Browsing by Author "Aderoju, Samuel Adewale"

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    An efficient extragradient method for solving variational inequality problems with pseudo-monotone operators
    (Vertex Academic Press, 2026-01-20) Abdullahi, Muhammed Liman; Wahab, Olalekan Taofeek; Aderoju, Samuel Adewale; Mohammed, Ghazali Nasirudeen; John Dunama; Musa, Salaudeen Alaro
    Variational Inequality Problems (VIPs) provide a strong framework for exhibiting equilibrium problems in a variety of disciplines. Global Lipschitz continuity and strong monotonicity are two restrictive assumptions that are frequently used in traditional extragradient methods for solving VIPs, which limit their applicability to solve pseudo-monotone operators. This paper introduces a novel extragradient-type technique that eliminates the need for a global Lipschitz constant. A relaxation parameter that stabilizes the iterative process by taking a convex combination of the current point and a standard projection step is one of the two main innovations included in the new technique. The second innovation is an adaptive line search strategy that dynamically modifies the step size in response to local operator behaviour. We present a thorough convergence analysis that demonstrates the resulting sequence's weak convergence to a VIP solution. The suggested algorithm is more effective and reliable than previous approaches, especially for large-scale issues with sensitive initial circumstances, as shown by numerical experiments on well-known benchmark problems such as Sun's and Kojima-Shindo problems.
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    The New Extended Exponential-Gamma (NEEG) Distribution: Properties and Applications to Infectious Disease Modelling
    (African Journal Online, 2025-08-06) Aderoju, Samuel Adewale; Salau, Ganiyat Monishola; Sanni, Bello Ishola; Adeshola, Adediran Dauda; Jimoh, Abdulazeez Kayode; Wahab, Olalekan Taofeek; Kalu, Uchechukwu
    This study introduces the New Extended Exponential-Gamma (NEEG) distribution, a flexible lifetime model developed to address the limitations of classical and generalized distributions in capturing real-world data complexity. The statistical properties of the proposed distribution are thoroughly explored, including its probability density function, cumulative distribution function, and parameter estimation via the maximum likelihood method. The practical effectiveness of the NEEG model is demonstrated using two real-life COVID- 19 datasets from Italy and Nigeria, where it is benchmarked against several existing models such as the Gamma, Exponential, UYEG, and two variants of the Generalized Lindley distribution. Model comparison was conducted using a combination of information criteria (AIC, AICc, BIC, HQIC) and graphical tools such as density plots overlaid on empirical histograms. The results consistently show that the NEEG distribution provides the best fit across both datasets, outperforming all competing models in terms of flexibility, goodness-of-fit, and alignment with the empirical data. The model’s adaptability to skewed and peaked data structures is particularly evident in pandemic-related scenarios, where traditional models often fail. These findings position the NEEG distribution as a powerful and versatile tool for statistical modelling in public health, reliability analysis, and other domains requiring robust handling of nonnormal, skewed, or heavy-tailed data. Future research may extend the model into regression frameworks or multivariate contexts to enhance its applicability further. INTRODUCTION One notable development in lifetime data modelling