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...

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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
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cited_by cdi_FETCH-LOGICAL-c388t-975c9fc093cc70510b0cc5d8cef083850e06dbac95a4d1a7a781adfcb154f1e23
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container_issue 3
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container_title Fractal and fractional
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creator Mishra, Shashi Kant
Rajković, Predrag
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description 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.
doi_str_mv 10.3390/fractalfract5030110
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subjects Algorithms
Calculus
Convergence
descent methods
inexact line searches
iterative methods
Optimization
Optimization algorithms
q-calculus
Standard deviation
title A q-Gradient Descent Algorithm with Quasi-Fejér Convergence for Unconstrained Optimization Problems
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