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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 741–750 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
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Revisiting Transformation Invariant Geometric Deep Learning: Are Initial Representations All You Need?—0
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
Pretrained Cost Model for Distributed Constraint Optimization ProblemsCode0
Constrained Resource Allocation Problems in Communications: An Information-assisted Approach—0
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