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In this paper, we propose a quantized learning equation with a monotone increasing resolution of quantization and stochastic analysis for the proposed algorithm. According to the white noise hypothesis for the quantization error with dense and uniform distribution, we can regard the quantization error as i.i.d. white noise. Based on this, we show that the learning equation with monotonically increasing quantization resolution converges weakly as the distribution viewpoint. The analysis of this paper shows that global optimization is possible for a domain that satisfies the Lipschitz condition instead of local convergence properties such as the Hessian constraint of the objective function.
Author Information
Jinwuk Seok (Electronics and Telecommunications Research Institute)
Jinwuk Seok received his BS and MS degrees in Electrical Control Engineering from Hong-Ik University, Seoul, Republic of Korea, in 1993 and 1995, respectively. Additionally, he received his Ph.D. degree in Electrical Engineering from Hong-Ik University, Seoul, Republic of Korea, in 1998. He has been a principal member of engineering staff at Electronics and Telecommunications Research Institute in Korea since 2000, and an adjunct professor of the Computer Software Engineering Department at the University of Science and Technology in Korea since 2009. His research interests include artificial intelligence, machine learning, and stochastic nonlinear control.
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2022 : Quantization based Optimization : Alternative Stochastic Approximation of Global Optimization »
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2022 : Poster Session 2 »
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