no code implementations • 11 Aug 2022 • Brais González-Rodríguez, Raúl Alvite-Pazó, Samuel Alvite-Pazó, Bissan Ghaddar, Julio González-Díaz
In this research, we investigate the strengthening of RLT relaxations of polynomial optimization problems through the addition of nine different types of constraints that are based on linear, second-order cone, and semidefinite programming to solve to optimality the instances of well established test sets of polynomial optimization problems.
no code implementations • 22 Apr 2022 • Bissan Ghaddar, Ignacio Gómez-Casares, Julio González-Díaz, Brais González-Rodríguez, Beatriz Pateiro-López, Sofía Rodríguez-Ballesteros
The use of machine learning techniques to improve the performance of branch-and-bound optimization algorithms is a very active area in the context of mixed integer linear problems, but little has been done for non-linear optimization.
