no code implementations • 28 Feb 2024 • Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
no code implementations • 22 Dec 2023 • Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron Ferber, Louis-Martin Rousseau
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).
no code implementations • 3 Oct 2023 • Aaron Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian
Generating diverse objects (e. g., images) using generative models (such as GAN or VAE) has achieved impressive results in the recent years, to help solve many design problems that are traditionally done by humans.
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.
no code implementations • 9 Jun 2021 • Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
no code implementations • 12 Jul 2019 • Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.