54. Anderson acceleration with adaptive relaxation for convergent fixed-point iterations
Invited abstract in session WC-3: Acceleration Methods in Optimization, stream Large scale optimization: methods and algorithms.
Wednesday, 14:00-16:00Room: B100/4011
Authors (first author is the speaker)
| 1. | Nicolas Lepage-Saucier
|
| Economics, Concordia University |
Abstract
Two adaptive relaxation strategies are proposed for Anderson acceleration. They are specifically designed for applications in which mappings converge to a fixed point. Their superiority over alternative Anderson acceleration is demonstrated for linear contraction mappings. Both strategies perform well in three nonlinear fixed-point applications that include partial differential equations and the EM algorithm. One strategy surpasses all other Anderson acceleration implementations tested in terms of computation time across various specifications, including composite Anderson acceleration.
Keywords
- Large-scale optimization
- Computational mathematical optimization
Status: accepted
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