Along the same line, once you have the <br><br>vnl_symmetric_eigensystem<double> G(data);<br><br>you have got the eigenvalues. You may then compute the square root of the eigenvalues and reconstruct the matrix which gives the desired result. The same applies for calculating logarithm of a matrix.
<br><br>===<br><br><br><div><span class="gmail_quote">On 4/15/07, <b class="gmail_sendername">Luis Ibanez</b> <<a href="mailto:luis.ibanez@kitware.com">luis.ibanez@kitware.com</a>> wrote:</span><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<br>Hi Achille,<br><br>Thanks for letting us know that you found the solution<br>for computing the square root of the diagnolizable matrix.<br><br><br> Regards,<br><br><br> Luis<br><br><br>--------------------<br>
achille mangna wrote:<br>><br>><br>> sorry I wanted to say diagonalisable.<br>> i finally resolve my probleme, as my matrix is symetric and positive<br>> semi-definite , i used vnl_symetric_eigensystem (I had not seen it before)
<br>> there is function for "square_root" and "inverse_square_root"<br>><br>> vnl_matrix<double> data;<br>> vnl_symmetric_eigensystem<double> G(data);<br>> vnl_matrix<double> m_SphereMatrix=
G.inverse_square_root();<br>><br>> thanks for your help!<br>><br>><br>><br>_______________________________________________<br>Insight-users mailing list<br><a href="mailto:Insight-users@itk.org">Insight-users@itk.org
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