most citedComputational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks

2 citations · 3 across the 4 of their papers we have counts for

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math.OC2024

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…

math.OC2024★ 2 cited

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…

math.OC2023

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…