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oai:riuma.uma.es:10630/351702026-02-03T11:13:50Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Muts, Pavlo author Nowak, Ivo author Hendrix, Eligius María Theodorus author 2020 This paper presents a new two-phase method for solving convex mixed-integer nonlinear programming (MINLP) problems, called Decomposition-based Outer Approximation Algo- rithm (DECOA). In the first phase, a sequence of linear integer relaxed sub-problems (LP phase) is solved in order to rapidly generate a good linear relaxation of the original MINLP problem. In the second phase, the algorithm solves a sequence of mixed integer linear pro- gramming sub-problems (MIP phase). In both phases the outer approximation is improved iteratively by adding new supporting hyperplanes by solving many easier sub-problems in parallel. DECOA is implemented as a part of Decogo (Decomposition-based Global Opti- mizer), a parallel decomposition-based MINLP solver implemented in Python and Pyomo. Preliminary numerical results based on 70 convex MINLP instances up to 2700 variables show that due to the generated cuts in the LP phase, on average only 2–3 MIP problems have to be solved in the MIP phase. Muts, P., Nowak, I. and Hendrix, E.M.T. (2020), The Decomposition-based Outer Approximation Algorithm for convex mixed-integer nonlinear programming, Journal of Global Optimization, 77, 1, 75-96 https://hdl.handle.net/10630/35170 10.1007/s10898-020-00888-x Programación no lineal Programación no convexa The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming.