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 921930 of 1277 papers

TitleStatusHype
A Generative Graph Method to Solve the Travelling Salesman Problem0
A Generative Neural Annealer for Black-Box Combinatorial Optimization0
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover0
A Genetic Algorithm for solving Quadratic Assignment Problem(QAP)0
Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks0
A Graph Multi-separator Problem for Image Segmentation0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning0
A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem0
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem0
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features0
Show:102550
← PrevPage 93 of 128Next →

No leaderboard results yet.