no code implementations • 23 Feb 2017 • Julianne Chung, Matthias Chung, J. Tanner Slagel, Luis Tenorio
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e. g., the size may exceed computer memory or data are collected in real-time).
