A globally convergent Newton method for convex SC1 minimization problems

J. S. Pang; Liqun Qi

doi:10.1007/BF02193060

A globally convergent Newton method for convex SC¹ minimization problems

J. S. Pang
, Liqun Qi

Research output: Journal article publication › Journal article › Academic research › peer-review

61 Citations (Scopus)

Abstract

This paper presents a globally convergent and locally superlinearly convergent method for solving a convex minimization problem whose objective function has a semismooth but nondifferentiable gradient. Applications to nonlinear minimax problems, stochastic programs with recourse, and their extensions are discussed.

Original language	English
Pages (from-to)	633-648
Number of pages	16
Journal	Journal of Optimization Theory and Applications
Volume	85
Issue number	3
DOIs	https://doi.org/10.1007/BF02193060
Publication status	Published - 1 Jun 1995
Externally published	Yes

Keywords

Newton method
Nonsmooth optimization

ASJC Scopus subject areas

Control and Optimization
Management Science and Operations Research
Applied Mathematics

More information

10.1007/BF02193060

Cite this

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title = "A globally convergent Newton method for convex SC1 minimization problems",

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AB - This paper presents a globally convergent and locally superlinearly convergent method for solving a convex minimization problem whose objective function has a semismooth but nondifferentiable gradient. Applications to nonlinear minimax problems, stochastic programs with recourse, and their extensions are discussed.

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KW - Nonsmooth optimization

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DO - 10.1007/BF02193060

M3 - Journal article

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JF - Journal of Optimization Theory and Applications

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