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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 91100 of 1277 papers

TitleStatusHype
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial OptimizationCode1
Optimal Discrete Beamforming of RIS-Aided Wireless Communications: An Inner Product Maximization ApproachCode1
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement LearningCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Theory and Approximate Solvers for Branched Optimal Transport with Multiple SourcesCode1
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-CutCode1
Learning with Combinatorial Optimization Layers: a Probabilistic ApproachCode1
Unsupervised Learning for Combinatorial Optimization with Principled Objective RelaxationCode1
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