MIT Press
Page : 521 pages
File Size : 50,5 Mb
Release : 2019-03-12
Category : Computers
ISBN : 9780262039420

Get Book

Algorithms for Optimization by Mykel J. Kochenderfer,Tim A. Wheeler Pdf

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.

Optimization

Rajesh Kumar Arora
Optimization Book PDF

Author : Rajesh Kumar Arora
Publisher : CRC Press
Page : 454 pages
File Size : 41,5 Mb
Release : 2015-05-06
Category : Business & Economics
ISBN : 9781498721158

Get Book

Optimization by Rajesh Kumar Arora Pdf

Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and co

Discrete Optimization Algorithms

Maciej M. Sys?o,Narsingh Deo,Janusz S. Kowalik
Discrete Optimization Algorithms Book PDF

Author : Maciej M. Sys?o,Narsingh Deo,Janusz S. Kowalik
Publisher : Courier Corporation
Page : 564 pages
File Size : 40,5 Mb
Release : 2006-01-01
Category : Computers
ISBN : 9780486453538

Get Book

Discrete Optimization Algorithms by Maciej M. Sys?o,Narsingh Deo,Janusz S. Kowalik Pdf

Rich in publications, the well-established field of discrete optimization nevertheless features relatively few books with ready-to-use computer programs. This book, geared toward upper-level undergraduates and graduate students, addresses that need. In addition, it offers a look at the programs' derivation and performance characteristics. Subjects include linear and integer programming, packing and covering, optimization on networks, and coloring and scheduling. A familiarity with design, analysis, and use of computer algorithms is assumed, along with knowledge of programming in Pascal. The book can be used as a supporting text in discrete optimization courses or as a software handbook, with twenty-six programs that execute the most common algorithms in each topic area. Each chapter is self-contained, allowing readers to browse at will.

Graphs, Algorithms, and Optimization

William Kocay,Donald L. Kreher
Graphs Algorithms and Optimization Book PDF

Author : William Kocay,Donald L. Kreher
Publisher : CRC Press
Page : 504 pages
File Size : 48,6 Mb
Release : 2017-09-20
Category : Mathematics
ISBN : 9781351989121

Get Book

Graphs, Algorithms, and Optimization by William Kocay,Donald L. Kreher Pdf

Graph theory offers a rich source of problems and techniques for programming and data structure development, as well as for understanding computing theory, including NP-Completeness and polynomial reduction. A comprehensive text, Graphs, Algorithms, and Optimization features clear exposition on modern algorithmic graph theory presented in a rigorous yet approachable way. The book covers major areas of graph theory including discrete optimization and its connection to graph algorithms. The authors explore surface topology from an intuitive point of view and include detailed discussions on linear programming that emphasize graph theory problems useful in mathematics and computer science. Many algorithms are provided along with the data structure needed to program the algorithms efficiently. The book also provides coverage on algorithm complexity and efficiency, NP-completeness, linear optimization, and linear programming and its relationship to graph algorithms. Written in an accessible and informal style, this work covers nearly all areas of graph theory. Graphs, Algorithms, and Optimization provides a modern discussion of graph theory applicable to mathematics, computer science, and crossover applications.

Stochastic Recursive Algorithms for Optimization

S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Stochastic Recursive Algorithms for Optimization Book PDF

Author : S. Bhatnagar,H.L. Prasad,L.A. Prashanth
Publisher : Springer
Page : 310 pages
File Size : 43,8 Mb
Release : 2012-08-11
Category : Technology & Engineering
ISBN : 9781447142850

Get Book

Stochastic Recursive Algorithms for Optimization by S. Bhatnagar,H.L. Prasad,L.A. Prashanth Pdf

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.

Fundamentals of Optimization Techniques with Algorithms

Sukanta Nayak
Fundamentals of Optimization Techniques with Algorithms Book PDF

Author : Sukanta Nayak
Publisher : Academic Press
Page : 323 pages
File Size : 45,7 Mb
Release : 2020-08-25
Category : Technology & Engineering
ISBN : 9780128224922

Get Book

Fundamentals of Optimization Techniques with Algorithms by Sukanta Nayak Pdf

Optimization is a key concept in mathematics, computer science, and operations research, and is essential to the modeling of any system, playing an integral role in computer-aided design. Fundamentals of Optimization Techniques with Algorithms presents a complete package of various traditional and advanced optimization techniques along with a variety of example problems, algorithms and MATLAB© code optimization techniques, for linear and nonlinear single variable and multivariable models, as well as multi-objective and advanced optimization techniques. It presents both theoretical and numerical perspectives in a clear and approachable way. In order to help the reader apply optimization techniques in practice, the book details program codes and computer-aided designs in relation to real-world problems. Ten chapters cover, an introduction to optimization; linear programming; single variable nonlinear optimization; multivariable unconstrained nonlinear optimization; multivariable constrained nonlinear optimization; geometric programming; dynamic programming; integer programming; multi-objective optimization; and nature-inspired optimization. This book provides accessible coverage of optimization techniques, and helps the reader to apply them in practice. Presents optimization techniques clearly, including worked-out examples, from traditional to advanced Maps out the relations between optimization and other mathematical topics and disciplines Provides systematic coverage of algorithms to facilitate computer coding Gives MATLAB© codes in relation to optimization techniques and their use in computer-aided design Presents nature-inspired optimization techniques including genetic algorithms and artificial neural networks

Algorithms for Continuous Optimization

E. Spedicato
Algorithms for Continuous Optimization Book PDF

Author : E. Spedicato
Publisher : Springer Science & Business Media
Page : 572 pages
File Size : 53,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789400903692

