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Statements

Subject Item
dbr:Jacobi_eigenvalue_algorithm
rdfs:label
Jacobi eigenvalue algorithm
rdfs:comment
In numerical linear algebra, the Jacobi eigenvalue algorithm is an iterative method for the calculation of the eigenvalues and eigenvectors of a real symmetric matrix (a process known as diagonalization). It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, but only became widely used in the 1950s with the advent of computers.
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dbr:Diagonal dbc:Articles_with_example_pseudocode dbr:Iterative_method dbr:Diagonalizable_matrix dbr:Carl_Gustav_Jacob_Jacobi dbr:Givens_rotation dbr:Hilbert_matrix dbr:Mathematics_of_Computation dbr:Matrix_norm dbr:Arnold_Schönhage dbr:Jacobi_rotation dbr:Real_number dbr:Symmetric_matrix dbr:Round-off_error dbr:Matrix_similarity dbc:Numerical_linear_algebra dbr:Jacobi_method_for_complex_Hermitian_matrices dbr:Eigenvalues_and_eigenvectors dbr:Numerical_linear_algebra
dbo:abstract
In numerical linear algebra, the Jacobi eigenvalue algorithm is an iterative method for the calculation of the eigenvalues and eigenvectors of a real symmetric matrix (a process known as diagonalization). It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, but only became widely used in the 1950s with the advent of computers.
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