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

H. Jiang; Liqun Qi

doi:10.1007/BF02189800

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 publication › Journal article › Academic research › peer-review

8 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 language	English
Pages (from-to)	649-669
Number of pages	21
Journal	Journal of Optimization Theory and Applications
Volume	90
Issue number	3
DOIs	https://doi.org/10.1007/BF02189800
Publication status	Published - 1 Jan 1996
Externally published	Yes

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

More information

10.1007/BF02189800

Cite this

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AB - 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.

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KW - Stochastic quadratic programs

KW - Superlinear convergence

KW - Trust-region algorithms

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