Automating Symbolic Partial Differentiation on Multivariate Polynomials

Henry Samambgwa; Thomas Musora

doi:10.30574/gjeta.2025.25.3.0363

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Automating Symbolic Partial Differentiation on Multivariate Polynomials

Henry Samambgwa ^* and Thomas Musora

Department of Mathematics and Statistics, School of Natural Sciences and Mathematics, Chinhoyi University of Technology, 78 Magamba Way, Off Chirundu Road, Chinhoyi, Zimbabwe.

Research Article

Global Journal of Engineering and Technology Advances, 2025, 25(03), 286-292.

Article DOI: 10.30574/gjeta.2025.25.3.0363

DOI url: https://doi.org/10.30574/gjeta.2025.25.3.0363

Publication history

Received on 12 November 2025; revised on 29 December 2025; accepted on 31 December 2025

Abstract

This study develops a novel implementation for symbolic partial differentiation of multivariate polynomials in MATLAB. Pseudocode, a flowchart and MATLAB source code are developed. The program is then tested for samples of multivariate polynomials and partial differentiation operations. It is demonstrated that the program generates results with 100% accuracy. Applications of the developed program are unlimited, with potential uses in economic, engineering and scientific neural network and computer-based model and systems.

Keywords

Symbolic Partial Differentiation; Multivariate Polynomial; MATLAB Source Code; Algorithm

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How to cite this article

Henry Samambgwa and Thomas Musora. Automating Symbolic Partial Differentiation on Multivariate Polynomials. Global Journal of Engineering and Technology Advances, 2025, 25(3), 286-292. Article DOI: https://doi.org/10.30574/gjeta.2025.25.3.0363

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