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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 10811090 of 1277 papers

TitleStatusHype
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Automatic Rank Selection for High-Speed Convolutional Neural Network0
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization0
Deep Learning of Graph Matching0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis0
Generic CP-Supported CMSA for Binary Integer Linear Programs0
A novel channel pruning method for deep neural network compression0
Safe Element Screening for Submodular Function Minimization0
Solving the Rubik's Cube Without Human KnowledgeCode0
Evolutionary RL for Container Loading0
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