Difference between revisions of "Decompose"

(Test for positive definiteness, see also links)
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  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: min([[EigenDecomp]](A+[[Transpose]](A,I,J),I,J)[.Item='value'],J)>0
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  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

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