.png){kind=icon}
返回
An Efficient Riemannian Gradient Based Algorithm for Max-Cut Problems
.png){kind=icon}
OA
AI
DOI：10.1109/TCSII.2021.3104251.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The max-cut problem addresses the problem of finding a cut for a graph that splits the graph into two subsets of vertices so that the number of edges between these two subsets is as large as possible. However, this problem is NP-Hard, which may be solved by suboptimal algorithms. In this brief, we propose a fast and accurate Riemannian optimization algorithm for solving the max-cut problem. To do so, we develop a gradient descent algorithm and prove its convergence. Our simulation results show that the proposed method is extremely efficient on some already-investigated graphs. Specifically, our method is on average 35 times faster than the best well-known techniques with slightly losing the performance, which is on average 0.96 of the max-cut value of the others.
Keyword:
Approximation algorithms
Manifolds
Convergence
Routing
Runtime
Clustering algorithms
Circuits and systems
Max-cut problem
manifold optimization
polynomial time
NP-hard
期刊
I
IF:
4.9
论文数:
8.8K
被引数:
2.5W
机构
暂无机构信息
引用论文
Bioinspired Asymmetric Surface Property of Functionalized Mesh to Maximize the Efficiency of Fog Harvesting.png)
ChemNanoMat
ChemNanoMatIF0
PREAI
分享收藏Heterogeneous Doping via Methyl-Encapsulated Fumed Silica Enabling Weak Solvated and Self-Purified Electrolyte in Long-Term High-Voltage Lithium Batteries通过甲基封装的气相二氧化硅实现异质掺杂，在长期高压锂离子电池中启用弱溶剂化且自净化的电解质
PREAI
分享收藏Predictive risk stratification model: a progressive cluster-randomised trial in chronic conditions management (PRISMATIC) research protocolTrials
OAAI
TrialsIF0
OAAI分享收藏
查看更多内容