1 code implementation • 18 Jul 2023 • Arman Zharmagambetov, Brandon Amos, Aaron Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian
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.
no code implementations • 3 Feb 2023 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
no code implementations • 15 Dec 2022 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
LNS relies on heuristics to select neighborhoods to search in.
no code implementations • 22 Oct 2022 • Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian
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.
1 code implementation • 7 Sep 2022 • Sumedh Pendurkar, Taoan Huang, Sven Koenig, Guni Sharon
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.
no code implementations • 10 Dec 2020 • Taoan Huang, Bistra Dilkina, Sven Koenig
In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work.
no code implementations • NeurIPS Workshop LMCA 2020 • Taoan Huang, Bistra Dilkina, Sven Koenig
Multi-Agent Path Finding is an NP-hard problem that is difficult for current approaches to solve optimally.
no code implementations • 20 Jul 2019 • Taoan Huang, Bohui Fang, Xiaohui Bei, Fei Fang
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.