Constrained Global Optimization: Algorithms and Applications - Pardalos, Panos M.
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Présentation Constrained Global Optimization: Algorithms And Applications de Pardalos, Panos M. Format Broché
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Biographie:
Ding-Zhu Du is Professor of Computer Science at the University of Texas at Dallas. For a number of years he was the Editor-in-Chief of the Journal of Combinatorial Optimization and the combinatorial optimization series editor for the SOIA book series. Professor Du is co-editor of the first and second editions of the Handbook of Combinatorial Optimization. He was also co-author (with Pardalos and Wu) of the Kluwer publication Mathematical Theory of Optimization. Panos M. Pardalos is Distinguished Professor Emeritus of Industrial and Systems Engineering at the University of Florida. Additionally, he is the Paul and Heidi Brown Preeminent Professor in Industrial & Systems Engineering. He is also an affiliated faculty member of the Computer and Information Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the Director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing. He has co-authored and co-edited more than 30 books, as well as publishing more than 600 journal articles and conference proceedings. Prof. Pardalos is a Fellow of AAAS (American Association for the Advancement of Science), Fellow of American Institute for Medical and Biological Engineering (AIMBE), and EUROPT. He is a Distinguished International Professor by the Chinese Minister of Education...
Sommaire:
Convex sets and functions.- Optimality conditions in nonlinear programming.- Combinatorial optimization problems that can be formulated as nonconvex quadratic problems.- Enumerative methods in nonconvex programming.- Cutting plane methods.- Branch and bound methods.- Bilinear programming methods for nonconvex quadratic problems.- Large scale problems.- Global minimization of indefinite quadratic problems.- Test problems for global nonconvex quadratic programming algorithms.
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