On decomposition and multiobjective-based column and disjunctive cut generation for MINLP. - Muts, Pavlo; Nowak, Ivo; Hendrix, Eligius María Theodorus (Springer Nature, 2020)
Most industrial optimization problems are sparse and can be formulated as block- separable mixed-integer nonlinear programming (MINLP) problems, defined by linking low-dimensional sub-problems by (linear) coupling constraints. This paper investigates the potential of using decomposition and a novel multiobjective-based column and cut generation approach for solving nonconvex block-separable MIN- LPs, based on the so-called resource-constrained reformulation. Based on this approach, two decomposition-based inner- and outer-refinement algorithms are presented and preliminary numerical results with nonconvex MINLP instances are reported.
Keywords: Programación no lineal; Decomposition; Parallel computing; Column generation; Nonconvex optimization; Global optimization; Mixed-integer nonlinear programming