A q-gradient descent algorithm with quasi-fejer convergence for unconstrained optimization problems
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
Divisions
MathematicalSciences
Publication Title
Fractal and Fractional
Volume
5
Issue
3
Publisher
MDPI
Publisher Location
ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
COinS