HAMEL NAIMA

Specialty: 
mathematics
Department: 
Faculty: 
Campus: Ancien Campus
  • hamel.naima@univ-guelma.dz
  • hamelnaima24@gmail.com
Doctoral advisor: 
Google Scholar Link: 
Rersearchgate Link: 
ORCID: 

Thesis title

Doctoral thesis title: 
The interrelation between conjugate gradient algorithms and Newton's algorithms

Thesis title (Ar)

Doctoral thesis title (Ar): 
العلاقة المتبادلة بين خوارزميات التدرج المترافق وخوارزميات نيوتن

Thesis title (Fr)

Doctoral thesis title (Fr): 
L’interrelation entre les algorithmes de gradient conjugué et ceux de Newton

keywords

Keywords: 
Conjugate gradient method. Newton method. Descent direction. Global convergence.

keywords (Ar)

Key words (Ar): 
طريقة التدرج المترافق. طريقة نيوتن.اتجاه النزول. تقارب عالمي

keywords (Fr)

Key words (Fr): 
Méthode de gradient conjugué. Méthode de Newton. Direction de descente. Convergence globale.

Abstract

Abstract: 

Both of these algorithms are quite mature, with well-established theoretical results and solid computational experience. The issue is that we have made no significant progress in designing efficient and robust algorithms for large-scale problems using concepts from these two classes of algorithms. We seek to define the relationships between these two algorithms to better understand the basis of their convergence, with the aim of building new, more efficient global optimization algorithms.

Abstract (Ar)

Abstract (Ar): 

تتمتع هاتان الخوارزميتان بنضج معين مع نتائج نظرية راسخة وخبرة حسابية قوية. تكمن المشكلة في أننا لم نحقق أي تقدم كبير في تصميم خوارزميات فعالة وقوية للمشكلات واسعة النطاق باستخدام مفاهيم من هاتين الفئتين من الخوارزميات. ونحن نسعى إلى تحديد العلاقات بين هاتين الخوارزميتين لفهم أسس تقاربهما بشكل أفضل، بهدف بناء خوارزميات تحسين عالمية جديدة وأكثر كفاءة.

Abstract (Fr)

Abstract (Fr): 

Ces deux algorithmes ont une certaine maturité avec des résultats théoriques très bien établis et une solide expérience en calcul. La question est que nous n’avons aucun progrès significatif dans la conception d’algorithmes efficaces et robustes pour des problèmes à grande échelle utilisant des concepts issus de ces deux classes d’algorithmes. Nous cherchons à définir les relations entre ces  deux algorithmes  pour mieux appréhender les fondements  de leurs convergence, dans le but de construire de nouveaux algorithmes d’optimisation globale plus performent.

Scientific publications

Scientific publications : 

Scientific publications

Publications internationales:

1. A new hybrid conjugate gradient algorithm based on the Newton direction to solve unconstrained optimization problems. J. Appl. Math. Comput. 69, 2531–2548 (2023). https://doi.org/10.1007/s12190-022-01821-z

Scientific conferences

Scientific conferences: 

Scientific conferences

 Scientific international conferences:

  1.  6 th INTERNATIONAL HYBRID CONFERENCE ON MATHEMATICS “An Istanbul Meeting for World Mathematicians” 21-24 June 2022, Istanbul, Turkey. A new conjugate gradient method as a modified Conjugate Descent method using the Newton direction for unconstrained optimization.  https://www.kongreuzmani.com/6-international-conference-
  2. The 6th International Workshop on Applied Mathematics and Modelling « WIMAM’2022», Guelma, Algeria, 26-27 October 2022.  A convex combination between two di erent descent directions of conjugate gradient method using the Newton direction for unconstrained optimization. https://www.univ-guelma.dz/sites/default/files/2022-09/WIMAM2022.pdf 
  3. INTERNATIONAL CONFERENCE ON MATHEMATICS APPLIED IN LIFE SCIENCES, June 23-24, 2022, Iași, Romania. Employing Newton's method to modify the CD-    conjugate gradient method  for unconstrained optimization. http://uaiasi.ro/horticultura/icmals2022/

 Scientific national conferences:

  1. Third National Mathematics Seminar , Mentouri University of Constantine 1, Algeria. A modified Conjugate Gradient method based on the Newton direction for solving unconstrained optimization problems, Constantine, Algeria. 26.05.2022. http://seminars.labomam.net/
  2. The Second National Conference on Mathematics and its Applications (2nd SCNMA 2022) September 17-18, Bordj Bou Arréridj, Algeria. An adaptive version of the conjugate gradient parameter βk CD  using the Newton direction for unconstrained optimization. https://site-5709206-7403-6518.mystrikingly.com/