from Wiktionary, Creative Commons Attribution/Share-Alike License
- adj. Describing a bilinear form, over a vector space, that is either always positive or always negative
from The Century Dictionary and Cyclopedia
- Half definite.
Posted Oct 30, 2006 at 7:13 PM | Permalink | Reply so there must be weaker conditions than positive semidefinite which still generate positive eigenvalues.
However you still seem to get positive eigenvalues – so there must be weaker conditions than positive semidefinite which still generate positive eigenvalues.
For a symmetric matrix M, eigenvalues are positive if and only if the M is positive semidefinite.
Posted Oct 30, 2006 at 2:09 PM | Permalink | Reply covariance matrices are always positive semidefinite
A principal components analysis operates on positive semidefinite matrices covariance or correlation and thus has positive eigenvalues.
My research: The NEOS Server offers SDPA-GMP for the solution of semidefinite programming problems
bender, think it this way: you have a symmetric positive semidefinite matrix A.
For a non-symmetric matrix, eigenvalues are positive if the symmetric matrix 0.5*M+M’ is positive semidefinite.
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