Kernel function with BFGS quasi-newton methods for solving nonlinear semi-definite problems
Volume 33, Issue 1, pp 1--16
http://dx.doi.org/10.22436/jmcs.033.01.01
Publication Date: November 16, 2023
Submission Date: February 17, 2023
Revision Date: September 20, 2023
Accteptance Date: October 14, 2023
Authors
M. Laouar
- Laboratory of Partial Differential Equations, University of Batna 2, Batna 05000, Algeria.
M. Brahimi
- Laboratory of Partial Differential Equations, University of Batna 2, Batna 05000, Algeria.
I. E. Lakhdari
- Laboratory of Probabilities and Optimizations, University of Biskra, Biskra 07000, Algeria.
Abstract
In this paper, we will improve the practical performance of an interior
points algorithm for convex nonlinear semi-definite optimization, where to minimize a nonlinear convex
objective function subject to nonlinear convex constraints. We will propose a new method
for solving this kind of problem by using a straightforward kernel function and the iterative
Newton directions combined with the Broyden-Fletcher-Goldfarb-Shanno (BFGS in short)
quasi-Newton method. Further, a best polynomial complexity for solving nonlinear convex
problems will be found until now.
Share and Cite
ISRP Style
M. Laouar, M. Brahimi, I. E. Lakhdari, Kernel function with BFGS quasi-newton methods for solving nonlinear semi-definite problems, Journal of Mathematics and Computer Science, 33 (2024), no. 1, 1--16
AMA Style
Laouar M., Brahimi M., Lakhdari I. E., Kernel function with BFGS quasi-newton methods for solving nonlinear semi-definite problems. J Math Comput SCI-JM. (2024); 33(1):1--16
Chicago/Turabian Style
Laouar, M., Brahimi, M., Lakhdari, I. E.. "Kernel function with BFGS quasi-newton methods for solving nonlinear semi-definite problems." Journal of Mathematics and Computer Science, 33, no. 1 (2024): 1--16
Keywords
- Algorithm
- nonlinear semidefinite optimization
- Kernel function
- BFGS quasi-Newton method
MSC
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