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Self-regularity : a new paradigm for primal-dual interior-point algorithms / Jiming Peng, Cornelis Roos, and Tam�as Terlaky.

By: Peng, Jiming.
Contributor(s): Roos, Cornelis, 1941- | Terlaky, Tam�as.
Material type: materialTypeLabelBookSeries: Princeton series in applied mathematics: Publisher: Princeton, N.J. ; Oxford : Princeton University Press, �2002Description: 1 online resource (xiii, 185 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 9781400825134; 140082513X; 1400814529; 9781400814527; 9780691091938; 0691091935; 9780691091921; 0691091927.Subject(s): Mathematical optimization | Interior-point methods | Programming (Mathematics) | Optimisation math�ematique | M�ethodes de points int�erieurs | Programmation (Math�ematiques) | MATHEMATICS -- Optimization | MATHEMATICS -- Applied | Interior-point methods | Mathematical optimization | Programming (Mathematics) | Controleleer | Zelfregulering | Algoritmen | Mathematische programmeringGenre/Form: Electronic books.Additional physical formats: Print version:: Self-regularity.DDC classification: 519.6 Online resources: Click here to access online
Contents:
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.
Summary: 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.
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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.

Print version record.

In English.

IEEE IEEE Xplore Princeton University Press eBooks Library

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