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Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
Victor Picheny · Robert B Gramacy · Stefan Wild · Sebastien Le Digabel

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #33

An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems which are then usually solved with local solvers. Recently, surrogate-based Bayesian optimization (BO) sub-solvers have been successfully deployed in the AL framework for a more global search in the presence of inequality constraints; however a drawback was that expected improvement (EI) evaluations relied on Monte Carlo. Here we introduce an alternative slack variable AL, and show that in this formulation the EI may be evaluated with library routines. The slack variables furthermore facilitate equality as well as inequality constraints, and mixtures thereof. We show our new slack "ALBO" compares favorably to the original. Its superiority over conventional alternatives is reinforced on several new mixed constraint examples.

Author Information

Victor Picheny (Institut National de la Recherche Agronomique)
Robert B Gramacy (Virginia Tech)
Stefan Wild (Argonne National Lab)
Sebastien Le Digabel (École Polytechnique de Montréal)

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