Overview
- Editors:
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Ding-Zhu Du
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Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA
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Panos M. Pardalos
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Center for Applied Optimization, ISE Department, University of Florida, Gainesville, USA
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Weili Wu
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Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA
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Table of contents (15 chapters)
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Front Matter
Pages i-xiii
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 1-21
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 23-40
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 41-50
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 51-63
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 65-79
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 81-98
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 99-123
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 125-132
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 133-150
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 151-166
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 167-185
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 187-200
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 201-213
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 215-226
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- Ding-Zhu Du, Panos M. Pardalos, Weili Wu
Pages 227-243
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Back Matter
Pages 245-273
About this book
Optimization is of central importance in all sciences. Nature inherently seeks optimal solutions. For example, light travels through the "shortest" path and the folded state of a protein corresponds to the structure with the "minimum" potential energy. In combinatorial optimization, there are numerous computationally hard problems arising in real world applications, such as floorplanning in VLSI designs and Steiner trees in communication networks. For these problems, the exact optimal solution is not currently real-time computable. One usually computes an approximate solution with various kinds of heuristics. Recently, many approaches have been developed that link the discrete space of combinatorial optimization to the continuous space of nonlinear optimization through geometric, analytic, and algebraic techniques. Many researchers have found that such approaches lead to very fast and efficient heuristics for solving large problems. Although almost all such heuristics work well in practice there is no solid theoretical analysis, except Karmakar's algorithm for linear programming. With this situation in mind, we decided to teach a seminar on nonlinear optimization with emphasis on its mathematical foundations. This book is the result of that seminar. During the last decades many textbooks and monographs in nonlinear optimization have been published. Why should we write this new one? What is the difference of this book from the others? The motivation for writing this book originated from our efforts to select a textbook for a graduate seminar with focus on the mathematical foundations of optimization.
Editors and Affiliations
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Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA
Ding-Zhu Du,
Weili Wu
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Center for Applied Optimization, ISE Department, University of Florida, Gainesville, USA
Panos M. Pardalos