Search Results for author:
Found 11 papers, 3 papers with code
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods
We curate MILP distributions from existing work in this area as well as real-world problems that have not been used, and classify them into different hardness levels.
Learning Backdoors for Mixed Integer Linear Programs with Contrastive Learning
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method.
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i. e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning.
GenCO: Generating Diverse Designs with Combinatorial Constraints
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images.
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
Local Branching Relaxation Heuristics for Integer Linear Programs
LNS relies on heuristics to select neighborhoods to search in.
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems
Our first experimental results for three representative NP-hard minimum-cost path problems suggest that using neural networks to approximate completely informed heuristic functions with high precision might result in network sizes that scale exponentially in the instance sizes.
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search
In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work.
Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding
Multi-Agent Path Finding is an NP-hard problem that is difficult for current approaches to solve optimally.
Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests
Transportation service providers that dispatch drivers and vehicles to riders start to support both on-demand ride requests posted in real time and rides scheduled in advance, leading to new challenges which, to the best of our knowledge, have not been addressed by existing works.
