论文标题
具有复杂性的严格鞍座功能的线搜索下降算法
A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees
论文作者
论文摘要
我们描述了一种线路搜索算法，该算法获得了特定“严格的鞍座”属性问题的最著名的最坏情况复杂性结果，该属性已被观察到在低级别矩阵优化问题中所持的。从某种意义上说，我们的算法是自适应的，它利用了回溯线搜索，并且不需要对定义严格的鞍属性的参数的先验知识。
We describe a line-search algorithm which achieves the best-known worst-case complexity results for problems with a certain "strict saddle" property that has been observed to hold in low-rank matrix optimization problems. Our algorithm is adaptive, in the sense that it makes use of backtracking line searches and does not require prior knowledge of the parameters that define the strict saddle property.
