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 121130 of 1277 papers

TitleStatusHype
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization SolversCode1
MMSR: Symbolic Regression is a Multi-Modal Information Fusion TaskCode1
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial OptimizationCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
ASP: Learn a Universal Neural Solver!Code1
Equivariant quantum circuits for learning on weighted graphsCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
Show:102550
← PrevPage 13 of 128Next →

No leaderboard results yet.