9 papers
FunL2O: LLM-Guided Feature Function Design for Learning to Optimize
Bingheng Li, Junyang Cai, Yupeng Zhang +3
Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance. A…
ID-PaS+ : Identity-Aware Predict-and-Search for General Mixed-Integer Linear Programs
Junyang Cai, El Mehdi Er Raqabi, Pascal Van Hentenryck +1
Mixed-Integer Linear Programs (MIPs) are powerful and flexible tools for modeling a wide range of real-world combinatorial optimization problems. Predict-and-Search methods operate…
Gala: Global LLM Agents for Text-to-Model Translation
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and…
ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search
Alican Yilmaz, Junyang Cai, Serdar Kadioglu +1
Solving Mixed-Integer Programming (MIP) problems often requires substantial computational resources due to their combinatorial nature. Parallelization has emerged as a critical str…
Domain-Aware Machine Learning for Accelerating MILP-Based Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Brendan Long +4
Motion-planning problems with temporal-logic or chance constraints are often encoded as mixed-integer linear programs (MILPs). Although these encodings provide rigorous specificati…
Multi-task Representation Learning for Mixed Integer Linear Programming
Junyang Cai, Taoan Huang, Bistra Dilkina
Mixed Integer Linear Programs (MILPs) are highly flexible and powerful tools for modeling and solving complex real-world combinatorial optimization problems. Recently, machine lear…
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
Mixed-integer programming (MIP) is a powerful paradigm for modeling and solving various important combinatorial optimization problems. Recently, learning-based approaches have show…
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
Jiatai Tong, Junyang Cai, Thiago Serra
Besides training, mathematical optimization is also used in deep learning to model and solve formulations over trained neural networks for purposes such as verification, compressio…
Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning
Junyang Cai, Taoan Huang, Bistra Dilkina
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method. Prior work has shown the existence of MILP…