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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 141150 of 1277 papers

TitleStatusHype
A Reinforcement Learning Environment For Job-Shop SchedulingCode1
Contingency-Aware Influence Maximization: A Reinforcement Learning ApproachCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
A Learning-based Iterative Method for Solving Vehicle Routing ProblemsCode1
ASP: Learn a Universal Neural Solver!Code1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable ApproachCode1
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