A Dai-Liao-type projection method for monotone nonlinear equations and signal processing

• Autorzy: Ibrahim Abdulkarim Hassan , Kumam Poom , Abubakar Auwal Bala , Abdullahi Muhammad Sirajo , Mohammad Hassan

Identyfikatory

DOI

10.1515/dema-2022-0159

• Języki publikacji: EN

Abstrakty

EN

In this article, inspired by the projection technique of Solodov and Svaiter, we exploit the simple structure, low memory requirement, and good convergence properties of the mixed conjugate gradient method of Stanimirović et al. [New hybrid conjugate gradient and broyden-fletcher-goldfarbshanno conjugate gradient methods, J. Optim. Theory Appl. 178 (2018), no. 3, 860–884] for unconstrained optimization problems to solve convex constrained monotone nonlinear equations. The proposed method does not require Jacobian information. Under monotonicity and Lipschitz continuity assumptions, the global convergence properties of the proposed method are established. Computational experiments indicate that the proposed method is computationally efficient. Furthermore, the proposed method is applied to solve the ℓ1 -norm regularized problems to decode sparse signals and images in compressive sensing.

Słowa kluczowe

EN

nonlinear equations; unconstrained optimization; conjugate gradient method; projection method; compressive sensing

PL

równania nieliniowe; metoda gradientu sprzężonego; metoda rzutowania; próbkowanie oszczędne

• Wydawca: De Gruyter
• Czasopismo: Demonstratio Mathematica
• Rocznik: 2022
• Tom: Vol. 55, nr 1
• Strony: 978--1013
• Opis fizyczny: Bibliogr. 54 poz., rys., tab., wykr.

Twórcy

autor

Ibrahim Abdulkarim Hassan

• Center of Excellence in Theoretical and Computational Science (TaCS-CoE), SCL 802 Fixed Point Laboratory, Science Laboratory Building, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha Uthit Rd., Bang Mod, Thung Khru, Bangkok 10140, Thailand
• Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Ga-Rankuwa, Pretoria, Medunsa-0204, South Africa

autor

Kumam Poom

• Center of Excellence in Theoretical and Computational Science (TaCS-CoE), SCL 802 Fixed Point Laboratory, Science Laboratory Building, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha Uthit Rd., Bang Mod, Thung Khru, Bangkok 10140, Thailand
• Department of Medical Research, China
• Medical University Hospital, China Medical University, Taichung 40402, Taiwan

autor

Abubakar Auwal Bala

• Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Ga-Rankuwa, Pretoria, Medunsa-0204, South Africa
• Department of Mathematical Sciences, Faculty of Physical Sciences, Bayero University, Kano. Kano, Nigeria

autor

Abdullahi Muhammad Sirajo

• Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria

autor

Mohammad Hassan

• Department of Mathematical Sciences, Faculty of Physical Sciences, Bayero University, Kano. Kano, Nigeria

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).