2 citations · 3 across the 4 of their papers we have counts for
4 papers
Tightening convex relaxations of trained neural networks: a unified approach for convex and S-shaped activations
Pablo Carrasco, Gonzalo Muñoz
The non-convex nature of trained neural networks has created significant obstacles in their incorporation into optimization models. In this context, Anderson et al. (2020) provided…
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
Fabian Badilla, Marcos Goycoolea, Gonzalo Muñoz +1
The use of Mixed-Integer Linear Programming (MILP) models to represent neural networks with Rectified Linear Unit (ReLU) activations has become increasingly widespread in the last…
Decision-focused predictions via pessimistic bilevel optimization: complexity and algorithms
Víctor Bucarey, Sophia Calderón, Gonzalo Muñoz +1
Dealing with uncertainty in optimization parameters is an important and longstanding challenge. Typically, uncertain parameters are predicted accurately, and then a deterministic o…
The MIP Workshop 2023 Computational Competition on Reoptimization
Suresh Bolusani, Mathieu Besançon, Ambros Gleixner +5
This paper describes the computational challenge developed for a computational competition held in 2023 for the anniversary of the Mixed Integer Programming Work…