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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 11311140 of 1277 papers

TitleStatusHype
A Formal Perspective on Byte-Pair EncodingCode0
Diversity-Driven View Subset Selection for Indoor Novel View SynthesisCode0
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean OptimizationCode0
Regret in Online Combinatorial OptimizationCode0
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial OptimizationCode0
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint SatisfactionCode0
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial ProblemsCode0
Ecole: A Library for Learning Inside MILP SolversCode0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Regularized Langevin Dynamics for Combinatorial OptimizationCode0
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