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https://issues.apache.org/jira/browse/MAHOUT-792?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13091944#comment-13091944
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Ted Dunning commented on MAHOUT-792:
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{quote}
One thing that i don't understand is how you are proposing to derive this R^-1.
To do Cholesky on Y'Y, take transpose and inverse and declare it R^-1?
{quote}
This is just a notational deficiency. The CholeskyDecomposition has solveLeft
and solveRight methods that do use back or forward substitution to effectively
right or left multiply by the inverse of R (or R'). I find my current naming
to be confusing and think that it might be better to have the methods
timesLinv, timesRinv and leftTimesLinv and leftTimesRinv or some such because
that would match the actual mathematical exposition better. If you have a
good idea for naming, please suggest it.
> Add new stochastic decomposition code
> -------------------------------------
>
> Key: MAHOUT-792
> URL: https://issues.apache.org/jira/browse/MAHOUT-792
> Project: Mahout
> Issue Type: New Feature
> Reporter: Ted Dunning
> Attachments: MAHOUT-792.patch, MAHOUT-792.patch, sd-2.pdf
>
>
> I have figured out some simplification for our SSVD algorithms. This
> eliminates the QR decomposition and makes life easier.
> I will produce a patch that contains the following:
> - a CholeskyDecomposition implementation that does pivoting (and thus
> rank-revealing) or not. This should actually be useful for solution of large
> out-of-core least squares problems.
> - an in-memory SSVD implementation that should work for matrices up to
> about 1/3 of available memory.
> - an out-of-core SSVD threaded implementation that should work for very
> large matrices. It should take time about equal to the cost of reading the
> input matrix 4 times and will require working disk roughly equal to the size
> of the input.
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