Open AccessA Modified Scaled Spectral-Conjugate Gradient-Based Algorithm for Solving Monotone Operator Equations
Author(s) -
Auwal Bala Abubakar,
Kanikar Muangchoo,
Abdulkarim Hassan Ibrahim,
Sunday Emmanuel Fadugba,
Kazeem Olalekan Aremu,
Lateef Olakunle Jolaoso
Publication year - 2021
Publication title -
journal of mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.252
H-Index - 13
eISSN - 2314-4785
pISSN - 2314-4629
DOI - 10.1155/2021/5549878
Subject(s) - mathematics , conjugate gradient method , monotone polygon , monotonic function , convergence (economics) , operator (biology) , operator splitting , algorithm , derivation of the conjugate gradient method , bounded function , conjugate , gradient descent , conjugate residual method , mathematical analysis , computer science , biochemistry , chemistry , geometry , repressor , machine learning , artificial neural network , transcription factor , economics , gene , economic growth
This paper proposes a modified scaled spectral-conjugate-based algorithm for finding solutions to monotone operator equations. The algorithm is a modification of the work of Li and Zheng in the sense that the uniformly monotone assumption on the operator is relaxed to just monotone. Furthermore, unlike the work of Li and Zheng, the search directions of the proposed algorithm are shown to be descent and bounded independent of the monotonicity assumption. Moreover, the global convergence is established under some appropriate assumptions. Finally, numerical examples on some test problems are provided to show the efficiency of the proposed algorithm compared to that of Li and Zheng.
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