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2009•International Journal of Computer MathematicsRequires access

Accelerate weighted GMRES by augmenting error approximations

Qiang Niu, Linzhang Lu, Jituan Zhou

Open publisher page 7 citations

Abstract

By augmenting error approximations at every restart cycle, this paper presents an accelerating strategy for restarted weighted generalized minimum residual (GMRES) method. We show that the procedure can effectively correct the occurrence of small skip D-angles, which indicates a slow convergent phase. Numerical results show that the new method converges much regular and faster than the weighted GMRES method. Finally, comparisons are made between the new and the recently proposed LGMRES methods.

About this research paper

What this paper is about

By augmenting error approximations at every restart cycle, this paper presents an accelerating strategy for restarted weighted generalized minimum residual (GMRES) method. We show that the procedure can effectively correct the occurrence of small skip D-angles, which indicates a slow convergent phase. Numerical results show that the new method converges much regular and faster than the weighted GMRES method. Finally, comparisons are made between the new and the recently proposed LGMRES methods.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

By augmenting error approximations at every restart cycle, this paper presents an accelerating strategy for restarted weighted generalized minimum residual (GMRES) method. We show that the procedure can effectively correct the occurrence of small skip D-angles, which indicates a slow convergent phase. Numerical results show that the new method converges much regular and faster than the weighted GMRES method. Finally, comparisons are made between the new and the recently proposed LGMRES methods.

Key concepts: Generalized minimal residual method, Residual, Mathematics, Applied mathematics, Mathematical optimization, Algorithm

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