semidefinite

Posted Oct 30, 2006 at 7:13 PM | Permalink | Reply so there must be weaker conditions than positive semidefinite which still generate positive eigenvalues.

The Euro Team and the SWM Network « Climate Audit 2006

However you still seem to get positive eigenvalues – so there must be weaker conditions than positive semidefinite which still generate positive eigenvalues.

The Euro Team and the SWM Network « Climate Audit 2006

For a symmetric matrix M, eigenvalues are positive if and only if the M is positive semidefinite.

The Euro Team and the SWM Network « Climate Audit 2006

Posted Oct 30, 2006 at 2:09 PM | Permalink | Reply covariance matrices are always positive semidefinite

The Euro Team and the SWM Network « Climate Audit 2006

A principal components analysis operates on positive semidefinite matrices covariance or correlation and thus has positive eigenvalues.

The Euro Team and the SWM Network « Climate Audit 2006

My research: The NEOS Server offers SDPA-GMP for the solution of semidefinite programming problems

Planet go-oo 2009

bender, think it this way: you have a symmetric positive semidefinite matrix A.

The Euro Team and the SWM Network « Climate Audit 2006

For a non-symmetric matrix, eigenvalues are positive if the symmetric matrix 0.5*M+M’ is positive semidefinite.

The Euro Team and the SWM Network « Climate Audit 2006