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 701–710 of 1277 papers

TitleStatusHype
Reinforcement Learning for Combinatorial Optimization: A Survey—0
Reinforcement Learning for Multi-Truck Vehicle Routing Problems—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Learning with Chromatic Networks for Compact Architecture Search—0
Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization—0
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization—0
ReLU Neural Networks of Polynomial Size for Exact Maximum Flow Computation—0
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference—0
Show:102550
← PrevPage 71 of 128Next →

No leaderboard results yet.