Range Space and Similarity Transformation Using Symbolic Mathematics in Python

Linear systems involved in engineering and scientific calculations can be more easily analyzed using similarity transformation. However, understanding the numerous abstract linear algebra theorems associated with this transformation can be challenging. This paper proposes a systematic approach to or...

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Veröffentlicht in:	IEEE access Jg. 12; S. 182788 - 182798
Hauptverfasser:	Wisassakwichai, Chainarong, Phaochoo, Prasert
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applications programs Canonical forms Covariance matrices Eigenvalues Eigenvalues and eigenfunctions Eigenvectors Electrical engineering Linear algebra Linear systems Mathematical analysis Mathematical models Mobile handsets Modules Null space Object oriented programming Principal component analysis Programming profession Python Similarity Similarity transformation Source code symbolic mathematics SymPy Theorems Transformations (mathematics) Vector spaces Vectors
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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