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Corollary 1.4 (Polar decomposition by singular value decomposition).
Let the matrix J R
2 × 2 have the singular value decomposition J = UΣV , where the matrices
U
2 × 2 are orthonormal (unitary), i.e. U U=I 2 arid V V=I 2 ,andwhere
Σ=diag( σ 1 2 ) in descending order σ 1
R
2 × 2 and V
R
σ 2
0 is the diagonal matrix of singular values
. If J has the polar decomposition J = RS, then R = UV and S = VΣV (J) and σ (J)
denote, respectively, the set of eigenvalues and the set of singular values of J. Then
{
σ 1 2 }
the left eigenspace is spanned by the left eigencolumns u 1 and u 2 which are generated by
(JJ − λ i I 2 ) u i =(JJ − σ i I 2 ) u i = 0 , || u 1 || = || u 2 || = 1;
(1.28)
the right eigenspace is spanned by the right eigencolumns v 1 and v 2 generated by
(J J
λ j I 2 ) v j =(J J
σ j I 2 ) v j = 0 ,
||
v 1
||
=
||
v 2
||
= 1;
(1.29)
the characteristic equation of the eigenvalues is determined by
JJ
J J
|
λ I 2 |
=0or
|
λ I 2 |
=0 ,
(1.30)
which leads to λ 2
λI + II = 0, with the invariants
I := tr [JJ ]=tr[J J] , I := (det [J]) 2 = det [JJ ]=det[J J] ,
(1.31)
2 I + I 2
4 II 2 = σ 2 = 1
2 I
4 II ;
I 2
λ 1 = σ 1 = 1
the matrices S and R can be expressed as
S=(J J) 1 / 2 =( v 1 , v 2 )diag( σ 1 2 )( v 1 , v 2 ) , R=JS 1 =( u 1 , u 2 )( v 1 , v 2 );
(1.32)
J is normal if and only if RS = SR.
End of Corollary.
More details about the polar decomposition related to the singular value decomposition can be
found in the classical text by Higham ( 1986 ),
Kenney and Laub ( 1991 ), and Ting ( 1985 ). Example 1.4 is a numerical example for singular value
decomposition and polar decomposition.
Example 1.4 (Singular value decomposition, polar decomposition).
Let there be given the Jacobi matrix J and the product matrices JJ and J J such that the left
and right characteristic equations of eigenvalues read
J= 52
, JJ = 29 9
, J J= 26 3
,
(1.33)
17
950
353
JJ
J J
|
λ I 2 |
=
|
λ I 2 |
=
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