Abstract :

This Paper Investigates The Efficiency Of The Parametric Accelerated Over-Relaxation (PAOR) Method For Solving Large-scale Linear Systems With Partitioned Matrix Structures. An 8×8 Matrix Example Is Used To Compare PAOR Against Classical Iterative Techniques Including Jacobi, Gauss-Seidel, Successive OverRelaxation (SOR), And Accelerated OverRelaxation (AOR). Artificial Intelligence (AI) And Machine Learning (ML) Are Incorporated To Predict Convergence Trends And Estimate Error Bounds. Results Show That PAOR Achieves Faster Convergence And Improved Stability In Partitioned Systems When Optimized Parameters Are Selected Using ML Techniques.

Published:

25-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

312-314

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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