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http://issues.apache.org/jira/browse/MATH-157?page=comments#action_12446423 ] 
            
Tyler Ward commented on MATH-157:
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Also, about testing. I've found that the best way to test this is just with 
random matrices. If you can use, say, 10,000 random matrices and always get 
back what you started with (within some small tolerance), then you'll know that 
you're doing it correctly. In order to create a symmetric matrix, just make a 
random matrix and multiply it by its own transpose, that will always be square 
symmetric, then try the above procedure, and make sure you can get back to what 
you started with. 

There are some special cases to be dealt with, but those are easily added 
later. 

> Add support for SVD.
> --------------------
>
>                 Key: MATH-157
>                 URL: http://issues.apache.org/jira/browse/MATH-157
>             Project: Commons Math
>          Issue Type: New Feature
>            Reporter: Tyler Ward
>         Attachments: svd.tar.gz
>
>
> SVD is probably the most important feature in any linear algebra package, 
> though also one of the more difficult. 
> In general, SVD is needed because very often real systems end up being 
> singular (which can be handled by QR), or nearly singular (which can't). A 
> good example is a nonlinear root finder. Often the jacobian will be nearly 
> singular, but it is VERY rare for it to be exactly singular. Consequently, LU 
> or QR produces really bad results, because they are dominated by rounding 
> error. What is needed is a way to throw out the insignificant parts of the 
> solution, and take what improvements we can get. That is what SVD provides. 
> The colt SVD algorithm has a serious infinite loop bug, caused primarily by 
> Double.NaN in the inputs, but also by underflow and overflow, which really 
> can't be prevented. 
> If worried about patents and such, SVD can be derrived from first principals 
> very easily with the acceptance of two postulates.
> 1) That an SVD always exists.
> 2) That Jacobi reduction works. 
> Both are very basic results from linear algebra, available in nearly any text 
> book. Once that's accepted, then the rest of the algorithm falls into place 
> in a very simple manner. 

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