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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 3140 of 1277 papers

TitleStatusHype
CLIPPER: A Graph-Theoretic Framework for Robust Data AssociationCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Automatic Truss Design with Reinforcement LearningCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Attention, Learn to Solve Routing Problems!Code1
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