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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 131140 of 1277 papers

TitleStatusHype
OpenABC-D: A Large-Scale Dataset For Machine Learning Guided Integrated Circuit SynthesisCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Hybrid Pointer Networks for Traveling Salesman Problems OptimizationCode1
Learning the Markov Decision Process in the Sparse Gaussian EliminationCode1
Rationales for Sequential PredictionsCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Maximum Entropy Weighted Independent Set Pooling for Graph Neural NetworksCode1
Learning Primal Heuristics for Mixed Integer ProgramsCode1
Combinatorial Optimization with Physics-Inspired Graph Neural NetworksCode1
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