Constraint Programming and Safe Global Optimization

Abstract : We investigate the capabilities of constraints programming techniques in rigor- ous global optimization methods. We introduce different constraint programming techniques to reduce the gap between efficient but unsafe systems like Baron1, and safe but slow global optimization approaches. We show how constraint program- ming filtering techniques can be used to implement optimality-based reduction in a safe and efficient way, and thus to take advantage of the known bounds of the ob- jective function to reduce the domain of the variables, and to speed up the search of a global optimum. We describe an efficient strategy to compute very accurate approximations of feasible points. This strategy takes advantage of the Newton method for under-constrained systems of equalities and inequalities to compute efficiently a promising upper bound. Experiments on the COCONUT benchmarks demonstrate that these different techniques drastically improve the performances.
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Communication dans un congrès
SCAN 2008, 3th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Verified Numerical Computations, Sep 2008, El Paso, TX, United States
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Dernière modification le : jeudi 9 juillet 2015 - 14:43:06
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  • HAL Id : hal-00510305, version 1

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Alexandre Goldsztejn, Yahia Lebbah, Claude Michel, Michel Rueher. Constraint Programming and Safe Global Optimization. SCAN 2008, 3th GAMM - IMACS International Symposium on Scientific Computing, Computer Arithmetic and Verified Numerical Computations, Sep 2008, El Paso, TX, United States. <hal-00510305>

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