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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 331340 of 1277 papers

TitleStatusHype
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
Fair Correlation ClusteringCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Estimating the stability number of a random graph using convolutional neural networksCode0
Controlling Continuous Relaxation for Combinatorial OptimizationCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise ConstraintsCode0
EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization FormulationsCode0
Enriching Documents with Compact, Representative, Relevant Knowledge GraphsCode0
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