Over the last two decades, it has been observed that using the gradient vector as a search
direction in large-scale optimization may lead to efficient algorithms. The effectiveness relies …

We propose a new gradient method for quadratic programming, named SDC, which
alternates some steepest descent (SD) iterates with some gradient iterates that use a …

We conduct a comprehensive investigation into the dynamics of gradient descent using
large-order constant step-sizes in the context of quadratic regression models. Within this …

The primary aim of this paper is to provide a review of the last few decades of research
focused on delayed gradient methods for solving symmetric positive definite linear systems …

We study gradient descent (GD) dynamics on logistic regression problems with large,
constant step sizes. For linearly-separable data, it is known that GD converges to the …

Fitting geometric or algebraic surfaces to 3D data is a pervasive problem in many fields of
science and engineering. In particular, ellipsoids are some of the most employed features in …

The performance of gradient methods has been considerably improved by the introduction
of delayed parameters. Recently, the revealing of second-order information has given rise to …

This project attempts to predict the scaled sound pressure levels in decibels, based on the
aerodynamic and acoustic related attributes. Each attribute can be regarded as a potential …

This paper studies the convergence properties of a family of Relaxed $\ell $-Minimal
Gradient Descent methods for quadratic optimization; the family includes the omnipresent …

The hydraulic shock absorber of a certain tractor suspension system is analyzed to
determine the influence of the cross-sectional area ratio of the throttling holes of the …