Large-scale and distributed optimization
Author(s)
Bibliographic Information
Large-scale and distributed optimization
（Lecture notes in mathematics, 2227）
Springer, c2018
Available at / 36 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
L/N||LNM||2227200037724253
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Note
Includes bibliographical references
Description and Table of Contents
Description
This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians.
Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th-16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.
Table of Contents
- Large-Scale and Distributed Optimization: An Introduction. - Exploiting Chordality in Optimization Algorithms for Model Predictive Control. - Decomposition Methods for Large-Scale Semidefinite Programs with Chordal Aggregate Sparsity and Partial Orthogonality. - Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization. - Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework. - Block-Coordinate Primal-Dual Method for Nonsmooth Minimization over Linear Constraints. - Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions. - Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints. - Frank-Wolfe Style Algorithms for Large Scale Optimization. - Decentralized Consensus Optimization and Resource Allocation. - Communication-Efficient Distributed Optimization of Self-concordant Empirical Loss. - Numerical Construction of Nonsmooth Control Lyapunov Functions. - Convergence of an Inexact Majorization-Minimization Method for Solving a Class of Composite Optimization Problems.
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