Column generation algorithms for nonlinear optimization, II: Numerical investigations
Artikel i vetenskaplig tidskrift, 2011

Garcia et al. [1] present a class of column generation (CG) algorithms for nonlinear programs. Its main motivation from a theoretical viewpoint is that under some circumstances, finite convergence can be achieved, in much the same way as for the classic simplicial decomposition method; the main practical motivation is that within the class there are certain nonlinear column generation problems that can accelerate the convergence of a solution approach which generates a sequence of feasible points. This algorithm can, for example, accelerate simplicial decomposition schemes by making the subproblems nonlinear. This paper complements the theoretical study on the asymptotic and finite convergence of these methods given in [1] with an experimental study focused on their computational efficiency. Three types of numerical experiments are conducted. The first group of test problems has been designed to study the parameters involved in these methods. The second group has been designed to investigate the role and the computation of the prolongation of the generated columns to the relative boundary. The last one has been designed to carry out a more complete investigation of the difference in computational efficiency between linear and nonlinear column generation approaches. In order to carry out this investigation, we consider two types of test problems: the first one is the nonlinear, capacitated single-commodity network flow problem of which several large-scale instances with varied degrees of nonlinearity and total capacity are constructed and investigated, and the second one is a combined traffic assignment model.

traffic assignment problem

transportation

combined modes

Simplicial decomposition

Convex

restricted simplicial decomposition

Nonlinear column generation

Network flow problems

minimization

network equilibrium

Författare

R. Garcia-Rodenas

Universidad de Castilla-La Mancha

A. Marin

Universidad Politecnica de Madrid

Michael Patriksson

Chalmers, Matematiska vetenskaper, Matematik

Göteborgs universitet

Computers and Operations Research

0305-0548 (ISSN)

Vol. 38 3 591-604

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1016/j.cor.2010.07.021

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Senast uppdaterat

2018-03-29