I have a matrix A with real values that I wish to decompose into a proper orthogonal matrix R and a symmetric matrix U.(adsbygoogle = window.adsbygoogle || []).push({});

Now the matrix A is obtained from a process such that this decomposition isusuallypossible, and U turns out to be also positive definite.

The problem arises when I get a matrix that is "close" to an identity matrix, i.e, its components differ from the identity matrix by random values on the order 1e-3.

Here, sometimes I get non-numeric (NaN) values when I attempt to do the decomposition. I can disregard these 'bad' matrices altogether, but I do not know how to check for such a 'bad' matrix.

Is there a condition on A such that A=RU is possible for real-valued matrices? I can guess this question reduces to the existence of matrix square root for A^{T}A, but not sure of how to proceed further...

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# Condition for existence of polar decomposition for a real matrix?

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