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A Stochastic Variance Reduced Gradient using Barzilai-Borwein Techniques as Second Order Information
Autor:
Tankaria, Hardik, Yamashita, Nobuo
In this paper, we consider to improve the stochastic variance reduce gradient (SVRG) method via incorporating the curvature information of the objective function. We propose to reduce the variance of stochastic gradients using the computationally eff
Externí odkaz:
http://arxiv.org/abs/2208.11075
Second-order optimization methods are among the most widely used optimization approaches for convex optimization problems, and have recently been used to optimize non-convex optimization problems such as deep learning models. The widely used second-o
Externí odkaz:
http://arxiv.org/abs/2110.08577
The limited memory BFGS (L-BFGS) method is one of the popular methods for solving large-scale unconstrained optimization. Since the standard L-BFGS method uses a line search to guarantee its global convergence, it sometimes requires a large number of
Externí odkaz:
http://arxiv.org/abs/2101.04413
Akademický článek
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Autor:
Tankaria, Hardik1 (AUTHOR) hardik7@amp.i.kyoto-u.ac.jp, Sugimoto, Shinji2 (AUTHOR), Yamashita, Nobuo1 (AUTHOR)
Publikováno v:
Computational Optimization & Applications. May2022, Vol. 82 Issue 1, p61-88. 28p.
