Trust-region algorithms for nonlinear stochastic programming and mixed logit models

  • Fabian Bastin

    Student thesis: Doc typesDoctor of Sciences


    This work is concerned with the study of nonlinear nonconvex stochastic programming, in particular in the context of trust-region approaches. We first explore how to exploit the structure of multistage stochastic nonlinear programs with linear constraints, in the framework of primal-dual interior point methods. We next study consistency of sample average approximations (SAA) for general nonlinear stochastic programs. We also develop a new algorithm to solve the SAA problem, using the statistical inference information to reduce numercial costs, by means of an internal variable sample size strategy. We finally assess the numerical efficiency of the proposed method for the estimation of discrete choice models, more precisely mixed logit models, using our software AMLET, written for this purpose.
    Date of Award12 Mar 2004
    Original languageEnglish
    Awarding Institution
    • University of Namur
    SupervisorPhilippe TOINT (Supervisor), Francois LOUVEAUX (Co-Supervisor), Annick Sartenaer (Jury), Marcel Remon (Jury), Michel Bierlaire (Jury) & John Polak (Jury)


    • Discrete choice
    • Mixed logit
    • Nonlinear programming
    • Trust-region
    • Stochastic programming

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