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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 541550 of 1277 papers

TitleStatusHype
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies ReconstructionCode0
Application of Decision Tree Classifier in Detection of Specific Denial of Service Attacks with Genetic Algorithm Based Feature Selection on NSL-KDD0
Towards Practical Explainability with Cluster Descriptors0
Navigating Memory Construction by Global Pseudo-Task Simulation for Continual LearningCode0
Theory and Approximate Solvers for Branched Optimal Transport with Multiple SourcesCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Travel the Same Path: A Novel TSP Solving StrategyCode0
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem0
DIMES: A Differentiable Meta Solver for Combinatorial Optimization ProblemsCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
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