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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 431440 of 1277 papers

TitleStatusHype
A Formal Perspective on Byte-Pair EncodingCode0
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization BenchmarkCode4
Automatic Truss Design with Reinforcement LearningCode1
Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and ApplicationsCode0
Object Detection based on the Collection of Geometric Evidence0
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph MatchingCode1
TreeDQN: Learning to minimize Branch-and-Bound treeCode0
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss0
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelCode0
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