Globally and superlinearly convergent trust-region algorithm for convex SC1-minimization problems and its application to stochastic programs

H. Jiang, Liqun Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

A function mapping from ℛnto ℛ is called an SC1-function if it is diflerentiable and its derivative is semismooth. A convex SC1-minimization problem is a convex minimization problem with an SC1-objective function and linear constraints. Applications of such minimization problems include stochastic quadratic programming and minimax problems. In this paper, we present a globally and superlinearly convergent trust-region algorithm for solving such a problem. Numerical examples are given on the application of this algorithm to stochastic quadratic programs.
Original languageEnglish
Pages (from-to)649-669
Number of pages21
JournalJournal of Optimization Theory and Applications
Volume90
Issue number3
DOIs
Publication statusPublished - 1 Jan 1996
Externally publishedYes

Keywords

  • Global convergence
  • Stochastic quadratic programs
  • Superlinear convergence
  • Trust-region algorithms

ASJC Scopus subject areas

  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

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