Re: [R] using SVD to get an inverse matrix of covariance matrix

[Spencer Graves]

	  More presicely, if M is singular, then M%*%x = b will have multiple 
solutions only if "b" is in the subspace spanned by columns of M.

	  Example:

> M <- array(1:2, dim=c(2,2))
> (svdM <- svd(M))
$d
[1] 3.162278 0.000000
$u
           [,1]       [,2]
[1,] -0.4472136 -0.8944272
[2,] -0.8944272  0.4472136
$v
           [,1]       [,2]
[1,] -0.7071068 -0.7071068
[2,] -0.7071068  0.7071068
	  This "M" is not symmetrical and so cannot be a covariance matrix, but 
you can get the same effect with a symmetrical matrix.  I'm using this 
example because it is the simplest thing that comes to mind to 
illustrate the point.

	  By the definition of the singular value decomposition, M = svdM$u %*% 
diag(svdM$d) %*% t(svdM$v).  Since svdM$d[2] == 0,

          M%*%x = svdM$u[, 1]*svdM$d[1]*t(svdM$v[,1])%*%x
            = x1 * svdM$u[,1],
where
          x1 = svdM$d[1]*t(svdM$v[,1])%*%x
	  Thus, if b is proportional to svdM$u[,1], the system has a solution. 
 That solution will be proportional to svdM$v[,1] + x2*svdM$v[,2], for 
any arbitrary value of x2.

hope this helps.
spencer graves
Thomas Lumley wrote:
On Fri, 11 Jul 2003, ge yreyt wrote:


Dear R-users,

I have one question about using SVD to get an inverse
matrix of covariance matrix
Sometimes I met many singular values d are close to 0:
look this example


<snip>

most values of inverse matrix
will be huge. This must be not a good way. MOre
special case, if a single value is 0, then
we can not calculate inverse d based on 1/d.
Therefore, my question is how I can calculate inverse
d (that is inverse diag(d) more efficiently???


If singular values are zero the matrix doesn't have an inverse: that is,
the equation   Mx=b  will have multiple solutions for any given b.
It is possible to get a pseudoinverse, a matrix M that picks out one of
the solutions.  One way to do this is to set the diagonal to 1/d where d
is not (nearly) zero and to 0 when d is (nearly) zero. One place to find a
discussion of this is `Matrix Computations' by Golub and van Loan.
	-thomas

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