A Novel Iterative Algorithm for Regularized Separable Nonlinear Inverse Problems

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This is a Quantitative Study study.

Hui Hu; Wenquan Xu

doi:10.1142/s0219876225500586

September 24, 2025International Journal of Computational Methods

A Novel Iterative Algorithm for Regularized Separable Nonlinear Inverse Problems

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Key Points

The proposed algorithm offers significant efficiency in solving nonlinear inverse problems, enhancing computational speed.
Key metric indicates improved accuracy in results, demonstrating the algorithm's robustness in various applications.
The method integrates regularization techniques, providing effective handling of ill-posed problems in computational mathematics.
This approach may enable new advancements in algorithm development and broaden applicable areas in inverse problem solutions.

Implication

Novel algorithm improves solutions for nonlinear inverse problems, suggesting enhancements in computational methods.

Abstract

International Journal of Computational MethodsAccepted Papers No AccessA Novel Iterative Algorithm for Regularized Separable Nonlinear Inverse ProblemsHui Hu and Wenquan XuHui Hu and Wenquan Xu Search for more papers by this author https://doi.org/10.1142/S0219876225500586Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out these titles in finite element methods! FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 6 April 2025 Accepted 16 September 2025 PDF download

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Cite This Study

Hu et al. (2025) studied this question.

synapsesocial.com/papers/68d6e1248b2b6861e4c3f917 https://doi.org/10.1142/s0219876225500586

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A Novel Iterative Algorithm for Regularized Separable Nonlinear Inverse Problems

Key Points

Implication

Abstract

Discussion

Cite This Study

A Novel Iterative Algorithm for Regularized Separable Nonlinear Inverse Problems

Key Points

Implication

Abstract

Discussion

Cite This Study

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