A penalty approach for solving nonsmooth and nonconvex MINLP problems

By Costa, M.F.P.; Rocha, A.M.A.C.; Fernandes, E.M.G.P.

Springer Proceedings in Mathematics and Statistics

2018

Abstract

This paper presents a penalty approach for globally solving nonsmooth and nonconvex mixed-integer nonlinear programming (MINLP) problems. Both integrality constraints and general nonlinear constraints are handled separately by hyperbolic tangent penalty functions. Proximity from an iterate to a feasible promising solution is enforced by an oracle penalty term. The numerical experiments show that the proposed oracle-based penalty approach is effective in reaching the solutions of the MINLP problems and is competitive when compared with other strategies.

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