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On the Extension of Dai-Liao Conjugate Gradient Method for Vector Optimization.

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Title: On the Extension of Dai-Liao Conjugate Gradient Method for Vector Optimization.
Authors: Hu, Qingjie1,2,3 (AUTHOR) hqj0715@126.com.cn, Li, Ruyun1,2,3 (AUTHOR) liruyun2023@163.com, Zhang, Yanyan1,2,3 (AUTHOR), Zhu, Zhibin1,2,3 (AUTHOR)
Source: Journal of Optimization Theory & Applications. Oct2024, Vol. 203 Issue 1, p810-843. 34p.
Subjects: Vector valued functions, Convex functions, Conjugate gradient methods
Abstract: In this paper, we extend the Dai-Liao conjugate gradient method to vector optimization. Firstly, we analyze the global convergence of the direct extension version of the Dai-Liao conjugate gradient method for K-strongly convex vector functions. Secondly, we investigate the global convergence of the vector version of restricted non-negative Dai-Liao conjugate gradient method for general vector functions. Additionally, we discuss the global convergence of the vector version of modified Dai-Liao conjugate gradient method for general vector functions. Finally, numerical experiments demonstrate that the proposed conjugate gradient methods are effective for solving vector optimization problems. In particular, these methods can effectively generate the Pareto frontiers for the test problems. [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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  Data: On the Extension of Dai-Liao Conjugate Gradient Method for Vector Optimization.
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  Data: <searchLink fieldCode="AR" term="%22Hu%2C+Qingjie%22">Hu, Qingjie</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> hqj0715@126.com.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Ruyun%22">Li, Ruyun</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> liruyun2023@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yanyan%22">Zhang, Yanyan</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Zhibin%22">Zhu, Zhibin</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Optimization+Theory+%26+Applications%22">Journal of Optimization Theory & Applications</searchLink>. Oct2024, Vol. 203 Issue 1, p810-843. 34p.
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  Data: <searchLink fieldCode="DE" term="%22Vector+valued+functions%22">Vector valued functions</searchLink><br /><searchLink fieldCode="DE" term="%22Convex+functions%22">Convex functions</searchLink><br /><searchLink fieldCode="DE" term="%22Conjugate+gradient+methods%22">Conjugate gradient methods</searchLink>
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  Label: Abstract
  Group: Ab
  Data: In this paper, we extend the Dai-Liao conjugate gradient method to vector optimization. Firstly, we analyze the global convergence of the direct extension version of the Dai-Liao conjugate gradient method for K-strongly convex vector functions. Secondly, we investigate the global convergence of the vector version of restricted non-negative Dai-Liao conjugate gradient method for general vector functions. Additionally, we discuss the global convergence of the vector version of modified Dai-Liao conjugate gradient method for general vector functions. Finally, numerical experiments demonstrate that the proposed conjugate gradient methods are effective for solving vector optimization problems. In particular, these methods can effectively generate the Pareto frontiers for the test problems. [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/s10957-024-02535-x
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      – Code: eng
        Text: English
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        PageCount: 34
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    Subjects:
      – SubjectFull: Vector valued functions
        Type: general
      – SubjectFull: Convex functions
        Type: general
      – SubjectFull: Conjugate gradient methods
        Type: general
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              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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