Stochastic Gradient Langevin Dynamics with Variance Reduction

• URL: http://arxiv.org/abs/2102.06759v1
• Date: Fri, 12 Feb 2021 20:22:56 GMT
• Title: Stochastic Gradient Langevin Dynamics with Variance Reduction
• Authors: Zhishen Huang, Stephen Becker
• Abstract summary: gradient Langevin dynamics (SGLD) has gained the attention of global optimization researchers. This paper proves an improved non objective functions using accelerated property reductions.
• Score: 6.243995448840211
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Stochastic gradient Langevin dynamics (SGLD) has gained the attention of optimization researchers due to its global optimization properties. This paper proves an improved convergence property to local minimizers of nonconvex objective functions using SGLD accelerated by variance reductions. Moreover, we prove an ergodicity property of the SGLD scheme, which gives insights on its potential to find global minimizers of nonconvex objectives.

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