Learning for Spatial Branching: An Algorithm Selection Approach

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. To bridge this gap, we develop a learning framework for spatial branching and show its efficacy in the context of … Read more

Computational advances in polynomial optimization: RAPOSa, a freely available global solver

In this paper we introduce RAPOSa, a global optimization solver specifically designed for (continuous) polynomial programming problems with box-constrained variables. Written entirely in C++, RAPOSa is based on the Reformulation-Linearization Technique developed by Sherali and Tuncbilek (1992) and subsequently improved in Sherali et al. (2012a), Sherali et al. (2012b) and Dalkiran and Sherali (2013). We … Read more