A Fixed-Point of View on Gradient Methods for Big Data

Interpreting gradient methods as fixed-point iterations, we provide a detailed analysis of those methods for minimizing convex objective functions. Due to their conceptual and algorithmic simplicity, gradient methods are widely used in machine learning for massive data sets (big data). In particular...

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Bibliographic Details
Published in:	Frontiers in Applied Mathematics and Statistics
Main Author:	Alexander Jung
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2017-09-01
Subjects:	convex optimization fixed point theory big data machine learning contraction mapping gradient descent
Online Access:	http://journal.frontiersin.org/article/10.3389/fams.2017.00018/full

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