A q-gradient descent algorithm with quasi-fejer convergence for unconstrained optimization problems

Authors

• Shashi Kant Mishra
• Predrag Rajkovic
• Mohammad Esmael Samei
• Suvra Kanti Chakraborty
• Bhagwat Ram
• Mohammed K. A. Kaabar

Document Type

Article

Publication Date

9-1-2021

Abstract

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-Fejer 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.

Keywords

Descent methods, Q-calculus, Iterative methods, Inexact line searches

Publication Title

Fractal and Fractional

Recommended Citation

Mishra, Shashi Kant; Rajkovic, Predrag; Samei, Mohammad Esmael; Chakraborty, Suvra Kanti; Ram, Bhagwat; and Kaabar, Mohammed K. A., "A q-gradient descent algorithm with quasi-fejer convergence for unconstrained optimization problems" (2021). Research Publications (2021 to 2025). 6774.
https://knova.um.edu.my/research_publications_2021_2025/6774

Divisions

MathematicalSciences

Volume

5

Issue

3

Publisher

MDPI

Publisher Location

ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND