Self-regularity: a new paradigm for primal-dual interior-point algorithms
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Beteilige Person: Peng, Jiming (VerfasserIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Princeton, N.J. Princeton University Press ©2002
Schriftenreihe:Princeton series in applied mathematics
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Beschreibung:Includes bibliographical references (pages 175-181) and index
Preface; Acknowledgements; Notation; List of Abbreviations; Chapter 1. Introduction and Preliminaries; Chapter 2. Self-Regular Functions and Their Properties; Chapter 3. Primal-Dual Algorithms for Linear Optimization Based on Self-Regular Proximities; Chapter 4. Interior-Point Methods for Complementarity Problems Based on Self-Regular Proximities; Chapter 5. Primal-Dual Interior-Point Methods for Semidefinite Optimization Based on Self-Regular Proximities; Chapter 6. Primal-Dual Interior-Point Methods for Second-Order Conic Optimization Based on Self-Regular Proximities
Research on interior-point methods (IPMs) has dominated the field of mathematical programming for the last two decades. Two contrasting approaches in the analysis and implementation of IPMs are the so-called small-update and large-update methods, although, until now, there has been a notorious gap between the theory and practical performance of these two strategies. This book comes close to bridging that gap, presenting a new framework for the theory of primal-dual IPMs based on the notion of the self-regularity of a function. The authors deal with linear optimization, nonlinear complementarity
Umfang:1 Online-Ressource (xiii, 185 pages)
ISBN:0691091927
1400814529
140082513X
9781400814527
9781400825134