METHOD FOR CALCULATING MATRIX RANK USING MODERN PROGRAMMING LANGUAGES
Authors/Creators
Description
This article focuses on an interactive application developed using
Python and the Flask web framework for calculating the rank of a matrix. Matrix rank,
a fundamental concept in linear algebra, represents the degree of linear independence
of a matrix's rows or columns and is widely applied in areas such as linear systems,
data compression, machine learning, and scientific research. The application enables
users to input the dimensions and elements of a matrix, computes its rank using the
NumPy library, and displays the result in a user-friendly interface. This tool is
particularly beneficial in educational contexts for demonstrating linear algebra
principles, as well as in research and practical applications that require efficient
mathematical analysis. The article thoroughly explores the application's functionalities,
technical structure, mathematical foundation, and practical significance, showcasing
its role as a reliable and interactive computational resource for modern technological
advancements.
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299-304 Jumaboyeva Zevarjon.pdf
Additional details
References
- 1. Gilbert Strang, Linear Algebra and Its Applications, 4th Edition, Brooks Cole, 2005.. 2. Refethen, Lloyd N., and David Bau III, Numerical Linear Algebra, SIAM, 1997. 3. NumPy Documentation: https://numpy.org/doc/ 4. MATLAB Documentation: https://www.mathworks.com/help/matlab/ 5. Julia Linear Algebra Documentation: https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/ 6. Horn, Roger A., and Charles R. Johnson, Matrix Analysis, Cambridge University Press, 2012. 7. Golub, Gene H., and Charles F. Van Loan, Matrix Computations, Johns Hopkins University Press, 2013.