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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 5160 of 1277 papers

TitleStatusHype
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing SystemCode1
DeepACO: Neural-enhanced Ant Systems for Combinatorial OptimizationCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
Are Graph Neural Networks Optimal Approximation Algorithms?Code1
A Bayesian algorithm for retrosynthesisCode1
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDNCode1
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on GraphsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
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