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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 10511060 of 1277 papers

TitleStatusHype
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Attack Graph ObfuscationCode0
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks0
First-Order Bayesian Regret Analysis of Thompson Sampling0
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy gamesCode0
Exploiting Problem Structure in Combinatorial Landscapes: A Case Study on Pure Mathematics Application0
Algorithms Inspired by Nature: A Survey0
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