Search Results for author:
Found 8 papers, 2 papers with code
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information
The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing.
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.
