3 papers
math.OC2023
SLiSeS: Subsampled Line Search Spectral Gradient Method for Finite Sums
Stefania Bellavia, Nataša Krejić, Nataša Krklec Jerinkić +1
The spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stoc…
math.OC2020
LSOS: Line-search Second-Order Stochastic optimization methods for nonconvex finite sums
Daniela di Serafino, Nataša Krejić, Nataša Krklec Jerinkić +1
We develop a line-search second-order algorithmic framework for minimizing finite sums. We do not make any convexity assumptions, but require the terms of the sum to be continuousl…
math.NA2018
Subsampled Inexact Newton methods for minimizing large sums of convex functions
Stefania Bellavia, Natasa Krejic, Natasa Krklec Jerinkic
This paper deals with the minimization of large sum of convex functions by Inexact Newton (IN) methods employing subsampled functions, gradients and Hessian approximations. The Con…