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 12711277 of 1277 papers

TitleStatusHype
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability0
Higher-Order Quantum-Inspired Genetic Algorithms0
High-Level Plan for Behavioral Robot Navigation with Natural Language Directions and R-NET0
Highly parallel algorithm for the Ising ground state searching problem0
High-quality Thermal Gibbs Sampling with Quantum Annealing Hardware0
How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem0
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems0
Show:102550
← PrevPage 128 of 128Next →

No leaderboard results yet.