A revisit on Nesterov acceleration for linear ill-posed problems

In recent years, Nesterov acceleration has been introduced to enhance the efficiency of Landweber iteration for solving ill-posed problems. For linear ill-posed problems in Hilbert spaces, Nesterov acceleration has been analyzed with a discrepancy principle proposed to terminate the iterations. However, the existing approach requires computing residuals along two distinct iterative sequences, resulting in increased computational costs. In this paper, we propose an alternative discrepancy principle for Nesterov acceleration that eliminates the need to compute the residuals for one of the iterative sequences, thereby reducing computational time by approximately one-third per iteration. We provide a convergence analysis of the proposed method, establishing both its convergence and convergence rates. The effectiveness of our approach is demonstrated through numerical simulations.

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Publication Details

Journal
Journal of Complexity
Published
2024-12-04
DOI
https://doi.org/10.1016/j.jco.2024.101920
Citations
1
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
0.89
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article

A revisit on Nesterov acceleration for linear ill-posed problems

Qin Huang, Duo Liu, Qinian Jin
1 citations
Journal of Complexity
Numerical methods in inverse problems
0.89
article

A revisit on Nesterov acceleration for linear ill-posed problems

Qin Huang, Duo Liu, Qinian Jin
article en
1 citations

Abstract

In recent years, Nesterov acceleration has been introduced to enhance the efficiency of Landweber iteration for solving ill-posed problems. For linear ill-posed problems in Hilbert spaces, Nesterov acceleration has been analyzed with a discrepancy principle proposed to terminate the iterations. However, the existing approach requires computing residuals along two distinct iterative sequences, resulting in increased computational costs. In this paper, we propose an alternative discrepancy principle for Nesterov acceleration that eliminates the need to compute the residuals for one of the iterative sequences, thereby reducing computational time by approximately one-third per iteration. We provide a convergence analysis of the proposed method, establishing both its convergence and convergence rates. The effectiveness of our approach is demonstrated through numerical simulations.

Journal of ComplexityVol. 87
Australian National University (AU), Beijing Institute of Technology (CN), Beijing Jiaotong University (CN), Australian Mathematical Sciences Institute (AU)
Affordable and clean energy
Openalex Percentile: Top 25%
Numerical methods in inverse problems
0.89
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