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I. D. Edem

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I. D. Edem is a registered researcher in their academic field.

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3 research works linked to this profile

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Research · 2026 · Ktrend - International Journal of Mathematics and Statistics (IJMS)

A Unified Recurrence Framework for Power-Series and Frobenius Solutions of Second-Order Linear ODEs

This paper presents a unified coefficient-recurrence framework for obtaining series solutions of second-order linear ordinary differential equations with variable coefficients. The standard power-series method, applicable at ordinary points, and the Frobenius method, applicable at regular singular points, are treated as special cases of a common series ansatz $y(x)=(x-x_0)^r\sum_{n=0}^{\infty}a_n(x-x_0)^n$, where $r=0$ recovers the ordinary case and $r$ is determined by an indicial equation in the singular case. General recurrence relations for analytic coefficient functions are derived, conditions under which series terminate to yield polynomial solutions are established, and resonance in the integer-difference root case is characterized. Carefully selected examples illustrate the framework, including polynomial solutions, fractional exponents, resonance with logarithmic terms, and the Bessel equation. The analysis provides a systematic structural comparison of the two methods in terms of recurrence order, termination, resonance, and computational complexity.

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Research · 2026 · Ktrend - International Journal of Computational Mathematics and Scientific Computing

Fixed-Point Iteration for Nonlinear Volterra Equations in Computational Physics: A Strictly Pseudo-Contractive Mann Framework with Application to Heat Conduction with Memory

Nonlinear Volterra integral equations (VIEs) of the second kind arise in numerous physical contexts, including nonlinear heat conduction with memory, viscoelasticity, and population dynamics. However, when the kernel satisfies only a one-sided Lipschitz condition, classical contraction-based numerical methods fail, and efficient, provably convergent solvers are lacking. In this paper, we develop a fully discrete, computationally efficient numerical scheme for such equations. The method combines an implicit Euler time discretisation with an inexact Mann inner solver that requires no Jacobian evaluations, making it ideally suited for large-scale parallel computations. We establish global convergence of the adaptive, fully discrete scheme under realistic smoothness assumptions, and introduce a novel adaptive time-stepping strategy based on the Mann residual. The performance of the method is demonstrated on a physically motivated model of one-dimensional (1D) nonlinear heat conduction with memory. Extensive numerical comparisons against Picard iteration and Newton’s method show that the Mann-based solver is robust for large time steps, scales efficiently to high-resolution spatial discretisations, and maintains linear convergence rates as predicted by theory. Our open-source implementation provides a practical, ready-to-use tool for the computational physics community, and the framework is easily extended to partial integro-differential equations (PIDEs) and fractional-order memory kernels.

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Volterra integral equation; strictly pseudo-contractive mapping; Mann iteration; one-sided Lipschitz condition; Banach space; numerical analysis; method of lines. · 2026 · African Journal of Mathematics, Statistics and Computer Science

Strictly Pseudo-Contractive Volterra Integral Operators in Banach Spaces: Weak and Strong Convergence of the Mann Iteration with a Numerical Analysis Extension

We study nonlinear Volterra integral equations in uniformly convex Banach spaces $L^p(\Omega)$ $(1