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Found 7 papers, 1 papers with code
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
To bridge this gap, we propose SurCo that learns linear Surrogate costs which can be used by existing Combinatorial solvers to output good solutions to the original nonlinear combinatorial optimization problem, combining the flexibility of gradient-based methods with the structure of linear combinatorial optimization.
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.
