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Posted to dev@commons.apache.org by Bruce A Johnson <jo...@umbc.edu> on 2010/03/26 19:16:47 UTC

Math Release 2.1 SVD

The 2.1 API docs for the Singular Value Decomposition say:

The size p depends on the chosen algorithm:

for full SVD, p is n,
for compact SVD, p is the rank r of the matrix (i. e. the number of
positive singular values),
for truncated SVD p is min(r, t) where t is user-specified.


but I don't see any method or constructor to specify whether to do the
full, compact or truncated SVD.  Am I missing something, or is the code
missing something?

Bruce



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Re: Math Release 2.1 SVD

Posted by Luc Maisonobe <Lu...@free.fr>.
Bruce A Johnson a écrit :
> The 2.1 API docs for the Singular Value Decomposition say:
> 
> The size p depends on the chosen algorithm:
> 
> for full SVD, p is n,
> for compact SVD, p is the rank r of the matrix (i. e. the number of
> positive singular values),
> for truncated SVD p is min(r, t) where t is user-specified.
> 
> 
> but I don't see any method or constructor to specify whether to do the
> full, compact or truncated SVD.  Am I missing something, or is the code
> missing something?

The implementation has been completely rewritten recently, but it seems
some comments from an intermediate version remains.
The current version only computes full SVD.

Luc

> 
> Bruce
> 
> 
> 
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Re: Math Release 2.1 SVD

Posted by Luc Maisonobe <Lu...@free.fr>.
Dimitri Pourbaix a écrit :
> Bruce,
> 
>>
>> The 2.1 API docs for the Singular Value Decomposition say:
>>
>> The size p depends on the chosen algorithm:
>>
>> for full SVD, p is n,
>> for compact SVD, p is the rank r of the matrix (i. e. the number of
>> positive singular values),
>> for truncated SVD p is min(r, t) where t is user-specified.
>>
>>
>> but I don't see any method or constructor to specify whether to do the
>> full, compact or truncated SVD.  Am I missing something, or is the code
>> missing something?
> 
> The doc is not synchronized with the source, I apologize (but the
> javadoc should be).  The code offers only one version which is none of
> the above!  From the Java file:
> 
>  The Singular Value Decomposition of matrix A is a set of three
>  matrices: U, &Sigma; and V such that
>  A = U &times; &Sigma; &times; V<sup>T</sup>. Let A be  a m &times; n
>  matrix, then U is a m &times; p orthogonal matrix, &Sigma; is a
>  p &times; p diagonal matrix with positive or null elements, V is a
>  p &times; n orthogonal matrix (hence V<sup>T</sup> is also orthogonal)
>  where p=min(m,n).

This documentation bug is now fixed in subversion (changed javadoc of
the interface to match javadoc of the implementation).

Luc

> 
> Dim.
> ----------------------------------------------------------------------------
> 
> Dimitri Pourbaix                         *
> Institut d'Astronomie et d'Astrophysique *      Don't worry, be happy
> CP 226, office 2.N4.211, building NO     *         and CARPE DIEM.
> Universite Libre de Bruxelles            *
> Boulevard du Triomphe                    *      Tel : +32-2-650.35.71
>  B-1050 Bruxelles                        *      Fax : +32-2-650.42.26
> http://sb9.astro.ulb.ac.be/~pourbaix     * mailto:pourbaix@astro.ulb.ac.be
> 
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Re: Math Release 2.1 SVD

Posted by Dimitri Pourbaix <po...@astro.ulb.ac.be>.
Bruce,

>
> The 2.1 API docs for the Singular Value Decomposition say:
>
> The size p depends on the chosen algorithm:
>
> for full SVD, p is n,
> for compact SVD, p is the rank r of the matrix (i. e. the number of
> positive singular values),
> for truncated SVD p is min(r, t) where t is user-specified.
>
>
> but I don't see any method or constructor to specify whether to do the
> full, compact or truncated SVD.  Am I missing something, or is the code
> missing something?

The doc is not synchronized with the source, I apologize (but the
javadoc should be).  The code offers only one version which is none of
the above!  From the Java file:

  The Singular Value Decomposition of matrix A is a set of three
  matrices: U, &Sigma; and V such that
  A = U &times; &Sigma; &times; V<sup>T</sup>. Let A be  a m &times; n
  matrix, then U is a m &times; p orthogonal matrix, &Sigma; is a
  p &times; p diagonal matrix with positive or null elements, V is a
  p &times; n orthogonal matrix (hence V<sup>T</sup> is also orthogonal)
  where p=min(m,n).

Dim.
----------------------------------------------------------------------------
Dimitri Pourbaix                         *
Institut d'Astronomie et d'Astrophysique *      Don't worry, be happy
CP 226, office 2.N4.211, building NO     *         and CARPE DIEM.
Universite Libre de Bruxelles            *
Boulevard du Triomphe                    *      Tel : +32-2-650.35.71
  B-1050 Bruxelles                        *      Fax : +32-2-650.42.26
http://sb9.astro.ulb.ac.be/~pourbaix     * mailto:pourbaix@astro.ulb.ac.be

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