Search Results for author:
Found 8 papers, 0 papers with code
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
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.
Learning Lagrangian Multipliers for the Travelling Salesman Problem
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).
GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature
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.
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
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.
Learning Pseudo-Backdoors for Mixed Integer Programs
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.
MIPaaL: Mixed Integer Program as a Layer
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.
