论文标题
两类耐噪声的一阶算法的复杂性和性能
Complexity and performance for two classes of noise-tolerant first-order algorithms
论文作者
论文摘要
提出了在噪声存在下进行优化的两类算法，这些算法不需要评估目标函数。第一个概括了著名的Adagrad方法。然后将其复杂性分析为其参数的函数。然后得出了第二类算法，其复杂性至少与第一类一样好。关于深入学习应用引起的有限和问题问题的初始数值实验表明，第二类的方法可能胜过第一类的方法。
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.
