118 citations · 249 across the 32 of their papers we have counts for
43 papers
Predictive Dual Smoothing for Column Generation
Senne Berden, Noah Schutte, Andrea Lodi +1
Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alternates between solving the…
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
Combinatorial problems appear in numerous industrial applications. A common approach is to formulate these problems as declarative constraint models that can subsequently be compil…
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…
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…
Preference Elicitation for Step-Wise Explanations in Logic Puzzles
Marco Foschini, Marianne Defresne, Emilio Gamba +2
Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that de…