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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 841850 of 1277 papers

TitleStatusHype
Modular Multi Target Tracking Using LSTM Networks0
A Knowledge Representation Approach to Automated Mathematical Modelling0
Evaluating Curriculum Learning Strategies in Neural Combinatorial Optimization0
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications0
Contrastive Losses and Solution Caching for Predict-and-OptimizeCode0
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach0
A Spectral Method for Unsupervised Multi-Document Summarization0
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