SOTAVerified

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 321330 of 1277 papers

TitleStatusHype
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
Evaluate Quantum Combinatorial Optimization for Distribution Network ReconfigurationCode0
Exact-K Recommendation via Maximal Clique OptimizationCode0
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search SpacesCode0
Deep Learning Chromatic and Clique Numbers of GraphsCode0
Estimating the stability number of a random graph using convolutional neural networksCode0
Large Neighborhood Prioritized Search for Combinatorial Optimization with Answer Set ProgrammingCode0
Latent Guided Sampling for Combinatorial OptimizationCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Show:102550
← PrevPage 33 of 128Next →

No leaderboard results yet.