3 papers
math.OC2026
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…
cs.LG2025
Decision-focused predictions via pessimistic bilevel optimization: complexity and algorithms
VÃctor 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…
math.OC2024
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…