论文标题
不精确的张量方法及其应用于随机凸优化
Inexact Tensor Methods and Their Application to Stochastic Convex Optimization
论文作者
论文摘要
我们提出了一般的非加速和加速张量方法，这些方法在有关目标的衍生物的不精确信息下，分析其收敛速率。此外，我们为每种衍生物中的不精确度提供条件，而每种算法就足以达到所需的准确性。作为推论，我们提出了随机张量方法，以进行凸优化，并为每个衍生物获得足够的迷你批量尺寸。
We propose general non-accelerated and accelerated tensor methods under inexact information on the derivatives of the objective, analyze their convergence rate. Further, we provide conditions for the inexactness in each derivative that is sufficient for each algorithm to achieve the desired accuracy. As a corollary, we propose stochastic tensor methods for convex optimization and obtain sufficient mini-batch sizes for each derivative.
