Newton-type methods for stochastic programming

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6 Citations (Scopus)

Abstract

Stochastic programming is concerned with practical procedures for decision making under uncertainty, by modelling uncertainties and risks associated with decision in a form suitable for optimization. The field is developing rapidly with contributions from many disciplines such as operations research, probability and statistics, and economics. A stochastic linear program with recourse can equivalently be formulated as a convex programming problem. The problem is often large-scale as the objective function involves an expectation, either over a discrete set of scenarios or as a multi-dimensional integral. Moreover, the objective function is possibly nondifferentiable. This paper provides a brief overview of recent developments on smooth approximation techniques and Newton-type methods for solving two-stage stochastic linear programs with recourse, and parallel implementation of these methods. A simple numerical example is used to signal the potential of smoothing approaches. (C) 2000 Elsevier Science Ltd.
Original languageEnglish
Pages (from-to)89-98
Number of pages10
JournalMathematical and Computer Modelling
Volume31
Issue number10-12
DOIs
Publication statusPublished - 22 May 2000
Externally publishedYes

Keywords

  • Newton-type methods
  • Smooth approximation techniques
  • Stochastic programming
  • Two-stage stochastic linear programs with recourse

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computer Science Applications

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