5 papers
Learning to Choose Branching Rules for Nonconvex MINLPs
Timo Berthold, Fritz Geis
Outer-approximation-based branch-and-bound is a common algorithmic framework for solving MINLPs (mixed-integer nonlinear programs) to global optimality, with branching variable sel…
Global Optimization for Combinatorial Geometry Problems Revisited in the Era of LLMs
Timo Berthold, Dominik Kamp, Gioni Mexi +2
Recent progress in LLM-driven algorithm discovery, exemplified by DeepMind's AlphaEvolve, has produced new best-known solutions for a range of hard geometric and combinatorial prob…
Cut-based Conflict Analysis in Mixed Integer Programming
Gioni Mexi, Felipe Serrano, Timo Berthold +2
For almost two decades, mixed integer programming (MIP) solvers have used graph-based conflict analysis to learn from local infeasibilities during branch-and-bound search. In this…
Tricks from the Trade for Large-Scale Markdown Pricing: Heuristic Cut Generation for Lagrangian Decomposition
Robert Streeck, Torsten Gellert, Andreas Schmitt +4
In automated decision making processes in the online fashion industry, the 'predict-then-optimize' paradigm is frequently applied, particularly for markdown pricing strategies. Thi…
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…