Difference between revisions of "Decompose"
(Test for positive definiteness, see also links) |
(link) |
||
Line 17: | Line 17: | ||
Function IsPosDefinite( A : Number[I,J] ; I,J : Index ) | Function IsPosDefinite( A : Number[I,J] ; I,J : Index ) | ||
Description: Returns true (1) if A is positive-definite, 0 (false) otherwise. | Description: Returns true (1) if A is positive-definite, 0 (false) otherwise. | ||
− | Definition: | + | Definition: [[Min]]([[EigenDecomp]](A+[[Transpose]](A,I,J),I,J)[.Item='value'],J)>0 |
= See Also = | = See Also = |
Revision as of 14:09, 11 October 2007
Decompose(C,I,J)
Returns the Cholesky decomposition (square root) matrix of matrix C along dimensions I and J. Matrix C must be symmetric and positive-definite. (Positive-definite means that v * C * v > 0, for all vectors v.)
Cholesky decomposition computes a lower diagonal matrix L such that L * L' = C, where L' is the transpose of L.
Testing for Positive Definiteness
To avoid an error, the following UDF can be used to test for positive definiteness:
Function IsPosDefinite( A : Number[I,J] ; I,J : Index ) Description: Returns true (1) if A is positive-definite, 0 (false) otherwise. Definition: Min(EigenDecomp(A+Transpose(A,I,J),I,J)[.Item='value'],J)>0
See Also
Comments
Enable comment auto-refresher