A q-Gradient Descent Algorithm with Quasi-Fejér Convergence for Unconstrained Optimization Problems

We present an algorithm for solving unconstrained optimization problems based on the q-gradient vector. The main idea used in the algorithm construction is the approximation of the classical gradient by a q-gradient vector. For a convex objective function, the quasi-Fejér convergence of the algorith...

Full description

Saved in:
Bibliographic Details
Published in:Fractal and fractional 2021-09, Vol.5 (3), p.110
Main Authors: Mishra, Shashi Kant, Rajković, Predrag, Samei, Mohammad Esmael, Chakraborty, Suvra Kanti, Ram, Bhagwat, Kaabar, Mohammed K. A.
Format: Article
Language:English
Subjects:
Algorithms
Calculus
Convergence
descent methods
inexact line searches
iterative methods
Optimization
Optimization algorithms
q-calculus
Standard deviation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We present an algorithm for solving unconstrained optimization problems based on the q-gradient vector. The main idea used in the algorithm construction is the approximation of the classical gradient by a q-gradient vector. For a convex objective function, the quasi-Fejér convergence of the algorithm is proved. The proposed method does not require the boundedness assumption on any level set. Further, numerical experiments are reported to show the performance of the proposed method.
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract5030110