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Communication Dans Un Congrès Année : 2022

Learning hidden constraints using a Stepwise Uncertainty Reduction strategy with Gaussian Process Classifiers

Morgane Menz
Delphine Sinoquet

Résumé

This work is motivated by optimization applications based on complex and expensive black-box simulators. Our goal is therefore to obtain the best improvement in function minimization with the least number of simulations. We propose a new approach to escape local minima with the local Derivative-Free Optimization Trust-region method for mixed continuous and discrete problems (DFOb). The latter method is based on two main steps: successive continuous quadratic subproblems and mixed binary quadratic subproblems which are both based on interpolation models defined for mixed variables, valid in an adaptive trust region. In order to force exploration for binary variables, "no-good cut" constraints are added to force the algorithm to explore outside the previously explored regions. A restart procedure is integrated in the DFOb method: it resets the size of the trust region and allows an enrichment of the simulation dataset for exploration whenever the algorithm is no longer making sufficient progress. Specifically, our strategy for choosing these new simulations relies on a novel design of experiment method that uses kernel-embedding of probability distributions adapted to mixed variables, allowing to take into account any available prior information on the type of problem we are dealing with.
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Dates et versions

hal-03688224 , version 1 (03-06-2022)

Identifiants

  • HAL Id : hal-03688224 , version 1

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Morgane Menz, Miguel Munoz Zuniga, Delphine Sinoquet. Learning hidden constraints using a Stepwise Uncertainty Reduction strategy with Gaussian Process Classifiers. Optimization days 2022, May 2022, Montréal, Canada. ⟨hal-03688224⟩

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