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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1231–1240 of 1277 papers

TitleStatusHype
Generalization Bounds of Surrogate Policies for Combinatorial Optimization Problems—0
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness—0
Robust Bayesian Inference for Moving Horizon Estimation—0
Generalizing and Unifying Gray-box Combinatorial Optimization Operators—0
Generative Adversarial Training for Neural Combinatorial Optimization Models—0
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems—0
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning—0
Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver—0
Generic CP-Supported CMSA for Binary Integer Linear Programs—0
Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka—0
Show:102550
← PrevPage 124 of 128Next →

No leaderboard results yet.