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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 12011210 of 1277 papers

TitleStatusHype
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
Bayesian Optimization of Functions over Node Subsets in GraphsCode0
Solving Graph-based Public Good Games with Tree Search and Imitation LearningCode0
Controlling Continuous Relaxation for Combinatorial OptimizationCode0
FIS-ONE: Floor Identification System with One Label for Crowdsourced RF SignalsCode0
Solving Graph-based Public Goods Games with Tree Search and Imitation LearningCode0
Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum AnnealingCode0
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial OptimizationCode0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
Differentiable TAN Structure Learning for Bayesian Network ClassifiersCode0
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