Nonlinear-Tructated-GCR

Optimization, Iterative Algorithm, Numerical Analysis Under Review in SIAM Journal On Optimization

Abstract:

This paper develops a new class of nonlinear acceleration algorithms based on extending conjugate residual-type procedures from linear to nonlinear equations. The main algorithm has strong similarities with Anderson acceleration as well as with inexact Newton methods - depending on which variant is implemented. We prove theoretically and verify experimentally, on a variety of problems from simulation experiments to deep learning applications, that our method is a powerful accelerated iterative algorithm.

Matlab:

Usage:

• To run the code, run run_me_first.m first. To reproduce the experiments in Figure 5.1-5.3 of the paper, go to scripts folder.

Contents:

• src folder contains implementations of baselines and NLTGCR with nonlinear, linear, and adaptive update.
• scripts folder contains experiments of the Bratu's problem (Section 5.1) and the Lennard-Jones problem (Section 5.2).
• problem folder contains functions to compute the gradient and cost of the Bratu's problem and the Lennard-Jones problem at a given point.
• line_search folder contains auxilary functions for line search in baselines.

Python:

Contents:

• test_nltgcr_demo.py solves a linear system. The convergence is identical to CG, which verifies the correctness of implementation.
• main.py is for baselines inlcuding SGD, Nesterov, Adam. main_nltgcr.py is for our method.

Paper:

NLTGCR: A class of Nonlinear Acceleration Procedures based on Conjugate Residuals