Get Book

Algorithms for Continuous Optimization by E. Spedicato Pdf

The NATO Advanced Study Institute on "Algorithms for continuous optimiza tion: the state of the art" was held September 5-18, 1993, at II Ciocco, Barga, Italy. It was attended by 75 students (among them many well known specialists in optimiza tion) from the following countries: Belgium, Brasil, Canada, China, Czech Republic, France, Germany, Greece, Hungary, Italy, Poland, Portugal, Rumania, Spain, Turkey, UK, USA, Venezuela. The lectures were given by 17 well known specialists in the field, from Brasil, China, Germany, Italy, Portugal, Russia, Sweden, UK, USA. Solving continuous optimization problems is a fundamental task in computational mathematics for applications in areas of engineering, economics, chemistry, biology and so on. Most real problems are nonlinear and can be of quite large size. Devel oping efficient algorithms for continuous optimization has been an important field of research in the last 30 years, with much additional impetus provided in the last decade by the availability of very fast and parallel computers. Techniques, like the simplex method, that were already considered fully developed thirty years ago have been thoroughly revised and enormously improved. The aim of this ASI was to present the state of the art in this field. While not all important aspects could be covered in the fifty hours of lectures (for instance multiob jective optimization had to be skipped), we believe that most important topics were presented, many of them by scientists who greatly contributed to their development.

Evolutionary Optimization Algorithms

Dan Simon
Evolutionary Optimization Algorithms Book PDF

Author : Dan Simon
Publisher : John Wiley & Sons
Page : 776 pages
File Size : 42,9 Mb
Release : 2013-06-13
Category : Mathematics
ISBN : 9781118659502

Get Book

Evolutionary Optimization Algorithms by Dan Simon Pdf

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

Combinatorial Optimization

Christos H. Papadimitriou,Kenneth Steiglitz
Combinatorial Optimization Book PDF

Author : Christos H. Papadimitriou,Kenneth Steiglitz
Publisher : Courier Corporation
Page : 528 pages
File Size : 53,6 Mb
Release : 2013-04-26
Category : Mathematics
ISBN : 9780486320137

Get Book

Combinatorial Optimization by Christos H. Papadimitriou,Kenneth Steiglitz Pdf

This graduate-level text considers the Soviet ellipsoid algorithm for linear programming; efficient algorithms for network flow, matching, spanning trees, and matroids; the theory of NP-complete problems; local search heuristics for NP-complete problems, more. 1982 edition.

Computational Optimization, Methods and Algorithms

Slawomir Koziel,Xin-She Yang
Computational Optimization Methods and Algorithms Book PDF

Author : Slawomir Koziel,Xin-She Yang
Publisher : Springer
Page : 292 pages
File Size : 48,7 Mb
Release : 2011-06-17
Category : Technology & Engineering
ISBN : 9783642208591

Get Book

Computational Optimization, Methods and Algorithms by Slawomir Koziel,Xin-She Yang Pdf

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.

MM Optimization Algorithms

Kenneth Lange
MM Optimization Algorithms Book PDF

Author : Kenneth Lange
Publisher : SIAM
Page : 229 pages
File Size : 52,7 Mb
Release : 2016-07-11
Category : Mathematics
ISBN : 9781611974393

Get Book

MM Optimization Algorithms by Kenneth Lange Pdf

MM Optimization Algorithms?offers an overview of the MM principle, a device for deriving optimization algorithms satisfying the ascent or descent property. These algorithms can separate the variables of a problem, avoid large matrix inversions, linearize a problem, restore symmetry, deal with equality and inequality constraints gracefully, and turn a nondifferentiable problem into a smooth problem.? The author presents the first extended treatment of MM algorithms, which are ideal for high-dimensional optimization problems in data mining, imaging, and genomics; derives numerous algorithms from a broad diversity of application areas, with a particular emphasis on statistics, biology, and data mining; and summarizes a large amount of literature that has not reached book form before.?

Advanced Optimization by Nature-Inspired Algorithms

Omid Bozorg-Haddad
Advanced Optimization by Nature Inspired Algorithms Book PDF

Author : Omid Bozorg-Haddad
Publisher : Springer
Page : 159 pages
File Size : 49,5 Mb
Release : 2017-06-30
Category : Technology & Engineering
ISBN : 9789811052217

Get Book

Advanced Optimization by Nature-Inspired Algorithms by Omid Bozorg-Haddad Pdf

This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.

Nature-Inspired Optimization Algorithms

Xin-She Yang
Nature Inspired Optimization Algorithms Book PDF

Author : Xin-She Yang
Publisher : Elsevier
Page : 277 pages
File Size : 43,9 Mb
Release : 2014-02-17
Category : Computers
ISBN : 9780124167452

Get Book

Nature-Inspired Optimization Algorithms by Xin-She Yang Pdf

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding as well as practical implementation hints Provides a step-by-step introduction to each algorithm

Algorithms for Decision Making

Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray
Algorithms for Decision Making Book PDF

Author : Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray
Publisher : MIT Press
Page : 701 pages
File Size : 53,9 Mb
Release : 2022-08-16
Category : Computers
ISBN : 9780262047012

Get Book

Algorithms for Decision Making by Mykel J. Kochenderfer,Tim A. Wheeler,Kyle H. Wray Pdf

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.

Accelerated Optimization for Machine Learning

Zhouchen Lin,Huan Li,Cong Fang
Accelerated Optimization for Machine Learning Book PDF

Author : Zhouchen Lin,Huan Li,Cong Fang
Publisher : Springer Nature
Page : 286 pages
File Size : 53,5 Mb
Release : 2020-05-29
Category : Computers
ISBN : 9789811529108

Get Book

Accelerated Optimization for Machine Learning by Zhouchen Lin,Huan Li,Cong Fang Pdf

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.