[jira] [Resolved] (MAHOUT-792) Add new stochastic decomposition code

["Ted Dunning (Resolved) (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-792?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ted Dunning resolved MAHOUT-792.
--------------------------------

       Resolution: Fixed
    Fix Version/s: 0.6
         Assignee: Ted Dunning

Finally committed these.  This provides Cholesky decomposition and several in-memory implementations of SSVD.  These are deficient with respect to power iterations.
                
> 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
>            Assignee: Ted Dunning
>             Fix For: 0.6
>
>         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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