A Regularized Tseng Method for Solving Various Variational Inclusion Problems and Its Application to a Statistical Learning Model

Autor: Adeolu Taiwo, Simeon Reich
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Axioms, Vol 12, Iss 11, p 1037 (2023)
Druh dokumentu: article
ISSN: 2075-1680
DOI: 10.3390/axioms12111037
Popis: We study three classes of variational inclusion problems in the framework of a real Hilbert space and propose a simple modification of Tseng’s forward-backward-forward splitting method for solving such problems. Our algorithm is obtained via a certain regularization procedure and uses self-adaptive step sizes. We show that the approximating sequences generated by our algorithm converge strongly to a solution of the problems under suitable assumptions on the regularization parameters. Furthermore, we apply our results to an elastic net penalty problem in statistical learning theory and to split feasibility problems. Moreover, we illustrate the usefulness and effectiveness of our algorithm by using numerical examples in comparison with some existing relevant algorithms that can be found in the literature.
Databáze: Directory of Open Access Journals
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