Probabilistic and Randomized Methods for Design under Uncertainty

  • Giuseppe Calafiore
  • Fabrizio Dabbene

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Chance-Constrained and Stochastic Optimization

    1. Front Matter
      Pages 1-1
    2. Arkadi Nemirovski, Alexander Shapiro
      Pages 3-47
    3. James C. Spall, Stacy D. Hill, David R. Stark
      Pages 99-117
    4. Andrzej Ruszczyński, Alexander Shapiro
      Pages 119-157
  3. Robust Optimization and Random Sampling

    1. Front Matter
      Pages 159-159
    2. Giuseppe Calafiore, Marco C. Campi
      Pages 161-188
    3. Vivek F. Farias, Benjamin Van Roy
      Pages 189-201
    4. Giuseppe Calafiore, Fabrizio Dabbene
      Pages 203-221
    5. Vladislav B. Tadić, Sean P. Meyn, Roberto Tempo
      Pages 243-261
  4. Probabilistic Methods in Identification and Control

    1. Front Matter
      Pages 263-263
    2. Mathukumalli Vidyasagar, Rajeeva L. Karandikar
      Pages 265-302
    3. Constantino M. Lagoa, Xiang Li, Maria Cecilia Mazzaro, Mario Sznaier
      Pages 331-363
    4. Peter F. Hokayem, Silvia Mastellone, Chaouki T. Abdallah
      Pages 365-379
    5. Qian Wang, Robert F. Stengel
      Pages 381-414
    6. Xinjia Chen, Kemin Zhou, Jorge Aravena
      Pages 415-431
  5. Back Matter
    Pages 433-457

About this book


In many engineering design and optimization problems, the presence of uncertainty in the data is a central and critical issue. Different fields of engineering use different ways to describe this uncertainty and adopt a variety of techniques to devise designs that are at least partly insensitive or robust to uncertainty.

Probabilistic and Randomized Methods for Design under Uncertainty examines uncertain systems in control engineering and general decision or optimization problems for which data is not known exactly. Gathering contributions from the world’s leading researchers in optimization and robust control; this book highlights the interactions between these two fields, and focuses on new randomised and probabilistic techniques for solving design problems in the presence of uncertainty:

  • Part I describes general theory and solution methodologies for probability-constrained and stochastic optimization problems, including chance-constrained optimization, stochastic optimization and risk measures;
  • Part II focuses on numerical methods for solving randomly perturbed convex programs and semi-infinite optimization problems by probabilistic techniques such as constraint sampling and scenario-based optimization;
  • Part III details the theory and applications of randomized techniques to the analysis and design of robust control systems.

Probabilistic and Randomized Methods for Design under Uncertainty will be of interest to researchers, academics and postgraduate students in control engineering and operations research as well as professionals working in operations research who are interested in decision-making, optimization and stochastic modeling.


Analysis Stochastic Optimization Stochastic model Stochastic modelling control engineering modeling operations research optimization

Editors and affiliations

  • Giuseppe Calafiore
    • 1
  • Fabrizio Dabbene
    • 2
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly
  2. 2.IEIIT-CNRPolitecnico di TorinoTorinoItaly

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag London Limited 2006
  • Publisher Name Springer, London
  • eBook Packages Engineering
  • Print ISBN 978-1-84628-094-8
  • Online ISBN 978-1-84628-095-5
  • Buy this book on publisher's site