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A lifting method for generalized semi-infinite programs based on lower level Wolfe duality

Moritz Diehl (moritz.diehl***at***esat.kuleuven.be)
Boris Houska (boris.houska***at***esat.kuleuven.be)
Oliver Stein (stein***at***kit.edu)
Paul Steuermann (steuermann***at***kit.edu)

Abstract: This paper introduces novel numerical solution strategies for generalized semi-infinite optimization problems (GSIP), a class of mathematical optimization problems which occur naturally in the context of design centering problems, robust optimization problems, and many fields of engineering science. GSIPs can be regarded as bilevel optimization problems, where a parametric lower-level maximization problem has to be solved in order to check feasibility of the upper level minimization problem. The current paper discusses several strategies to reformulate this class of problems into equivalent standard minimization problems by exploiting the concept of lower level Wolfe duality. Here, the main contribution is the discussion of the non-degeneracy of the corresponding formulations under various assumptions. Finally, these non-degenerate re-formulations of the original GSIP allow us to apply standard nonlinear optimization algorithms.

Keywords: Semi-infinite optimization, lower level duality, lifting approach, adaptive convexification, mathematical program with complementarity constraints

Category 1: Infinite Dimensional Optimization (Semi-infinite Programming )

Category 2: Robust Optimization

Category 3: Complementarity and Variational Inequalities

Citation: Computational Optimization and Applications, DOI: 10.1007/s10589-012-9489-4


Entry Submitted: 12/16/2011
Entry Accepted: 12/16/2011
Entry Last Modified: 06/05/2012

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