Acceleration methods for fixed point iterations
Yousef Saad

TL;DR
This paper reviews various acceleration techniques for fixed point iterations, highlighting methods like Anderson Acceleration that improve convergence speed in scientific computing.
Contribution
It provides a comprehensive overview of fixed point acceleration methods, emphasizing Anderson Acceleration and related techniques, and discusses their applications and effectiveness.
Findings
Anderson Acceleration significantly speeds up fixed point iterations.
Extrapolation methods like Aitken's process are simple but limited.
Accelerating fixed point methods enhances performance in physics simulations.
Abstract
A pervasive approach in scientific computing is to express the solution to a given problem as the limit of a sequence of vectors or other mathematical objects. In many situations these sequences are generated by slowly converging iterative procedures and this led practitioners to seek faster alternatives to reach the limit. ``Acceleration techniques'' comprise a broad array of methods specifically designed with this goal in mind. They started as a means of improving the convergence of general scalar sequences by various forms of ``extrapolation to the limit'', i.e., by extrapolating the most recent iterates to the limit via linear combinations. Extrapolation methods of this type, the best known example of which is Aitken's Delta-squared process, require only the sequence of vectors as input. However, limiting methods to only use the iterates is too restrictive. Accelerating sequences…
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Taxonomy
TopicsMatrix Theory and Algorithms · Advanced Optimization Algorithms Research · Numerical methods for differential equations
