RT Journal Article
T1 The decomposition-based outer approximation algorithm for convex mixed-integer nonlinear programming.
A1 Muts, Pavlo
A1 Nowak, Ivo
A1 Hendrix, Eligius María Theodorus
K1 Programación no lineal
K1 Programación no convexa
AB 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.
PB Springer Nature
YR 2020
FD 2020
LK https://hdl.handle.net/10630/35170
UL https://hdl.handle.net/10630/35170
LA eng
NO 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
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026