A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions
[article]
2021
arXiv
pre-print
Machine learning algorithms typically perform optimization over a class of non-convex functions. In this work, we provide bounds on the fundamental hardness of identifying the global minimizer of a non convex function. Specifically, we design a family of parametrized non-convex functions and employ statistical lower bounds for parameter estimation. We show that the parameter estimation problem is equivalent to the problem of function identification in the given family. We then claim that non
arXiv:2002.12911v2
fatcat:3k5orrzcqzbtxjuj33cgo4bmsy