A new three-term conjugate gradient algorithm based on the Dai-Liao  and the Liu-Xu  conjugate gradient methods

Khalil K. ABBO

Makale detayları

A new three-term conjugate gradient algorithm based on the Dai-Liao and the Liu-Xu conjugate gradient methods

Yazarlar:

Khalil K. ABBO

Yayıncı:

Ahmet ŞAHİNER

Ana Başlıklar:

Matematik

Dergi:

Journal of Multidisciplinary Modeling and Optimization 2020 Vol.3 Issue 1, pp.35-46

Belge Tipi:

Makale

Yayın Tarihi:

9/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000743110

Sıklık:

İki Yıllık

eISSN:

2645-923X

Özet:

 

Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.
Based on the Dai-Laio and Liu-Xu methods, we develop a new three-term conjugate gradient method for solving large-scale unconstrained optimization problem,. The suggested method satisfies both the descent condition and the conjugacy condition. For  uniformly convex function, under standard assumption the global convergence of the algorithm is proved. Finally, some numerical results of the proposed method are given.

Makale detayları

A new three-term conjugate gradient algorithm based on the Dai-Liao and the Liu-Xu conjugate gradient methods

Yazarlar: Khalil K. ABBO

Yayıncı: Ahmet ŞAHİNER

Ana Başlıklar: Matematik

Dergi: Journal of Multidisciplinary Modeling and Optimization 2020 Vol.3 Issue 1, pp.35-46

Belge Tipi: Makale

Yayın Tarihi: 9/2020

Belge Dili: İngilizce

Document ID (Org No.): 2000743110

Sıklık: İki Yıllık

eISSN: 2645-923X

Sayı

Yazarlar:

Khalil K. ABBO

Yayıncı:

Ahmet ŞAHİNER

Ana Başlıklar:

Matematik

Dergi:

Journal of Multidisciplinary Modeling and Optimization 2020 Vol.3 Issue 1, pp.35-46

Belge Tipi:

Makale

Yayın Tarihi:

9/2020

Belge Dili:

İngilizce

Document ID (Org No.):

2000743110

Sıklık:

İki Yıllık

eISSN:

2645-923X