Search Results for author: Taoan Huang

Found 8 papers, 2 papers with code

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information

1 code implementation18 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.

Portfolio Optimization

SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems

no code implementations22 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.

Combinatorial Optimization

The (Un)Scalability of Heuristic Approximators for NP-Hard Search Problems

1 code implementation7 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.

Combinatorial Optimization

Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

no code implementations10 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.

Multi-Agent Path Finding

Dynamic Trip-Vehicle Dispatch with Scheduled and On-Demand Requests

no code implementations20 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.

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