Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Huang, Taoan"'
Mixed Integer Linear Programming (MILP) is a fundamental tool for modeling combinatorial optimization problems. Recently, a growing body of research has used machine learning to accelerate MILP solving. Despite the increasing popularity of this appro
Externí odkaz:
http://arxiv.org/abs/2406.06954
Many real-world problems can be efficiently modeled as Mixed Integer Linear Programs (MILPs) and solved with the Branch-and-Bound method. Prior work has shown the existence of MILP backdoors, small sets of variables such that prioritizing branching o
Externí odkaz:
http://arxiv.org/abs/2401.10467
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively
Externí odkaz:
http://arxiv.org/abs/2312.16767
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images. However, many design settings arising in industrial design, material science, computer graphics and more require that the generated
Externí odkaz:
http://arxiv.org/abs/2310.02442
Autor:
Zharmagambetov, Arman, Amos, Brandon, Ferber, Aaron, Huang, Taoan, Dilkina, Bistra, Tian, Yuandong
Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$
Externí odkaz:
http://arxiv.org/abs/2307.08964
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems. Recently, it has been shown that Large Neighborhood Search (LNS), as a heuristic algorithm, can find high quality soluti
Externí odkaz:
http://arxiv.org/abs/2302.01578
Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iteratively improves it by searching a large neighborhood around the current
Externí odkaz:
http://arxiv.org/abs/2212.08183
Autor:
Ferber, Aaron, Huang, Taoan, Zha, Daochen, Schubert, Martin, Steiner, Benoit, Dilkina, Bistra, Tian, Yuandong
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. To bridge this gap, we propose $\textbf{SurCo}
Externí odkaz:
http://arxiv.org/abs/2210.12547
The A* algorithm is commonly used to solve NP-hard combinatorial optimization problems. When provided with a completely informed heuristic function, A* solves many NP-hard minimum-cost path problems in time polynomial in the branching factor and the
Externí odkaz:
http://arxiv.org/abs/2209.03393
Publikováno v:
In Artificial Intelligence September 2024 334
