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123 results on '"Ibrahim, Abdulkarim Hassan"'

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

Author

Ibrahim Abdulkarim Hassan, Kumam Poom, Abubakar Auwal Bala, Abdullahi Muhammad Sirajo, and Mohammad Hassan

Subjects
nonlinear equations, unconstrained optimization, conjugate gradient method, projection method, compressive sensing, 90c30, 65k05, 90c53, 49m37, 15a18, Mathematics, QA1-939
Abstract

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{\ell }_{1}-norm regularized problems to decode sparse signals and images in compressive sensing.

Published
2022
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33. A new black box method for monotone nonlinear equations.

Author

Ibrahim, Abdulkarim Hassan, Kimiaei, Morteza, and Kumam, Poom

Subjects
*NONLINEAR equations
Abstract

This paper discusses a new derivative-free line search method for nonlinear monotone equations. It uses a derivative-free direction based on Dai–Liao method, along which an improved line search – called IDFLS – is tried. In contrast to the basic line search method – called BasicLS – by Solodov and Svaiter, IDFLS uses extrapolation steps to guarantee a decrease in the function norm. In fact, IDFLS never accept a point with the worst function norm, while BasicLS may accept such a point. The global convergence of our method is established under the assumption that the underlying mapping is monotone. The numerical results show that the new method is competitive in comparison with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2023
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36. Some new scaled conjugate gradient methods via symmetric rank-one update for unconstrained optimization.

Author

Hassan, Basim A., Jabbar, Hawraz N., Alfarag, Fadhil, Abdullahi, Sunusi Bala, and Ibrahim, Abdulkarim Hassan

Subjects
CONJUGATE gradient methods, ALGORITHMS, MATRICES (Mathematics)
Abstract

This work demonstrates some of the scaled conjugate gradient algorithms based on order-one symmetric modernization. We adopted the outstanding attributes of the symmetric rank-one update (SR1) in providing superior Hessian approximations that lead to the development of a conjugate gradient with descent property and having no resources for the matrices. The obtained numerical results depict the superiority of the method. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Optimizing Hammerstein-Wiener Model for Forecasting Confirmed Cases of Covid-19.

Author

Abdullahi, Sunusi Bala, Ibrahim, Abdulkarim Hassan, Abubakar, Auwal Bala, and Kambheera, Abhiwat

Subjects
*COVID-19 pandemic, *FORECASTING, *STAY-at-home orders, *SOFT computing, *VIRAL transmission
Abstract

Noise poses challenge to nonlinear Hammerstein- Wiener (HW) subsystem model application, because HW subsystem need large number of parameter interactions. However, flexibility, soft computing, and automatic adjustment to dynamic observation for best model fitting make it potential for forecasting nonlinear data. In this article, we adopted improved HW inference from Levenberg-Marquardt optimization algorithm to optimize HW subsystem and to select best model parameters. Therefore, the adopted model is tested on COVID- 19 confirmed reported cases, to estimate transmission rate of COVID-19 virus for period from 15th March 2020 to 29th April 2020. Model validation is carried out on small dataset, which outperforms some existing models. The adopted model is further evaluated using statistical metrics and reported best accuracy of 0.127 and 0.998 for Mean Absolute percentage error (MAPE) and coefficient of determination (R²) respectively, with best model complexity of 1.86. The obtained results are promising enough in predicting spread of COVID-19 virus and may inspire as guidance to relax lockdown restriction policies. [ABSTRACT FROM AUTHOR]

Published
2022

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