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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 241–250 of 1277 papers

TitleStatusHype
Injecting Combinatorial Optimization into MCTS: Application to the Board Game boopCode0
A topological analysis of the space of recipes—0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP MethodsCode0
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention—0
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks—0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers—0
Decision-focused Graph Neural Networks for Combinatorial Optimization—0
Combinatorial Optimization with Automated Graph Neural NetworksCode2
Show:102550
← PrevPage 25 of 128Next →

No leaderboard results yet.