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Interior point algorithms: theory and analysis
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ISBN: 0471174203 9780471174202 Year: 1997 Publisher: London Wiley

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Internet and Network Economics (vol. # 3828) : First International Workshop, WINE 2005, Hong Kong, China, December 15-17, 2005, Proceedings
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ISBN: 9783540322931 Year: 2005 Publisher: Berlin Heidelberg Springer-Verlag GmbH

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Internet and network economics.
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ISBN: 3540309004 9783540309000 Year: 2005 Publisher: Berlin Springer

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Book
Linear and nonlinear programming
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ISBN: 0387745033 Year: 2008 Volume: 116 Publisher: New York, New York : Springer,

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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. .


Book
Linear and Nonlinear Programming
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ISBN: 9783319188416 3319188410 3319188429 Year: 2016 Volume: 228 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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This new edition covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. Again a connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve the problem. As in the earlier editions, the material in this fourth edition is organized into three separate parts. Part I is a self-contained introduction to linear programming covering numerical algorithms and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. Part III extends the concepts developed in the second part to constrained optimization problems. It is possible to go directly into Parts II and III omitting Part I, and, in fact, the book has been used in this way in many universities.


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Linear and nonlinear programming
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ISBN: 3030854507 3030854493 Year: 2021 Publisher: Cham, Switzerland : Springer,

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Book
Linear and nonlinear programming
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ISBN: 9781441945044 0387745025 1441945040 9780387745022 Year: 2008 Publisher: New York: Springer,

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Digital
Linear and Nonlinear Programming
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ISBN: 9780387745039 Year: 2008 Publisher: Boston, MA Springer Science+Business Media, LLC


Digital
Linear and Nonlinear Programming
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ISBN: 9783030854508 9783030854515 9783030854522 9783030854492 Year: 2021 Publisher: Cham Springer International Publishing

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The 5th edition of this classic textbook covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve that problem. End-of-chapter exercises are provided for all chapters. The material is organized into three separate parts. Part I offers a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. In turn, Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. As such, Parts II and III can easily be used without reading Part I and, in fact, the book has been used in this way at many universities. New to this edition are popular topics in data science and machine learning, such as the Markov Decision Process, Farkas' lemma, convergence speed analysis, duality theories and applications, various first-order methods, stochastic gradient method, mirror-descent method, Frank-Wolf method, ALM/ADMM method, interior trust-region method for non-convex optimization, distributionally robust optimization, online linear programming, semidefinite programming for sensor-network localization, and infeasibility detection for nonlinear optimization.


Book
Discrete Geometry and Optimization
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ISSN: 10695265 ISBN: 331900199X 3319033123 3319002007 Year: 2013 Volume: v. 69 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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Optimization has long been a source of both inspiration and applications for geometers, and conversely, discrete and convex geometry have provided the foundations for many optimization techniques, leading to a rich interplay between these subjects. The purpose of the Workshop on Discrete Geometry, the Conference on Discrete Geometry and Optimization, and the Workshop on Optimization, held in September 2011 at the Fields Institute, Toronto, was to further stimulate the interaction between geometers and optimizers. This volume reflects the interplay between these areas. The inspiring Fejes Tóth Lecture Series, delivered by Thomas Hales of the University of Pittsburgh, exemplified this approach. While these fields have recently witnessed a lot of activity and successes, many questions remain open. For example, Fields medalist Stephen Smale stated that the question of the existence of a strongly polynomial time algorithm for linear optimization is one of the most important unsolved problems at the beginning of the 21st century. The broad range of topics covered in this volume demonstrates the many recent and fruitful connections between different approaches, and features novel results and state-of-the-art surveys as well as open problems.

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