From the 1 of 16 linked papers with an AI index.
16 papers
Translating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy
Tias Guns, Ignace Bleukx, Hendrik Bierlee +8
Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems. The user specifies their problem through constraints and decision var…
LLM-Guided Evolutionary Search for Constraint Model Reformulation to Improve Solver Efficiency
Kostis Michailidis, Dimos Tsouros, Nguyen Dang +1
The paper explores using large language models within an evolutionary search framework to automatically reformulate constraint models for faster solving, introducing a diversity‑pr…
Scaling Decision-Focused Learning to Large Problems with Lagrangian Decomposition
Stéphane Eilles-Chan Way, Hugo Percot, Quentin Cappart +2
Decision-focused learning has shown great promise for addressing predict-then-optimize problems, particularly in the presence of under-specified models. However, its practical depl…
Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning
Mattia Silvestri, Senne Berden, Jayanta Mandi +4
Many real-world optimization problems contain parameters that are unknown before deployment time, either due to stochasticity or to lack of information (e.g., demand or travel time…
Scalable Decision-Focused Learning through Cost-Sensitive Regression
Noah Schutte, Senne Berden, Tias Guns +2
Many real-world combinatorial problems involve uncertain parameters, which can be predicted given contextual features and historical data. These `predict-then-optimize' or `context…
DCP-Bench-Open: Evaluating LLMs for Constraint Modelling of Discrete Combinatorial Problems
Kostis Michailidis, Dimos Tsouros, Tias Guns
Discrete Combinatorial Problems (DCPs) are prevalent in industrial decision-making and optimisation. However, while constraint solving technologies for DCPs have advanced significa…