<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we propose a modification that improves efficiency, robustness and reliability of the famous HS conjugate gradient method. In particular, we propose a hybrid of the HS and DHS methods, where DHS is another recent modification of the HS method. Irrespective of the line search, the search direction of the proposed method is sufficiently descent. Moreover, the new approach guarantees global convergence for general functions under the strong Wolfe line search. Numerical results and performance profiles are reported, and indicate that the new approach outperforms three similar methods in the literature. We also give a practical application of the new approach in minimizing risk in portfolio selection.</p>
</abstract>

In this paper, we propose a modification that improves efficiency, robustness and reliability of the famous HS conjugate gradient method. In particular, we propose a hybrid of the HS and DHS methods, where DHS is another recent modification of the HS method. Irrespective of the line search, the search direction of the proposed method is sufficiently descent. Moreover, the new approach guarantees global convergence for general functions under the strong Wolfe line search. Numerical results and performance profiles are reported, and indicate that the new approach outperforms three similar methods in the literature. We also give a practical application of the new approach in minimizing risk in portfolio selection.

Application of a globally convergent hybrid conjugate gradient method in portfolio optimization

Journal of Applied Mathematics, Statistics and Informatics

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, we propose a modification that improves efficiency, robustness and reliability of the famous HS conjugate gradient method. In particular, we...

Application of a globally convergent hybrid conjugate gradient method in portfolio optimization

Publié en ligne: 03 juin 2024

Pages: 33 - 52

DOI: https://doi.org/10.2478/jamsi-2024-0003

Mots clés
Conjugate gradient, Global convergence, Strong Wolfe-Powell conditions, Portfolio selection

© 2024 P. Mtagulwa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Application of a globally convergent hybrid conjugate gradient method in portfolio optimization

Publié en ligne: 03 juin 2024

Pages: 33 - 52

DOI: https://doi.org/10.2478/jamsi-2024-0003

Mots clésConjugate gradient, Global convergence, Strong Wolfe-Powell conditions, Portfolio selection

© 2024 P. Mtagulwa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Conjugate gradient, Global convergence, Strong Wolfe-Powell conditions, Portfolio selection