Summary of Convergence Rates Of Stochastic Gradient Method with Independent Sequences Of Step-size and Momentum Weight, by Wen-liang Hwang
Convergence rates of stochastic gradient method with independent sequences of step-size and momentum weight
by Wen-Liang Hwang
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper analyzes the convergence rate of two step-size learning rates, “diminishing-to-zero” and “constant-and-drop,” when used in large-scale machine learning algorithms with strongly convex functions. The authors show that the convergence rate for the first method can be expressed as a product of exponential terms in step-size and polynomial terms in momentum weight. The analysis justifies using default momentum weights and diminishing-to-zero step-size sequences in large-scale software, which is widely used in machine learning applications. The paper also presents conditions for the momentum weight sequence to converge at each stage in the “constant-and-drop” method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make big computer learning systems work faster and better. It looks at two ways to adjust how much a system learns from new information, called step-size learning rates. The study shows that one way works well when using a certain type of function in the learning process. This is important because many large-scale machine learning software use this method. The research also provides rules for another way to adjust learning rates to work properly. |
Keywords
» Artificial intelligence » Machine learning
