Self-regularity : a new paradigm for primal-dual interior-point algorithms
Interior-point methods Mathematical optimization Programming (Mathematics) e-böcker
Princeton University Press
2002
EISBN 9781400825134
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.
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.
