Hello,
I have a matrix equation, Ax=b, that I need to solve for x. x should be a
vector of positive numbers (between 0 and 1). A is not a square matrix in
general. This lead me to using the SVD. However, using the SVD gives me
positive and negative numbers, as well. I have some constraints
If A is not square, which dimension is larger? There will most likely be
either no solution or an infinity of solutions. If the latter, I think
you are using the Moore-Penrose inverse (depends exactly how you use the
SVD), that is the shortest solution, but the SVD will give you the whole
Ripley [mailto:[EMAIL PROTECTED]
Sent: 27 July 2004 10:13
To: Molins, Jordi
Cc: '[EMAIL PROTECTED]'
Subject: Re: [R] SVD with positivity constraints
If A is not square, which dimension is larger? There will most likely be
either no solution or an infinity of solutions. If the latter, I think
you
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Subject: Re: [R] SVD with positivity constraints
If A is not square, which dimension is larger? There will most likely be
either no solution or an infinity of solutions. If the latter, I think
you are using the Moore-Penrose inverse (depends exactly how you use the
SVD
: 27 July 2004 11:33
To: '[EMAIL PROTECTED]'
Cc: 'Prof Brian Ripley'; 'Ken Knoblauch'
Subject: RE: [R] SVD with positivity constraints
Thank you to Prof Brian Ripley and to Ken Knoblauch for your fast replies.
I should explain a little bit more about the problem at hand: in principle,
the matrix
