A progressive barrier derivative-free trust-region algorithm for constrained optimization

Charles Audet, Andrew R. Conn (), Sébastien Le Digabel and Mathilde Peyrega ()
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Charles Audet: École Polytechnique de Montréal
Andrew R. Conn: IBM T J Watson Research Center
Sébastien Le Digabel: École Polytechnique de Montréal
Mathilde Peyrega: École Polytechnique de Montréal

Computational Optimization and Applications, 2018, vol. 71, issue 2, No 1, 307-329

Abstract: Abstract We study derivative-free constrained optimization problems and propose a trust-region method that builds linear or quadratic models around the best feasible and around the best infeasible solutions found so far. These models are optimized within a trust region, and the progressive barrier methodology handles the constraints by progressively pushing the infeasible solutions toward the feasible domain. Computational experiments on 40 smooth constrained problems indicate that the proposed method is competitive with COBYLA, and experiments on two nonsmooth multidisciplinary optimization problems from mechanical engineering show that it can be competitive with the NOMAD software.

Keywords: Derivative-free optimization; Trust-region algorithms; Progressive barrier; 90C30; 90C56 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10589-018-0020-4

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