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"Linear and Nonlinear Programming" is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first and second editions. Now the third edition has been completely updated with recent Optimization Methods. Yinyu Ye has written chapters and chapter material on a number of these areas including Interior Point Methods. This book is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for technical students. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists. .

Linear programming. --- Nonlinear programming. --- Programming (Mathematics) --- Production scheduling

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This edited book presents recent developments and state-of-the-art review in various areas of mathematical programming and game theory. It is a peer-reviewed research monograph under the ISI Platinum Jubilee Series on Statistical Science and Interdisciplinary Research. This volume provides a panoramic view of theory and the applications of the methods of mathematical programming to problems in statistics, finance, games and electrical networks. It also provides an important as well as timely overview of research trends and focuses on the exciting areas like support vector machines, bilevel

Programming (Mathematics) --- Game theory. --- Decision making --- Games, Theory of --- Theory of games --- Mathematical models --- Mathematics --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Mathematical optimization --- Operations research --- Mathematical models.

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Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent developments.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- ingenieurswetenschappen --- robots --- Combinatorial optimization --- Computational intelligence. --- Computer algorithms --- Heuristic programming --- 519.863 --- 681.3*I2 --- 519.863 Optimization models --- Optimization models --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- Artificial intelligence --- Programming (Mathematics) --- Algorithms --- Intelligence, Computational --- Soft computing --- Optimization, Combinatorial --- Combinatorial analysis --- Mathematical optimization --- Data processing --- Computational intelligence

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One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc. Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.

Heuristic programming. --- Combinatorial optimization. --- Programmation heuristique --- Optimisation combinatoire --- Heuristic programming --- Combinatorial optimization --- Civil Engineering --- Operations Research --- Applied Mathematics --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Optimization, Combinatorial --- Engineering. --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Appl.Mathematics/Computational Methods of Engineering. --- Artificial Intelligence (incl. Robotics). --- Mathematical and Computational Engineering. --- Artificial Intelligence. --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematics --- Artificial intelligence --- Programming (Mathematics) --- Combinatorial analysis --- Mathematical optimization

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This text presents a multi-disciplined view of optimization, providing students and researchers with a thorough examination of algorithms, methods, and tools from diverse areas of optimization without introducing excessive theoretical detail. This second edition includes additional topics, including global optimization and a real-world case study using important concepts from each chapter. Key Features: Provides well-written self-contained chapters, including problem sets and exercises, making it ideal for the classroom setting; Introduces applied optimization to the hazardous waste blending problem; Explores linear programming, nonlinear programming, discrete optimization, global optimization, optimization under uncertainty, multi-objective optimization, optimal control and stochastic optimal control; Includes an extensive bibliography at the end of each chapter and an index; GAMS files of case studies for Chapters 2, 3, 4, 5, and 7 are linked to http://www.springer.com/math/book/978-0-387-76634-8; Solutions manual available upon adoptions. Introduction to Applied Optimization is intended for advanced undergraduate and graduate students and will benefit scientists from diverse areas, including engineers.

Mathematics. --- Calculus of Variations and Optimal Control; Optimization. --- Industrial Chemistry/Chemical Engineering. --- Appl.Mathematics/Computational Methods of Engineering. --- Systems Theory, Control. --- Business/Management Science, general. --- Chemical engineering. --- Systems theory. --- Mathematical optimization. --- Engineering mathematics. --- Economics. --- Mathématiques --- Génie chimique --- Optimisation mathématique --- Mathématiques de l'ingénieur --- Economie politique --- Convex programming. --- Finance -- Mathematical models. --- Finite element method. --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Programming (Mathematics) --- Mathematical programming --- Goal programming --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Business. --- Management science. --- System theory. --- Calculus of variations. --- Applied mathematics. --- Optimization. --- Business and Management, general. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Algorithms --- Functional equations --- Mathematical optimization --- Mathematical and Computational Engineering. --- Trade --- Economics --- Management --- Commerce --- Industrial management --- Engineering --- Engineering analysis --- Chemistry, Industrial --- Engineering, Chemical --- Industrial chemistry --- Chemistry, Technical --- Metallurgy --- Systems, Theory of --- Systems science --- Science --- Mathematics --- Philosophy --- Mathematical models. --- Isoperimetrical problems --- Variations, Calculus of --- Quantitative business analysis --- Problem solving --- Statistical decision