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SIAM Journal on Optimization
Article . 2005 . Peer-reviewed
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Article . 2020
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An Infeasible Bundle Method for Nonsmooth Convex Constrained Optimization without a Penalty Function or a Filter

An infeasible bundle method for nonsmooth convex constrained optimization without a penalty function or a filter
Authors: Claudia A. Sagastizábal; Mikhail V. Solodov;

An Infeasible Bundle Method for Nonsmooth Convex Constrained Optimization without a Penalty Function or a Filter

Abstract

From the abstract: In this paper, we demonstrate that the use of a parametrized penalty function in nonsmooth convex optimization can be avoided without using the relatively complex filter methods. We propose an approach which appears to be more direct and easier to implement, in the sense that it is closer in the spirit and structure to the well-developed unconstrained bundle methods. Preliminary computational results are also reported.

Keywords

Convex programming, Numerical mathematical programming methods, bundle methods, Nonsmooth analysis, nonsmooth convex optimization, constrained optimization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
69
Top 10%
Top 10%
Top 10%
bronze