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I am implementing a Monte-Carlo method that requires decomposition of a $k \times k$ covariance matrix $\Sigma=A^TA$ where the dimensionality of $A$ should also be $k\times k$. No further constraints are on the properties of $A$ except for $A\in \mathbb R$.

At this moment, $\Sigma$ is unknown but I have a $n\times k$ ($n\gg k$) data matrix $X$ (column means 0) whose column-wise covariances equal $\Sigma$. I am seeking a method that would give me an $A$ without computing $\Sigma$.

One option is to decompose $X=U\Sigma^\prime V^T$ via singular value decomposition (SVD) and let $A$ be the first $k$ rows of $\Sigma^\prime V^T$.

Is there any computationally faster method than SVD to generate an $A$?

Thank you!

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    $\begingroup$ The limitation not to compute $\Sigma$ looks artificial in this context, because the calculation of $\Sigma$ already is much faster than the calculation of the SVD of $X.$ Moreoever, if you do (miraculously) find an even more efficient way to obtain $A,$ then obtaining $\Sigma$ afterwards is nearly instantaneous, indicating you're not going to avoid doing at least as much work (asymptotically in $n$) as involved in computing $\Sigma$ anyway. $\endgroup$
    – whuber
    Commented May 31, 2019 at 20:20
  • $\begingroup$ @whuber Thank you for the comment. I have not compared the time costs but you might be right. I had this impression that the reason for PCA prefers SVD of the data matrix than eigendecomposition of the covariance matrix is because the covariances are really computationally demanding given $n\gg k$. Now after refreshing my memory I know it is also related to numerical stability and the non-negligible time for eigendecomposition itself. As for your 2nd point, in this simulation I would never need the covariance matrix $\Sigma$ $\endgroup$ Commented May 31, 2019 at 21:19
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    $\begingroup$ Understood: the idea was to show that asymptotically, because $n\gg k,$ there is nothing gained by avoiding the computation of $\Sigma.$ Thus, if the best route to computing $A$ goes through $\Sigma,$ don't hamstring yourself by precluding that option. $\endgroup$
    – whuber
    Commented May 31, 2019 at 21:43
  • $\begingroup$ @whuber Thank you for the advice. One last question, say I take the route of computing covariances to get some $A$, what is the fastest decomposition technique out there? I can do Cholesky decomposition if $\Sigma$ is also positive definite, and can retreat to eigendecomposition otherwise. Do you happen to know any faster method?:) $\endgroup$ Commented May 31, 2019 at 22:39

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