Limited-memory trust-region methods for sparse relaxation-Reference-Cited by-同舟云学术

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Limited-memory trust-region methods for sparse relaxation

Published:2017-08-24 Issue: Volume: Page:
ISSN:
Container-title:Wavelets and Sparsity XVII
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Author:

Adhikari Lasith,Erway Jennifer B.,Lockhart Shelby,Marcia Roummel F.,DeGuchy Omar

Publisher

SPIE

Reference19 articles.

1. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

2. Compressed Sensing

3. A quasi-Newton approach to nonsmooth convex optimization problems in machine learning;Yu;The Journal of Machine Learning Research,2010

4. Proximal Newton-type methods for convex optimization;Lee;Advances in Neural Information Processing Systems,2012

5. Low rank matrix completion: A smoothed l0-search;Zhou,2012

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods;Algorithms;2023-10-20

2. A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks;Lecture Notes in Networks and Systems;2022

3. Quasi-Newton Optimization Methods for Deep Learning Applications;Advances in Intelligent Systems and Computing;2020

4. Improving L-BFGS Initialization for Trust-Region Methods in Deep Learning;2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA);2018-12

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