An incremental quasi-Newton method with a local superlinear convergence rate

Aryan Mokhtari; Mark Eisen; Alejandro Ribeiro

ICASSP 2017accepted0 citations

An incremental quasi-Newton method with a local superlinear convergence rate

Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro

Abstract

We present an incremental Broyden-Fletcher-Goldfarb-Shanno (BFGS) method as a quasi-Newton algorithm with a cyclically iterative update scheme for solving large-scale optimization problems. The proposed incremental quasi-Newton (IQN) algorithm reduces computational cost relative to traditional quasi-Newton methods by restricting the update to a single function per iteration and relative to incremental second-order methods by removing the need to compute the inverse of the Hessian. A local superlinear convergence rate is established and a strong improvement is shown over first order methods numerically for a set of common large-scale optimization problems.

BibTeX

@inproceedings{icassp2017_anincrementalqua,
  title = {An incremental quasi-Newton method with a local superlinear convergence rate},
  author = {Aryan Mokhtari and Mark Eisen and Alejandro Ribeiro},
  booktitle = {ICASSP 2017},
  year = {2017}
}