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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 671–680 of 1277 papers

TitleStatusHype
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit FieldsCode1
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?—0
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data—0
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning—0
Learning for Robust Combinatorial Optimization: Algorithm and Application—0
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