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Large-scale Optimization with the Primal-Dual Column Generation Method

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dc.contributor.author Gondzio, J
dc.contributor.author González-Brevis, P
dc.contributor.author Munari, P
dc.date.accessioned 2016-10-07T22:09:51Z
dc.date.available 2016-10-07T22:09:51Z
dc.date.issued 2016
dc.identifier.citation J. Gondzio, P. González-Brevis and P. Munari, Large-scale Optimization with the Primal-Dual Column Generation Method. Mathematical Programming Computation 8 (2016) 47-82. es_CL
dc.identifier.uri es_CL
dc.identifier.uri http://hdl.handle.net/11447/760
dc.description.abstract The primal-dual column generation method (PDCGM) is a general-purpose column generation technique that relies on the primal-dual interior point method to solve the restricted master problems. The use of this interior point method variant allows to obtain suboptimal and well-centered dual solutions which naturally stabilizes the column generation process. As recently presented in the literature, reductions in the number of calls to the oracle and in the CPU times are typically observed when compared to the standard column generation, which relies on extreme optimal dual solutions. However, these results are based on relatively small problems obtained from linear relaxations of combinatorial applications. In this paper, we investigate the behaviour of the PDCGM in a broader context, namely when solving large-scale convex optimization problems. We have selected applications that arise in important real-life contexts such as data analysis (multiple kernel learning problem), decision-making under uncertainty (two-stage stochastic programming problems) and telecommunication and transportation networks (multicommodity network flow problem). In the numerical experiments, we use publicly available benchmark instances to compare the performance of the PDCGM against recent results for different methods presented in the literature, which were the best available results to date. The analysis of these results suggests that the PDCGM offers an attractive alternative over specialized methods since it remains competitive in terms of number of iterations and CPU times even for large-scale optimization problems. es_CL
dc.format.extent 47-82 es_CL
dc.publisher es_CL
dc.title Large-scale Optimization with the Primal-Dual Column Generation Method es_CL
dc.type Artículo es_CL


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