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We know that a positive definite can be done for Cholesky decomposition,but I want to know that how a positive semi-definite be done for Cholesky decomposition?The following sentences come from a paper. "There are two assumptions on the specified correlation matrix R. The first is a general assumption that R is a possible correlation matrix, i.e. that it is a symmetric positive semidefinite matrix with 1’s on the main diagonal. While implementing the algorithm there is no need to check positive semi-definiteness directly, as we do a Cholesky decomposition of the matrix R at the very start. If R is not positive semi-definite, the Cholesky decomposition will fail." Thank you for your answer.

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THANK YOU .but here I want to know if a positive semi-definite can be done for Cholesky decomposition? and how? –  Purple Jan 20 at 10:39
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"that how a positive semi-definite be done for Cholesky decomposition"??? What do you mean? English, please... –  Dima Pasechnik Jan 20 at 11:28
    
excuse for my bad english."There are two assumptions on the specified correlation matrix R. The first is a general assumption that R is a possible correlation matrix, i.e. that it is a symmetric positive semidefinite matrix with 1’s on the main diagonal. While implementing the algorithm there is no need to check positive semi-definiteness directly, as we do a Cholesky decomposition of the matrix R at the very start. If R is not positive semi-definite, the Cholesky decomposition will fail." –  Purple Jan 20 at 11:46
    
Do you mean to ask why R has a Cholesky decomposition if and only if R is positive semidefinite? –  Dima Pasechnik Jan 21 at 15:55
    
It is no doubt that R has a Cholesky decomposition when R is a positive definite matrix.I want to ask Whether R has a Cholesky decomposition when R is a positive semi-definite?Thank you for your patience~~ –  Purple Jan 22 at 2:41

1 Answer 1

You can either:

  • use a LDL^T decomposition (see e.g. here)

  • deflate the kernel yourself before: that is, compute a basis $Q_2$ for the kernel, complete it to a square orthonormal matrix $Q=[Q_1 \, Q_2]$, and assemble $$ Q^TRQ=\begin{bmatrix}R_{11} & 0\\ 0 & 0\end{bmatrix}, $$ where $R_{11}$ is going to be nonsingular (and hence can be Cholesky-factored).

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