Optimization and Its Applications in Control and Data Sciences

In Honor of Boris T. Polyak’s 80th Birthday

  • Boris Goldengorin

Part of the Springer Optimization and Its Applications book series (SOIA, volume 115)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Alexander S. Belenky, Lyudmila G. Egorova
    Pages 51-117
  3. Daniel Berend, Luba Sapir
    Pages 119-138
  4. Giuseppe C. Calafiore, Luca Carlone, Frank Dellaert
    Pages 139-184
  5. Patrizio Colaneri, Paolo Bolzern, José C. Geromel, Grace S. Deaecto
    Pages 185-219
  6. Steven Diamond, Stephen Boyd
    Pages 221-264
  7. Zoltán Horváth, Yunfei Song, Tamás Terlaky
    Pages 265-280
  8. Anders Lindquist, Giorgio Picci
    Pages 281-314
  9. Sergey I. Lyashko, Dmitry A. Klyushin, Vladimir V. Semenov, Maryna V. Prysiazhna, Maksym P. Shlykov
    Pages 327-340
  10. Daniel N. Mohsenizadeh, Vilma A. Oliveira, Lee H. Keel, Shankar P. Bhattacharyya
    Pages 341-351
  11. Yurii Nesterov, Vladimir Shikhman
    Pages 381-435

About this book


This book focuses on recent research in modern optimization and its implications in control and data analysis. This book is a collection of papers from the conference “Optimization and Its Applications in Control and Data Science” dedicated to Professor Boris T. Polyak, which was held in Moscow, Russia on May 13-15, 2015.

This book reflects developments in theory and applications rooted by Professor Polyak’s fundamental contributions to constrained and unconstrained optimization, differentiable and nonsmooth functions, control theory and approximation. Each paper focuses on techniques for solving complex optimization problems in different application areas and recent developments in optimization theory and methods. Open problems in optimization, game theory and control theory are included in this collection which will interest engineers and researchers working with efficient algorithms and software for solving optimization problems in market and data analysis. Theoreticians in operations research, applied mathematics, algorithm design, artificial intelligence, machine learning, and software engineering will find this book useful and graduate students will find the state-of-the-art research valuable.


Data analysis Dynamical Systems Linear programming Markovian Jumps Nonconvex programming Nonlinear programming Nonparametric ellipsoidal approximation global optimization

Editors and affiliations

  • Boris Goldengorin
    • 1
  1. 1.Department of Industrial and Systems EngineeringOhio UniversityAthensUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-42054-7
  • Online ISBN 978-3-319-42056-1
  • Series Print ISSN 1931-6828
  • Series Online ISSN 1931-6836
  • Buy this book on publisher's site
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