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 401410 of 1277 papers

TitleStatusHype
Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential HeuristicsCode0
Injecting Combinatorial Optimization into MCTS: Application to the Board Game boopCode0
A topological analysis of the space of recipes0
CHARME: A chain-based reinforcement learning approach for the minor embedding problem0
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP MethodsCode0
Distributed Combinatorial Optimization of Downlink User Assignment in mmWave Cell-free Massive MIMO Using Graph Neural Networks0
GFPack++: Improving 2D Irregular Packing by Learning Gradient Field with Attention0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers0
Decision-focused Graph Neural Networks for Combinatorial Optimization0
Show:102550
← PrevPage 41 of 128Next →

No leaderboard results yet.