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    • Livre
    Sélectionner

    Applied mathematical programming

    Bradley, Stephen P
    Hax, Arnoldo C, Magnanti, Thomas L
    Reading Mass. ; Menlo Park Cal. : Addison-Wesley
    [1977]
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    Titre: Applied mathematical programming / Stephen P. Bradley, Arnoldo C. Hax, Thomas L. Magnanti
    Auteur: Bradley, Stephen P
    Contributeur: Hax, Arnoldo C; Magnanti, Thomas L
    Editeur: Reading Mass. ; Menlo Park Cal. : Addison-Wesley
    Date: [1977]
    Collation: XVI, 716 p. : fig. ; 25 cm
    Sujet RERO: Recherche opérationnelle - Programmation (mathématiques)
    Classification: IMATH 12
    Identifiant: 020100464X (ISBN)
    No RERO: 0185243
    Permalien:
    http://data.rero.ch/01-0185243/html?view=FR_V1

    • Livre
    Sélectionner

    Network flows : theory, algorithms, and applications

    Ahuja, Ravindra K, Magnanti, Thomas L, Orlin, James B
    Englewood Cliffs N.J. : Prentice Hall
    [1993]
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    Titre: Network flows : theory, algorithms, and applications / Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin
    Auteur: Ahuja, Ravindra K; Magnanti, Thomas L; Orlin, James B
    Editeur: Englewood Cliffs N.J. : Prentice Hall
    Date: [1993]
    Collation: XV, 846 p. : ill. ; 25 cm
    Sujet RERO: Communication - Analyse de réseau - Optimisation mathématique
    Note: Bibliogr.: p. 821-839. Index
    Classification: DIUF 3.7
    LC T57.85
    Identifiant: 013617549X (ISBN); http://catalogue.bnf.fr/ark:/12148/cb374811664 (URN)
    No RERO: 1876470
    Permalien:
    http://data.rero.ch/01-1876470/html?view=FR_V1

    • Article
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    Separable Concave Optimization Approximately Equals Piecewise-Linear Optimization

    Magnanti, Thomas L., Stratila, Dan
    Cornell University
    Disponible
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    Titre: Separable Concave Optimization Approximately Equals Piecewise-Linear Optimization
    Auteur: Magnanti, Thomas L.; Stratila, Dan
    Sujet: Mathematics - Optimization And Control
    Description: We study the problem of minimizing a nonnegative separable concave function over a compact feasible set. We approximate this problem to within a factor of 1+epsilon by a piecewise-linear minimization problem over the same feasible set. Our main result is that when the feasible set is a polyhedron, the number of resulting pieces is polynomial in the input size of the polyhedron and linear in 1/epsilon. For many practical concave cost problems, the resulting piecewise-linear cost problem can be formulated as a well-studied discrete optimization problem. As a result, a variety of polynomial-time exact algorithms, approximation algorithms, and polynomial-time heuristics for discrete optimization problems immediately yield fully polynomial-time approximation schemes, approximation algorithms, and polynomial-time heuristics for the corresponding concave cost problems. We illustrate our approach on two problems. For the concave cost multicommodity flow problem, we devise a new heuristic and study its performance using computational experiments. We are able to approximately solve significantly larger test instances than previously possible, and obtain solutions on average within 4.27% of optimality. For the concave cost facility location problem, we obtain a new 1.4991+epsilon approximation algorithm. Comment: Full paper
    Identifiant: 1201.3148 (ARXIV ID)

    • Article
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    Strongly Polynomial Primal-Dual Algorithms for Concave Cost Combinatorial Optimization Problems

    Magnanti, Thomas L., Stratila, Dan
    Cornell University
    Disponible
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    Titre: Strongly Polynomial Primal-Dual Algorithms for Concave Cost Combinatorial Optimization Problems
    Auteur: Magnanti, Thomas L.; Stratila, Dan
    Sujet: Mathematics - Optimization And Control
    Description: We introduce an algorithm design technique for a class of combinatorial optimization problems with concave costs. This technique yields a strongly polynomial primal-dual algorithm for a concave cost problem whenever such an algorithm exists for the fixed-charge counterpart of the problem. For many practical concave cost problems, the fixed-charge counterpart is a well-studied combinatorial optimization problem. Our technique preserves constant factor approximation ratios, as well as ratios that depend only on certain problem parameters, and exact algorithms yield exact algorithms. Using our technique, we obtain a new 1.61-approximation algorithm for the concave cost facility location problem. For inventory problems, we obtain a new exact algorithm for the economic lot-sizing problem with general concave ordering costs, and a 4-approximation algorithm for the joint replenishment problem with general concave individual ordering costs.
    Identifiant: 1202.2654 (ARXIV ID)

    • Plusieurs versions

    A Comparison of Mixed-Integer Programming Models for Nonconvex Piecewise Linear Cost Minimization Problems

    Gendron, Bernard, Magnanti, Thomas L.
    Management Science, 1 September 2003, Vol.49(9), pp.1268-1273 [Revue évaluée par les pairs]

    • Plusieurs versions

    Beyond normality: A cross moment-stochastic user equilibrium model

    Ahipaşaoğlu, Selin Damla, Meskarian, Rudabeh, Magnanti, Thomas L, Natarajan, Karthik
    Transportation Research Part B, November 2015, Vol.81, pp.333-354 [Revue évaluée par les pairs]

    • Plusieurs versions

    Solving variational inequality and fixed point problems by line searches and potential optimization

    Magnanti, Thomas L., Perakis, Georgia
    Mathematical Programming, 2004, Vol.101(3), pp.435-461 [Revue évaluée par les pairs]

    • Plusieurs versions

    Network flow models for designing diameter‐constrained minimum‐spanning and Steiner trees

    Gouveia, Luis, Magnanti, Thomas L.
    Networks, May 2003, Vol.41(3), pp.159-173 [Revue évaluée par les pairs]

    • Plusieurs versions

    Facets and Reformulations for Solving Production Planning with Changeover Costs

    Magnanti, Thomas L., Sastry, Trilochan
    Operations Research, 1 July 2002, Vol.50(4), pp.708-719 [Revue évaluée par les pairs]

    • Plusieurs versions

    Modeling and Heuristic Worst-Case Performance Analysis of the Two-Level Network Design Problem

    Balakrishnan, Anantaram, Magnanti, Thomas L., Mirchandani, Prakash
    Management Science, 1 July 1994, Vol.40(7), pp.846-867 [Revue évaluée par les pairs]