nlTGCR: A CLASS OF NONLINEAR ACCELERATION PROCEDURES BASED ON CONJUGATE RESIDUALS.

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. The code is available at https://github.com/Data-driven-numericalmethods/Nonlinear-Truncated-Conjugate-Residual. [ABSTRACT FROM AUTHOR]