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 601610 of 1277 papers

TitleStatusHype
Combinatorial optimization for low bit-width neural networks0
Neural Improvement Heuristics for Graph Combinatorial Optimization ProblemsCode0
On the Generalization of Neural Combinatorial Optimization Heuristics0
MIP-GNN: A Data-Driven Framework for Guiding Combinatorial SolversCode1
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization0
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial OptimizationCode1
DevFormer: A Symmetric Transformer for Context-Aware Device PlacementCode2
DOGE-Train: Discrete Optimization on GPU with End-to-end TrainingCode1
Terrain Analysis in StarCraft 1 and 2 as Combinatorial OptimizationCode0
Decomposition Strategies and Multi-shot ASP Solving for Job-shop SchedulingCode1
Show:102550
← PrevPage 61 of 128Next →

No leaderboard results yet.