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Mathematical Programming
Article . 1992 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article . 1992
Data sources: zbMATH Open
DBLP
Article . 1992
Data sources: DBLP
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A polynomial method of approximate centers for linear programming

Authors: Kees Roos; Jean-Philippe Vial;

A polynomial method of approximate centers for linear programming

Abstract

The introduction and the last section illustrate clearly how this paper relates to the literature. The authors give a general picture of the stream of research that evolved from the publication of the Karmarkar method in 1984 and resulted in the development of a number of so-called interior point methods. Such methods are discovered to be strictly interrelated. For example, in a previous paper of the second author, it is stated that ``the projective algorithms for a phase II problems are all the same''. The method proposed here is essentially that of \textit{C. C. Gonzaga} [SIAM Rev. 34, No. 2, 167-224 (1992; Zbl 0763.90063)] and hence belongs to the category of path following methods. So, where is the contribution? It is in the convergence analysis. Providing a simple and elegant proof of the polynomial complexity of the method is the main topic of the paper. The method has its limitations --- the feasible regions of both the problem and its dual must have non-empty interiors and the coefficient matrix full row rank (although this latter hypothesis is claimed to be not essential). It is interesting to note that, in contrast with the promise of developing, in a subsequent note, some trickery that will lead to a record complexity, the method is deluding, when it comes to practical efficiency. The remedies are in the spirit of other already proposed methods. For the high quality of this paper, it leaves the desire of refreshing novelty in either the approach or the class of problems dealt with. This stream of research seems to be close to saturate its potential. Fortunately a revival of interest toward nonlinear optimization is emerging within the field of interior point methods.

Related Organizations
Keywords

interior point methods, Linear programming, Computational methods for problems pertaining to operations research and mathematical programming, path following methods, Abstract computational complexity for mathematical programming problems, convergence analysis

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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
Average
Top 10%
Top 10%
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