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September 28, 20250 citationsOpen Access

Anderson Acceleration For Perturbed Newton Methods

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MDMatt DallasThermo Fisher Scientific (United States)

Key Points

  • Anderson acceleration enhances convergence rates in perturbed Newton methods, ensuring more efficient root-finding.
  • Key finding shows local linear convergence in a starlike domain for 2-regular problems using gamma-safeguarding.
  • Application involves both classical Newton and Levenberg-Marquardt methods, yielding novel acceleration results.
  • Safeguarding technique effectively detects superlinear convergence, allowing for dynamic adjustments to the Anderson step.

Abstract

We present a convergence theory For Anderson acceleration (AA) applied to perturbed Newton methods (pNMs) For computing roots of nonlinear problems. Two important special cases are the classical Newton method and the Levenberg-Marquardt method. We prove that if a problem is 2-regular, then Anderson accelerated pNMs coupled with a safeguarding scheme, known as γ-safeguarding, converge locally linearly in a starlike domain of convergence, but with an improved rate of convergence compared to standard perturbed Newton methods. Since Levenberg-Marquardt methods are a special case of pNMs, we obtain a novel acceleration and local convergence result For Anderson accelerated Levenberg-Marquardt. We further show that the safeguarding technique can detect if the underlying perturbed Newton method is converging superlinearly, and respond by tuning the Anderson step down. We demonstrate the methods on several benchmark problems in the literature.

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Cite This Study

Matt Dallas (2025) studied this question.

synapsesocial.com/papers/68d913a34ddcf71ba560b986https://doi.org/10.48550/arxiv.2508.12513
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Also Consider

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  1. 1Convergence Analysis of the Alternating Anderson-Picard Method for Nonlinear Fixed-point Problems2024
  2. 2Anderson acceleration with approximate calculations: Applications to scientific computing2024 · 2 citations
  3. 3Anderson Acceleration Without Restart: A Novel Method with $n$-Step Super Quadratic Convergence Rate2024
  4. 4Accelerating Proximal Gradient-type Algorithms using Damped Anderson Acceleration with Restarts and Nesterov Initialization2025
  5. 5Analysis of an Adaptive Safeguarded Newton-Anderson Algorithm with Applications to Fluid Problems2024