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Large-Scale and Distributed Optimization

  • Book
  • © 2018

Overview

Editors:
  1. Pontus Giselsson

    1. Department of Automatic Control, Lund University, Lund, Sweden

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    You can also search for this editor in PubMed   Google Scholar

  2. Anders Rantzer

    1. Department of Automatic Control, Lund University, Lund, Sweden

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    You can also search for this editor in PubMed   Google Scholar

  • Contributes to current and upcoming research on large-scale and distributed optimization
  • Covers the increasingly important tools and methods for large-scale optimization
  • Offers a valuable source of information to both researchers and students

Part of the book series: Lecture Notes in Mathematics (LNM, volume 2227)

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Large-Scale and Distributed Optimization: An Introduction

    • Pontus Giselsson, Anders Rantzer
    Pages 1-10

  3. Exploiting Chordality in Optimization Algorithms for Model Predictive Control

    • Anders Hansson, Sina Khoshfetrat Pakazad
    Pages 11-32

  4. Decomposition Methods for Large-Scale Semidefinite Programs with Chordal Aggregate Sparsity and Partial Orthogonality

    • Yang Zheng, Giovanni Fantuzzi, Antonis Papachristodoulou
    Pages 33-55

  5. Smoothing Alternating Direction Methods for Fully Nonsmooth Constrained Convex Optimization

    • Quoc Tran-Dinh, Volkan Cevher
    Pages 57-95

  6. Primal-Dual Proximal Algorithms for Structured Convex Optimization: A Unifying Framework

    • Puya Latafat, Panagiotis Patrinos
    Pages 97-120

  7. Block-Coordinate Primal-Dual Method for Nonsmooth Minimization over Linear Constraints

    • D. Russell Luke, Yura Malitsky
    Pages 121-147

  8. Stochastic Forward Douglas-Rachford Splitting Method for Monotone Inclusions

    • Volkan Cevher, Bằng Công VÅ©, Alp Yurtsever
    Pages 149-179

  9. Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints

    • Anastasia Bayandina, Pavel Dvurechensky, Alexander Gasnikov, Fedor Stonyakin, Alexander Titov
    Pages 181-213

  10. Frank-Wolfe Style Algorithms for Large Scale Optimization

    • Lijun Ding, Madeleine Udell
    Pages 215-245

  11. Decentralized Consensus Optimization and Resource Allocation

    • Angelia Nedić, Alexander Olshevsky, Wei Shi
    Pages 247-287

  12. Communication-Efficient Distributed Optimization of Self-concordant Empirical Loss

    • Yuchen Zhang, Lin Xiao
    Pages 289-341

  13. Numerical Construction of Nonsmooth Control Lyapunov Functions

    • Robert Baier, Philipp Braun, Lars Grüne, Christopher M. Kellett
    Pages 343-373

  14. Convergence of an Inexact Majorization-Minimization Method for Solving a Class of Composite Optimization Problems

    • Amir Beck, Dror Pan
    Pages 375-410

  15. Back Matter

    Pages 411-412
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Keywords

About this book

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.

Editors and Affiliations

  • Department of Automatic Control, Lund University, Lund, Sweden

    Pontus Giselsson, Anders Rantzer

About the editors

Pontus Giselsson is an Associate Professor at the Department of Automatic Control at Lund University, Sweden. His current research interests include mathematical optimization and its applications in, e.g., control, signal processing, and machine learning. He received a M.Sc. degree from Lund University in 2006 and a Ph.D. degree from Lund University in 2012. During 2013 and 2014, he was a postdoc at Stanford University. In 2012, he received the Young Author Price at an IFAC Symposium, in 2013 he was a finalist for the Best Student Paper Award at the American Control Conference, in 2014 he received the Young Author Price at the IFAC World Congress, and in 2015 he received the Ingvar Carlsson Award from the Swedish Foundation for Strategic Research.

Anders Rantzer received a PhD in 1991 from KTH, Stockholm, Sweden. After postdoctoral positions at KTH and at IMA, University of Minnesota, he joined Lund University in 1993 and was appointed professor of Automatic Control in 1999. The academic year of 2004/05 he was visiting associate faculty member at Caltech and 2015/16 he was Taylor Family Distinguished Visiting Professor at University of Minnesota. Since 2008 he coordinates the Linnaeus center LCCC at Lund University. Rantzer has been associate editor of IEEE Transactions on Automatic Control and several other journals. He is a winner of the SIAM Student Paper Competition, the IFAC Congress Young Author Price and the IET Premium Award for the best article in IEE Proceedings - Control Theory & Applications during 2006. He is a Fellow of IEEE and a member of the Royal Swedish Academy of Engineering Sciences. For the period 2013-15 he was also chairman of the Swedish Scientific Council for Natural and Engineering Sciences. His research interests are in modeling, analysis and synthesis of control systems, with particular attention to uncertainty, optimization and distributed control.

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