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Numerical methods of optimisation --- Mathematical optimization --- Optimisation mathématique --- Production control --- 519.863 --- 519.863 Optimization models --- Optimization models --- Mathematical optimization. --- Production control. --- Optimisation mathématique --- Programmation mathematique --- Programmation non lineaire

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Mathematical optimization --- 519.863 --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- 519.863 Optimization models --- Optimization models --- Operational research. Game theory

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Structural analysis (Engineering) --- Structural design --- Mathematical optimization --- 519.863 --- Engineering design --- Architectural design --- Strains and stresses --- Architectural engineering --- Engineering, Architectural --- Structural mechanics --- Structures, Theory of --- Structural engineering --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Optimization models --- 519.863 Optimization models --- Mathematical optimization. --- Structural analysis

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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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The book gives a detailed and rigorous treatment of the theory of optimization (unconstrained optimization, nonlinear programming, semi-infinite programming, etc.) in finite-dimensional spaces. The fundamental results of convexity theory and the theory of duality in nonlinear programming and the theories of linear inequalities, convex polyhedra, and linear programming are covered in detail. Over two hundred, carefully selected exercises should help the students master the material of the book and give further insight. Some of the most basic results are proved in several independent ways in order to give flexibility to the instructor. A separate chapter gives extensive treatments of three of the most basic optimization algorithms (the steepest-descent method, Newton's method, the conjugate-gradient method). The first chapter of the book introduces the necessary differential calculus tools used in the book. Several chapters contain more advanced topics in optimization such as Ekeland's epsilon-variational principle, a deep and detailed study of separation properties of two or more convex sets in general vector spaces, Helly's theorem and its applications to optimization, etc. The book is suitable as a textbook for a first or second course in optimization at the graduate level. It is also suitable for self-study or as a reference book for advanced readers. The book grew out of author's experience in teaching a graduate level one-semester course a dozen times since 1993. Osman Guler is a Professor in the Department of Mathematics and Statistics at University of Maryland, Baltimore County. His research interests include mathematical programming, convex analysis, complexity of optimization problems, and operations research.

Mathematics. --- Optimization. --- Calculus of Variations and Optimal Control, Optimization. --- Operations Research, Mathematical Programming. --- Applications of Mathematics. --- Mathematical optimization. --- Operations research. --- Mathématiques --- Optimisation mathématique --- Recherche opérationnelle --- Mathematical optimization --- Programming (Mathematics) --- 519.8 --- 519.863 --- Operational research --- Optimization models --- 519.863 Optimization models --- 519.8 Operational research --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Operations research --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Simulation methods --- System analysis