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 10911100 of 1277 papers

TitleStatusHype
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Walkability Optimization: Formulations, Algorithms, and a Case Study of TorontoCode0
Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional NetworksCode0
An ant colony optimization algorithm for job shop scheduling problemCode0
Neural Set Function Extensions: Learning with Discrete Functions in High DimensionsCode0
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet ControlCode0
Neural Solver Selection for Combinatorial OptimizationCode0
Learning Interpretable Error Functions for Combinatorial Optimization Problem ModelingCode0
Automatic and effective discovery of quantum kernelsCode0
Learning Combinatorial Optimization Algorithms over GraphsCode0
Show:102550
← PrevPage 110 of 128Next →

No leaderboard results yet.