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<p>In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of <i>t</i> fixed-point iteration steps, interleaved with <i>s</i> steps of Anderson acceleration with window size <i>m</i>, to solve linear and nonlinear problems. This allows flexibility to use different combinations of fixed-point iteration and Anderson iteration. We present a convergence analysis of the proposed scheme for accelerating the Richardson iteration in the linear case, with a focus on specific parameter choices of interest. Specifically, we prove convergence of the proposed method under contractive fixed-point iteration and provide a sufficient condition for convergence when the Richardson iteration matrix is diagonalizable and noncontractive. To demonstrate the broader applicability of our proposed method, we use it to accelerate Jacobi iteration, Gauss–Seidel iteration, Picard iteration, gradient descent, and the alternating direction method of multipliers in solving partial differential equations and nonlinear, nonsmooth optimization problems. The numerical results illustrate that the proposed scheme is more efficient than the existing windowed Anderson acceleration and alternating Anderson (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(s=1\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) in terms of iteration number and CPU time for careful choice of parameters <i>m</i>,&#xa0;<i>s</i>,&#xa0;<i>t</i>.</p>

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A Generalized Alternating Anderson Acceleration Method

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Yunhui He,
  • Santolo Leveque

摘要

In this work, we propose a generalized alternating Anderson acceleration method, a periodic scheme composed of t fixed-point iteration steps, interleaved with s steps of Anderson acceleration with window size m, to solve linear and nonlinear problems. This allows flexibility to use different combinations of fixed-point iteration and Anderson iteration. We present a convergence analysis of the proposed scheme for accelerating the Richardson iteration in the linear case, with a focus on specific parameter choices of interest. Specifically, we prove convergence of the proposed method under contractive fixed-point iteration and provide a sufficient condition for convergence when the Richardson iteration matrix is diagonalizable and noncontractive. To demonstrate the broader applicability of our proposed method, we use it to accelerate Jacobi iteration, Gauss–Seidel iteration, Picard iteration, gradient descent, and the alternating direction method of multipliers in solving partial differential equations and nonlinear, nonsmooth optimization problems. The numerical results illustrate that the proposed scheme is more efficient than the existing windowed Anderson acceleration and alternating Anderson ( \(s=1\) s = 1 ) in terms of iteration number and CPU time for careful choice of parameters m, s, t.