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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 701710 of 1277 papers

TitleStatusHype
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence0
One model Packs Thousands of Items with Recurrent Conditional Query LearningCode1
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle0
Large Scale Diverse Combinatorial Optimization: ESPN Fantasy Football Player Trades0
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Symbolic Regression via Neural-Guided Genetic Programming Population SeedingCode1
Sample Selection for Fair and Robust Training0
Interpretable Decision Trees Through MaxSAT0
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