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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 111120 of 1277 papers

TitleStatusHype
BILP-Q: Quantum Coalition Structure GenerationCode1
MAP-Elites based Hyper-Heuristic for the Resource Constrained Project Scheduling ProblemCode1
Learning to Solve Travelling Salesman Problem with Hardness-adaptive CurriculumCode1
Pareto Set Learning for Neural Multi-objective Combinatorial OptimizationCode1
S-Rocket: Selective Random Convolution Kernels for Time Series ClassificationCode1
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and InsightsCode1
Instance-wise algorithm configuration with graph neural networksCode1
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
The First AI4TSP Competition: Learning to Solve Stochastic Routing ProblemsCode1
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