Automating Symbolic Partial Differentiation on Multivariate Polynomials

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-31
DOI
https://doi.org/10.5281/zenodo.18411888
Primary Topic
Polynomial and algebraic computation
Type
article
Field-Weighted Citation Impact
0.00
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article

Automating Symbolic Partial Differentiation on Multivariate Polynomials

Thomas Musora, Henry Samambgwa
Zenodo (CERN European Organization for Nuclear Research)
Polynomial and algebraic computation
article

Automating Symbolic Partial Differentiation on Multivariate Polynomials

Thomas Musora, Henry Samambgwa
article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Chinhoyi University of Technology (ZW)
Openalex Percentile: Top 24%
Polynomial and algebraic computation
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