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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

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Showing 201–210 of 1277 papers

TitleStatusHype
An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing—0
GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning—0
Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learning—0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Decision-Focused Learning to Predict Action Costs for PlanningCode0
Robust Estimation of Regression Models with Potentially Endogenous Outliers via a Modern Optimization Lens—0
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial OptimizationCode0
Cool-Fusion: Fuse Large Language Models without Training—0
Modeling Local Search Metaheuristics Using Markov Decision Processes—0
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