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 431440 of 1277 papers

TitleStatusHype
Partial information decomposition: redundancy as information bottleneckCode0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint0
Test-Time Augmentation for Traveling Salesperson ProblemCode0
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing SolverCode0
Early years of Biased Random-Key Genetic Algorithms: A systematic review0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization0
QOPTLib: a Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems0
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments0
Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Tools0
Show:102550
← PrevPage 44 of 128Next →

No leaderboard results yet.