Optimization Techniques and Applications with Examples

Optimization Techniques and Applications with Examples

Author: Xin-She Yang

Publisher: John Wiley & Sons

Published: 2018-09-19

Total Pages: 384

ISBN-13: 1119490545

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A guide to modern optimization applications and techniques in newly emerging areas spanning optimization, data science, machine intelligence, engineering, and computer sciences Optimization Techniques and Applications with Examples introduces the fundamentals of all the commonly used techniques in optimization that encompass the broadness and diversity of the methods (traditional and new) and algorithms. The author—a noted expert in the field—covers a wide range of topics including mathematical foundations, optimization formulation, optimality conditions, algorithmic complexity, linear programming, convex optimization, and integer programming. In addition, the book discusses artificial neural network, clustering and classifications, constraint-handling, queueing theory, support vector machine and multi-objective optimization, evolutionary computation, nature-inspired algorithms and many other topics. Designed as a practical resource, all topics are explained in detail with step-by-step examples to show how each method works. The book’s exercises test the acquired knowledge that can be potentially applied to real problem solving. By taking an informal approach to the subject, the author helps readers to rapidly acquire the basic knowledge in optimization, operational research, and applied data mining. This important resource: Offers an accessible and state-of-the-art introduction to the main optimization techniques Contains both traditional optimization techniques and the most current algorithms and swarm intelligence-based techniques Presents a balance of theory, algorithms, and implementation Includes more than 100 worked examples with step-by-step explanations Written for upper undergraduates and graduates in a standard course on optimization, operations research and data mining, Optimization Techniques and Applications with Examples is a highly accessible guide to understanding the fundamentals of all the commonly used techniques in optimization.


Topics in Optimization

Topics in Optimization

Author: George Leitmann

Publisher: Elsevier

Published: 2016-06-03

Total Pages: 486

ISBN-13: 1483266818

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Mathematics in Science and Engineering, Volume 31: Topics in Optimization compiles contributions to the field of optimization of dynamical systems. This book is organized into two parts. Part 1 covers reported investigations that are based on variational techniques and constitute essentially extensions of the classical calculus of variations. The contributions to optimal control theory and its applications, where the arguments are primarily geometric in nature, are discussed in Part 2. This volume specifically discusses the inequalities in a variational problem, singular extremals, mathematical foundations of system optimization, and synthesis of optimal controls. This publication is recommended for both theoreticians and practitioners.


New Ideas in Optimization

New Ideas in Optimization

Author: David Corne

Publisher:

Published: 1999

Total Pages: 493

ISBN-13: 9780077095062

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Optimization is a pivotal aspect of software design. The techniques treated in this text represent research as elucidated by the leaders in the field. The optimization methods are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.


Algorithms for Decision Making

Algorithms for Decision Making

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2022-08-16

Total Pages: 701

ISBN-13: 0262047012

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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.


Optimization for Machine Learning

Optimization for Machine Learning

Author: Suvrit Sra

Publisher: MIT Press

Published: 2012

Total Pages: 509

ISBN-13: 026201646X

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An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.


Optimization and Control with Applications

Optimization and Control with Applications

Author: Liqun Qi

Publisher: Springer Science & Business Media

Published: 2006-03-30

Total Pages: 587

ISBN-13: 0387242554

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A collection of 28 refereed papers grouped according to four broad topics: duality and optimality conditions, optimization algorithms, optimal control, and variational inequality and equilibrium problems. Suitable for researchers, practitioners and postgrads.


Foundations of Optimization

Foundations of Optimization

Author: Osman Güler

Publisher: Springer Science & Business Media

Published: 2010-08-03

Total Pages: 445

ISBN-13: 0387684077

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This book covers the fundamental principles of optimization in finite dimensions. It develops the necessary material in multivariable calculus both with coordinates and coordinate-free, so recent developments such as semidefinite programming can be dealt with.


Combinatorial Optimization

Combinatorial Optimization

Author: Bernhard Korte

Publisher: Springer Science & Business Media

Published: 2006-01-27

Total Pages: 596

ISBN-13: 3540292977

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This well-written textbook on combinatorial optimization puts special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. The book contains complete (but concise) proofs, as well as many deep results, some of which have not appeared in any previous books.


Optimization Models

Optimization Models

Author: Giuseppe C. Calafiore

Publisher: Cambridge University Press

Published: 2014-10-31

Total Pages: 651

ISBN-13: 1107050871

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This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.