Solving geometric quadratic stochastic operators using C++

In this research paper, an investigation is carried out to analyze the performance characteristics of C++ and Maple programming in the context of solving specific classes of Geometric Quadratic Stochastic Operators (GQSOs). The primary objective is to discern and evaluate the computational efficienc...

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Bibliographic Details
Main Author: Lim, Yik Heng
Format: Final Year Project / Dissertation / Thesis
Published: 2024
Subjects:
Online Access:http://eprints.utar.edu.my/6660/1/fyp_CS_2024_LYH.pdf
http://eprints.utar.edu.my/6660/
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Summary:In this research paper, an investigation is carried out to analyze the performance characteristics of C++ and Maple programming in the context of solving specific classes of Geometric Quadratic Stochastic Operators (GQSOs). The primary objective is to discern and evaluate the computational efficiency and reliability of these programming for addressing GQSOs, thereby offering valuable insights into their respective strengths and limitations. To meet this objective, a specific C++ program is designed to handle some classes of GQSOs that had chosen which are the ones solved earlier using the MAPLE programming. We then compare the results from our C++ program with the results from the Maple environment. C++ is chosen because it is less advance compared to Maple, which is a language that may confuse researcher or mathematician who lack of knowledge in programming, and Maple may take a long period of time to solve GQSO and obtain the result. In terms of the methodology, Visual Studio 2019 is used to create a tailored C++ program designed to effectively tackle the challenges posed by GQSOs. The results we obtained were stored into structured text files. Then, Gnuplot use the data as input to plot graphs for further analysis. This paper's contribution is introducing a specialized C++ program that offers convenience and efficiency to mathematicians and researchers in the field. This program serves as an accessible alternative to using MAPLE programming, making it viable for a wider audience, even those less experienced in programming. In conclusion, this research provides a well-structured analysis comparing C++ and MAPLE programming in solving various classes of GQSOs as well as a functional C++ program. Keywords: C++, Maple, quadratic stochastic operators, Geometric distribution.