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 661–670 of 1277 papers

TitleStatusHype
What's Wrong with Deep Learning in Tree Search for Combinatorial OptimizationCode1
The First AI4TSP Competition: Learning to Solve Stochastic Routing ProblemsCode1
Classical Simulation of Variational Quantum Classifiers using Tensor Rings—0
Recent Advances in Deep Learning for Routing Problems—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement LearningCode1
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation—0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size—0
Show:102550
← PrevPage 67 of 128Next →

No leaderboard results yet.