|Re: [eigen] Student contribution|
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Rather than making the SVD effort direction dependent on generalizing the Bidagonalization class, would it make sense to move in an orthogonal direction and focus the effort on decomposing square or tall matrices, at least initially? This should simplify the code, require only the UpperBidiagonalization, and decrease the scope. This improves the probability the students will get to a point of usefulness in a limited time. It is trivial to wrap a "tall SVD" to make a general purpose SVD: [u,s,v]=svd(A) <=> [v,s,u] = svd(A'). The resulting wrapper may have some inefficiencies on wide matrices due to the initial transpose, but I'd be surprised if it was much. If the students finish quickly, or if parallel development is desired; the task of Bidiagonalization could also be performed, but it would not need to be a prerequisite for the rest of the SVD.
On 05/22/2013 06:13 AM, Benoit Jacob wrote:
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