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Risk-Averse Optimization and Control

Theory and Methods

  • Book
  • Jun 2024

Overview

Authors:
  1. Darinka Dentcheva

    1. Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, USA

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    You can also search for this author in PubMed   Google Scholar

  2. Andrzej Ruszczyński

    1. Department of Management Science and Information Systems, Rutgers University, Piscataway, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Comprehensive presentation of the rapidly growing fields of risk-averse optimization and control
  • Self-contained presentation of the theory of measures of risk
  • Many examples included

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Hardcover Book USD 129.00
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Keywords

  • Risk-Averse Optimization
  • Risk Measures
  • Risk-Averse Dynamic Programming
  • Stochastic Dominance,
  • Risk-Averse Control

About this book

This book offers a comprehensive presentation of the theory and methods of risk-averse optimization and control. Problems of this type arise in finance, energy production and distribution, supply chain management, medicine, and many other areas, where not only the average performance of a stochastic system is essential, but also high-impact and low-probability events must be taken into account. The book is a self-contained presentation of the utility theory, the theory of measures of risk, including systemic and dynamic measures of risk, and their use in optimization and control models. It also covers stochastic dominance relations and their application as constraints in optimization models. Optimality conditions for problems with nondifferentiable and nonconvex functions and operators involving risk measures and stochastic dominance relations are discussed. Much attention is paid to multi-stage risk-averse optimization problems and to risk-averse Markov decision problems.

 

Specialized algorithms for solving risk-averse optimization and control problems are presented and analyzed: stochastic subgradient methods for risk optimization, decomposition methods for dynamic problems, event cut and dual methods for stochastic dominance constraints, and policy iteration methods for control problems.

 

The target audience is researchers and graduate students in the areas of mathematics, business analytics, insurance and finance, engineering, and computer science. The theoretical considerations are illustrated with examples, which make the book useful material for advanced courses in the area.

 

Authors and Affiliations

  • Department of Mathematical Sciences, Stevens Institute of Technology, Hoboken, USA

    Darinka Dentcheva

  • Department of Management Science and Information Systems, Rutgers University, Piscataway, USA

    Andrzej Ruszczyński

Bibliographic Information

  • Book Title: Risk-Averse Optimization and Control

  • Book Subtitle: Theory and Methods

  • Authors: Darinka Dentcheva, Andrzej Ruszczyński

  • Series Title: Springer Series in Operations Research and Financial Engineering

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-57987-5Due: 19 June 2024

  • Softcover ISBN: 978-3-031-57990-5Due: 19 June 2024

  • eBook ISBN: 978-3-031-57988-2Due: 19 June 2024

  • Series ISSN: 1431-8598

  • Series E-ISSN: 2197-1773

  • Edition Number: 1

  • Number of Pages: XIV, 451

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