Work

A Novel Conjugate Gradient Algorithm for Unconstrained Optimization and Its Application in COVID-19 Data Parameterization

Year: 2025

Type: article

Abstract: To solve unconstrained optimization problems, this study proposes a conjugate gradient (CG) algorithm that satisfies both the convergence and descent conditions. The technique improves on conventional CG techniques through guaranteeing quick convergence and increased solution accuracy across a range of test functions. The study also applies this CG method to model COVID-19 transmission dynamics within a parameterized optimization scope. Analyzing reported cases from January 28, 2024, to September 29, 2024, demonstrates the model's effectiveness in capturing nonlinear trends, with a resurgence of COVID-19 cases in the latter half of the study period. This emphasizes the need for adaptive public health strategies in response to fluctuating infection rates, highlighting the significance of advanced mathematical modeling in infectious disease management.

Source: ZANCO Journal of Pure and Applied Sciences

DOI: 10.21271/zjpas.37.3.7

Authors: Basim A. Hassan, Ibrahim Sulaiman Mohammed, Alaa Luqman Ibrahim, Faisal Falah Aiwa

Institutions: University of Mosul, Sohar University, Universiti Utara Malaysia, University of Zakho

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4411828332

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