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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

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Showing 751–760 of 1277 papers

TitleStatusHype
Maximizing Influence with Graph Neural Networks—0
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization—0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman ProblemCode0
UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning—0
COPS: Controlled Pruning Before Training Starts—0
Utilizing synchronization to partition power networks into microgrids—0
Power of human-algorithm collaboration in solving combinatorial optimization problems—0
Learning a Large Neighborhood Search Algorithm for Mixed Integer ProgramsCode1
Faster Matchings via Learned Duals—0
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