Research on the Anderson Acceleration (AA) has exploded in the last 15 years. This book brings together these recent fundamental results applied to nonlinear solvers for PDEs, which are ubiquitous across mathematics, science, engineering, and economics as predictive models for a vast quantity of important phenomena. Coverage includes:
AA convergence theory for both contractive and non-contractive operators, filtering techniques for AA, showing how the convergence theory can be fit to various application problems, and AAs effect on sublinear convergence, and how AA can be best combined with Newtons method.
The authors provide proofs of the main theorems and results of many of the test examples. Code for the test examples is provided in an online repository.
Audience Anderson Acceleration for Numerical PDE is intended for mathematicians, scientists, and engineers who solve nonlinear problems when Newtons method either fails or is inefficient. Graduate students in applied mathematics and computational science will also find the book useful. It has sufficient theory and coding for use in a second-year graduate course.
