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  • 1.
    Daneva (Mitradjieva), Maria
    et al.
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Larsson, Torbjörn
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Patriksson, Michael
    Mathematical Sciences, Chalmers University of Technology and Göteborg University, Gothenburg, Sweden.
    Rydergren, Clas
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    A Comparison of Feasible Direction Methods for the Stochastic Transportation Problem2010In: Computational optimization and applications, ISSN 0926-6003, E-ISSN 1573-2894, Vol. 46, no 3, p. 451-466Article in journal (Refereed)
    Abstract [en]

    The feasible direction method of Frank and Wolfe has been claimed to be efficient for solving the stochastic transportation problem. While this is true for very moderate accuracy requirements, substantially more efficient algorithms are otherwise diagonalized Newton and conjugate Frank–Wolfe algorithms, which we describe and evaluate. Like the Frank–Wolfe algorithm, these two algorithms take advantage of the structure of the stochastic transportation problem. We also introduce a Frank–Wolfe type algorithm with multi-dimensional search; this search procedure exploits the Cartesian product structure of the problem. Numerical results for two classic test problem sets are given. The three new methods that are considered are shown to be superior to the Frank–Wolfe method, and also to an earlier suggested heuristic acceleration of the Frank–Wolfe method.

  • 2.
    Kiwiel, Krzysztof C
    et al.
    Systems Research Institute, Newelska.
    Olov Lindberg, Per
    Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
    Nou, Andreas
    Royal Institute of Technology, Stockholm.
    Bregman proximal relaxation of large-scale 0-1 problems2000In: Computational optimization and applications, ISSN 0926-6003, E-ISSN 1573-2894, Vol. 15, no 1, p. 33-44Article in journal (Refereed)
    Abstract [en]

    We apply a recent extension of the Bregman proximal method for convex programming to LP relaxations of 0-1 problems. We allow inexact subproblem solutions obtained via dual ascent, increasing their accuracy successively to retain global convergence. Our framework is applied to relaxations of large-scale set covering problems that arise in airline crew scheduling. Approximate relaxed solutions are used to construct primal feasible solutions via a randomized heuristic. Encouraging preliminary experience is reported.

  • 3.
    Larsson, Torbjörn
    et al.
    Linköping University, Department of Mathematics, Optimization .
    Yuan, Di
    Linköping University, The Institute of Technology. Linköping University, Department of Science and Technology.
    An augmented Lagrangian algorithm for large scale multicommodity routing2004In: Computational optimization and applications, ISSN 0926-6003, E-ISSN 1573-2894, Vol. 27, p. 187-215Article in journal (Refereed)
1 - 3 of 3
CiteExportLink to result list
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Citation style
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  • harvard1
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  • modern-language-association-8th-edition
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  • Other style
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  • de-DE
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