  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.
