两类噪声容忍一阶算法的复杂度与性能
Complexity and performance for two classes of noise-tolerant first-order algorithms
AI总结：
本文提出两类无需目标函数评估的噪声容忍一阶优化算法，分析了第一类（推广Adagrad）的复杂度并推导出性能更优的第二类算法，初步实验表明第二类在深度学习有限和问题上表现更好。
摘要：
本文提出了两类在有噪声环境下的优化算法，这些算法不需要评估目标函数。第一类推广了著名的Adagrad方法，随后将其复杂度作为参数的函数进行分析。接着推导出第二类算法，其复杂度至少与第一类一样好。在深度学习应用中产生的有限和问题上的初步数值实验表明，第二类方法可能优于第一类。
英文摘要：
Two classes of algorithms for optimization in the presence of noise are presented, that do not require the evaluation of the objective function. The first generalizes the well-known Adagrad method. Its complexity is then analyzed as a function of its parameters. A second class of algorithms is then derived whose complexity is at least as good as that of the first class. Initial numerical experiments on finite-sum problems arising from deep-learning applications suggest that methods of the second class may outperform those of the first.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
