Optimization lies at the heart of machine learning and signal processing. Contemporary approaches based on the stochastic gradient method are non-adaptive in the sense that their implementation employs prescribed parameter values that need to be tuned for each application. This article summarizes recent research and motivates future work on adaptive stochastic optimization methods, which have the potential to offer significant computational savings when training large-scale systems.

中文翻译：

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自适应随机优化

优化是机器学习和信号处理的核心。基于随机梯度方法的当代方法是非自适应的，因为它们的实现采用了需要为每个应用程序调整的规定参数值。本文总结了最近的研究并激发了未来关于自适应随机优化方法的工作，这些方法有可能在训练大规模系统时提供显着的计算节省。