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 8190 of 1277 papers

TitleStatusHype
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation ResearchCode1
Neural Airport Ground HandlingCode1
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural NetworksCode1
ASP: Learn a Universal Neural Solver!Code1
Learning Large Neighborhood Search for Vehicle Routing in Airport Ground HandlingCode1
Quantum HyperNetworks: Training Binary Neural Networks in Quantum SuperpositionCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Reinforced Genetic Algorithm for Structure-based Drug DesignCode1
Show:102550
← PrevPage 9 of 128Next →

No leaderboard results yet.