An MHS-type Spectral Conjugate Gradient Method and Its Global Convergence.

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Title:	An MHS-type Spectral Conjugate Gradient Method and Its Global Convergence.
Authors:	Zhang, Keke¹ coco_xidian@163.com, Wang, Chunfeng² wangchunfeng09@126.com, Yang, Jun² xysfyangjun@163.com
Source:	IAENG International Journal of Applied Mathematics. Jun2026, Vol. 56 Issue 6, p2187-2195. 9p.
Subjects:	Conjugate gradient methods, Mathematical optimization, Iterative methods (Mathematics), Mathematical programming, Numerical analysis
Abstract:	As a highly effective iterative technique, spectral conjugate gradient method is one of the most crucial classes of algorithms for solving large-scale unconstrained optimization problems. Within the scope of this work, an MHS-type spectral conjugate gradient method is developed by integrating the MHS conjugate gradient method with the classical spectral conjugate gradient method. The new search direction fulfills the sufficient descent property without imposing additional line search constraints. Furthermore, under some mild assumptions and the Wolfe line search conditions, we have proven the method is globally convergent. The numerical experimental results demonstrate that the MHS-type spectral conjugate gradient method exhibits favorable and competitive computational performance when addressing large-scale unconstrained optimization problems. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	As a highly effective iterative technique, spectral conjugate gradient method is one of the most crucial classes of algorithms for solving large-scale unconstrained optimization problems. Within the scope of this work, an MHS-type spectral conjugate gradient method is developed by integrating the MHS conjugate gradient method with the classical spectral conjugate gradient method. The new search direction fulfills the sufficient descent property without imposing additional line search constraints. Furthermore, under some mild assumptions and the Wolfe line search conditions, we have proven the method is globally convergent. The numerical experimental results demonstrate that the MHS-type spectral conjugate gradient method exhibits favorable and competitive computational performance when addressing large-scale unconstrained optimization problems. [ABSTRACT FROM AUTHOR]
ISSN:	19929978