Новий підхід до пошуку власних векторів для кратних власних чисел матриці

An efficient method of calculating eigenvectors for multiple eigenvalues of a matrix is proposed. This method is based on a formalized transformation of the problem of solving degenerate systems of equations into a regular problem by “repairing” their matrices and correspondingly correcting the right-hand sides of the equations, as well as “exclusion” during calculations from the spectrum eigenvalues of the matrix of one of the multiple values. In the case of non-defective multiples of the matrix, orthogonal eigenvectors are formed in contrast to the results obtained using the Mathematica program.

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Publication Details

Journal
The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
Published
2024-12-25
Primary Topic
Matrix Theory and Algorithms
Type
article
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article

Новий підхід до пошуку власних векторів для кратних власних чисел матриці

The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
Matrix Theory and Algorithms
article

Новий підхід до пошуку власних векторів для кратних власних чисел матриці

article en

Abstract

An efficient method of calculating eigenvectors for multiple eigenvalues of a matrix is proposed. This method is based on a formalized transformation of the problem of solving degenerate systems of equations into a regular problem by “repairing” their matrices and correspondingly correcting the right-hand sides of the equations, as well as “exclusion” during calculations from the spectrum eigenvalues of the matrix of one of the multiple values. In the case of non-defective multiples of the matrix, orthogonal eigenvectors are formed in contrast to the results obtained using the Mathematica program.

The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA)
Reduced inequalities
Openalex Percentile: Top 49%
Matrix Theory and Algorithms
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