Accelerated Gradient Descent Driven by Lévy Perturbations

Author:

Chen Yuquan1ORCID,Wu Zhenlong2,Lu Yixiang3,Chen Yangquan4ORCID,Wang Yong5

Affiliation:

1. Department of Automation, Hohai University, Nanjing 210024, China

2. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China

3. Anhui Engineering Laboratory of Human Robot Integration System and Equipment, School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China

4. School of Engineering, University of California, Merced, CA 95343, USA

5. Department of Automation, University of Science and Technology of China, Hefei 230026, China

Abstract

In this paper, we mainly consider two kinds of perturbed accelerated gradient descents driven by Lévy perturbations, which is of great importance for enhancing the global search ability. By using Lévy representation, Lévy perturbations can be divided into two parts: small jumps and large jumps, whose properties are then carefully discussed. By introducing the concept of attraction domain for local minima, Makovian transition properties are proven for the proposed two perturbed accelerated gradient descents with different infinitesimal matrices. Finally, all the results are extended to the vector case and two simulation examples are provided to validate all the conclusions.

Funder

Anhui Engineering Laboratory of Human Robot Integration System and Equipment

National Nature Science Foundation of China

“SCBS” plan of Jiangsu Province

Publisher

MDPI AG

Reference33 articles.

1. Indirect model reference adaptive control for a class of fractional order systems;Chen;Commun. Nonlinear Sci. Numer. Simul.,2016

2. Lewis, F.L., Vrabie, D., and Syrmos, V.L. (2012). Optimal Control, John Wiley & Sons.

3. Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann.

4. Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.

5. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv.

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