The Matlab function cplusag in this repository minimizes a smooth convex function f via nonlinear conjugate gradient. A sufficient-progress measure
defined in terms of estimating sequences from Nesterov, Introductory Lectures on Convex Optimization, Kluwer 2004, is invoked on each iteration, and if
insufficient progress is made, the method switches to accelerated gradient. Refer to our paper Karimi & Vavasis, Nonlinear conjugate gradient for smooth
convex functions, available on arxiv.org, 2021.
A version of accelerated gradient is available in ag. In addition, there are three smooth convex test cases available. Function makeorthol1test computes
the ABPDN (approximate basis-pursuit denoising). Function makehandles_logistic computes logistic regression, and make_huber_regression
computes Huber regression. All three test problems are described in the paper.
Hager & Zhang's CG-Descent is available here: http://users.clas.ufl.edu/hager/papers/Software/ We have used version 6.8. Note that version 6.8 sometimes crashes
in Matlab because it runs out of memory. This repository contains a modified version of the Matlab front-end, called cg_descent.c, that is more aggressive
in freeing up memory from intermediate results and seems to fix the out-of-memory issues. Note that the file cg_descent.c here is meant to
replace the file cg_descent.c that appears in Hager & Zhang's subdirectory called MATLAB, not the file with the same name in the main
directory.