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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 641650 of 1277 papers

TitleStatusHype
NeuroPrim: An Attention-based Model for Solving NP-hard Spanning Tree ProblemsCode0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning0
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
Travel the Same Path: A Novel TSP Solving StrategyCode0
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem0
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems0
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint0
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