.png){kind=icon}
.png){kind=icon}
返回
Unsupervised Learning for Parametric Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/LCOMM.2020.3027981.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers of parameters. Such unsupervised training, which consists in repeatedly sampling parameter values and performing stochastic gradient descent, foregoes the taxing precomputation of labeled training data that supervised learning necessitates. As an example of application, we put this technique to use on a rather general constrained quadratic program. Follow-up letters subsequently apply it to more specialized wireless communication problems, some of them nonconvex in nature. In all cases, the performance of the proposed procedure is very satisfactory and, in terms of computational cost, its scalability with the problem dimensionality is superior to that of convex solvers.
Keyword:
Optimization
Artificial neural networks
Training
Linear programming
Wireless communication
Databases
Machine learning
neural networks
unsupervised learning
parametric optimization
convex optimization
quadratic program
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。