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Stochastic Programming

  • Book
  • © 1995

Overview

Authors:
  1. András Prékopa

    1. RUTCOR, Rutgers Center for Operations Research, Rutgers University, New Brunswick, USA
      Department of Operations Research, Loránd Eötvös University, Budapest, Hungary

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Part of the book series: Mathematics and Its Applications (MAIA, volume 324)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xviii
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  2. General Theory of Linear Programming

    • András Prékopa
    Pages 1-33

  3. Convex Polyhedra

    • András Prékopa
    Pages 35-57

  4. Special Problems and Methods

    • András Prékopa
    Pages 59-85

  5. Logconcave and Quasi-Concave Measures

    • András Prékopa
    Pages 87-123

  6. Moment Problems

    • András Prékopa
    Pages 125-178

  7. Bounding and Approximation of Probabilities

    • András Prékopa
    Pages 179-217

  8. Statistical Decisions

    • András Prékopa
    Pages 219-232

  9. Static Stochastic Programming Models

    • András Prékopa
    Pages 233-268

  10. Solutions of the Simple Recourse Problem

    • András Prékopa
    Pages 269-299

  11. Convexity Theory of Probabilistic Constrained Problems

    • András Prékopa
    Pages 301-317

  12. Programming under Probabilistic Constraint and Maximizing Probabilities under Constraints

    • András Prékopa
    Pages 319-371

  13. Two-Stage Stochastic Programming Problems

    • András Prékopa
    Pages 373-423

  14. Multi-Stage Stochastic Programming Problems

    • András Prékopa
    Pages 425-446

  15. Special Cases and Selected Applications

    • András Prékopa
    Pages 447-500

  16. Distribution Problems

    • András Prékopa
    Pages 501-539

  17. Back Matter

    Pages 541-599
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Keywords

About this book

Stochastic programming - the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques - lies at the intersection of statistics and mathematical programming. The book Stochastic Programming is a comprehensive introduction to the field and its basic mathematical tools. While the mathematics is of a high level, the developed models offer powerful applications, as revealed by the large number of examples presented. The material ranges form basic linear programming to algorithmic solutions of sophisticated systems problems and applications in water resources and power systems, shipbuilding, inventory control, etc.
Audience: Students and researchers who need to solve practical and theoretical problems in operations research, mathematics, statistics, engineering, economics, insurance, finance, biology and environmental protection.

Authors and Affiliations

  • RUTCOR, Rutgers Center for Operations Research, Rutgers University, New Brunswick, USA

    András Prékopa

  • Department of Operations Research, Loránd Eötvös University, Budapest, Hungary

    András Prékopa

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