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 591–600 of 1277 papers

TitleStatusHype
Learning to Control Local Search for Combinatorial OptimizationCode1
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setCode1
Quant-BnB: A Scalable Branch-and-Bound Method for Optimal Decision Trees with Continuous Features—0
The Influence of Local Search over Genetic Algorithms with Balanced Representations—0
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms—0
Diffusion models as plug-and-play priorsCode2
Concentration of Data Encoding in Parameterized Quantum Circuits—0
Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch—0
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems—0
Set Interdependence Transformer: Set-to-Sequence Neural Networks for Permutation Learning and Structure Prediction—0
Show:102550
← PrevPage 60 of 128Next →

No leaderboard results yet.