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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 11911200 of 1277 papers

TitleStatusHype
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly DetectionCode0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Ants can orienteer a thief in their robberyCode0
Co-training for Policy LearningCode0
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP MethodsCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Formulating Neural Sentence Ordering as the Asymmetric Traveling Salesman ProblemCode0
Contrastive Losses and Solution Caching for Predict-and-OptimizeCode0
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial OptimizationCode0
Partial information decomposition: redundancy as information bottleneckCode0
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