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A Modified Conjugate Gradient Projection Method for Constrained Monotone Equations with Applications.

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Title: A Modified Conjugate Gradient Projection Method for Constrained Monotone Equations with Applications.
Authors: Hu, Yaping1 (AUTHOR) huyaping@tust.edu.cn
Source: Journal of Optimization Theory & Applications. Dec2025, Vol. 207 Issue 3, p1-22. 22p.
Abstract: This paper presents an enhanced Wei-Yao-Liu conjugate gradient projection algorithm, tailored for solving large-scale nonlinear convex constrained monotone equations. The algorithm’s search direction guarantees sufficient descent, while both the direction and line search are derivative-free, making it highly efficient for large-scale problems. We prove the algorithm’s global convergence under suitable assumptions and demonstrate its applicability to sparse signal reconstruction and blurry image recovery in compressive sensing. Numerical experiments validate the algorithm’s effectiveness, especially in large-scale scenarios, underscoring the advantages of its derivative-free design. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 187330777
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  Data: A Modified Conjugate Gradient Projection Method for Constrained Monotone Equations with Applications.
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  Data: This paper presents an enhanced Wei-Yao-Liu conjugate gradient projection algorithm, tailored for solving large-scale nonlinear convex constrained monotone equations. The algorithm’s search direction guarantees sufficient descent, while both the direction and line search are derivative-free, making it highly efficient for large-scale problems. We prove the algorithm’s global convergence under suitable assumptions and demonstrate its applicability to sparse signal reconstruction and blurry image recovery in compressive sensing. Numerical experiments validate the algorithm’s effectiveness, especially in large-scale scenarios, underscoring the advantages of its derivative-free design. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Optimization Theory & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10957-025-02820-3
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      – Code: eng
        Text: English
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      – TitleFull: A Modified Conjugate Gradient Projection Method for Constrained Monotone Equations with Applications.
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              M: 12
              Text: Dec2025
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              Y: 2025
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