Improving feasible directions for a class of nondifferentiable functions

This paper describes a method to obtain improving feasible directions for the problem of minimizing a convex, nondifferentiable function subject to linear constraints. The method requires the knowledge of the subgradient of the function at each point and its projection on the null space of the gradi...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 34; no. 1; pp. 99 - 104
Main Author Tamarit Goerlich, JoséManuel
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 1988
Elsevier
Elsevier Sequoia S.A
SeriesEuropean Journal of Operational Research
Subjects
Applied sciences
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Exact sciences and technology
Mathematical models
Mathematical programming
nondifferentiable optimization
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
subgradient methods
nondifferentiable optimization
subgradient methods
Nonlinear programming
Non differential programming
Non linear programming
Feasible direction method
Mathematical programming
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ISSN0377-2217
1872-6860
DOI10.1016/0377-2217(88)90460-2

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Summary:This paper describes a method to obtain improving feasible directions for the problem of minimizing a convex, nondifferentiable function subject to linear constraints. The method requires the knowledge of the subgradient of the function at each point and its projection on the null space of the gradients of the binding constraints. We also report the results obtained by applying this method to the optimization of some piecewise linear functions.
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ISSN:0377-2217
1872-6860
DOI:10.1016/0377-2217(88)90460-2