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 381390 of 1277 papers

TitleStatusHype
On permutation symmetries in Bayesian neural network posteriors: a variational perspective0
Enhancing Column Generation by Reinforcement Learning-Based Hyper-Heuristic for Vehicle Routing and Scheduling Problems0
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale GeneralizationCode1
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
GRASP: Accelerating Shortest Path Attacks via Graph Attention0
Diversity from Human Feedback0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems0
Oracle Efficient Algorithms for Groupwise Regret0
Routing Arena: A Benchmark Suite for Neural Routing Solvers0
A Deep Instance Generative Framework for MILP Solvers Under Limited Data AvailabilityCode1
Show:102550
← PrevPage 39 of 128Next →

No leaderboard results yet.