Re: [eigen] Allocation policy of eigen decompositions

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2010/3/5 Márton Danóczy <marton78@xxxxxxxxx>:
> Dear Adolfo,
> and while you're at it - it would be also useful to have the
> decompositions (or, at least in my case, HouseholderQR) work
> optionally in-place. Internally, HouseholderQR copies its argument
> into its member m_qr of the same type and performs the decomposition
> on that, so it would be easy to expose this behaviour as an option, at
> least for decompositions without pivoting. Benoit, you being the
> master of the decompositions API, what do you think?

Indeed that would be a great addition. Fortunately, here again, it
seems to me that that can be done at any later date without impacting
the existing API.

Possible approach: add a computeInPlace() method, where you put 99% of
the code of the existing compute() method, and rewrite compute() to
just copy the matrix to a temporary and call computeInPlace().

This is just adding API, doesn't require changing existing API. So
again --- patches welcome, not my own priority :)


> Marton
> 2010/3/5 Adolfo Rodríguez Tsouroukdissian <dofo79@xxxxxxxxx>:
>> Hello,
>> I have a question on what the allocation policy of the eigen 3.x matrix
>> decompositions will be, for this will strongly bias how I use eigen on
>> different parts of my code.
>> On the one hand, I know that the template parameters of the matrix to be
>> decomposed (size, options, max size, ...) will be propagated to all the
>> decomposition's members and temporaries [1], so if the input matrix is
>> stack-allocated, so will be the internals of a decomposition. So far so
>> good.
>> On the other hand, I had the expectation that if heap-allocated matrices are
>> used, all heap allocations of the decomposition algorithm would take place
>> during the initialization phase, and none would be done in the computation
>> phase (provided that the initialization and computation matrices have the
>> same size in memory). This currently does not seem to be the case. For
>> example,
>> MatrixXd I(MatrixXd::Identity(16, 16));
>> MatrixXd A(MatrixXd::Random(16, 16));   // Same size as I
>> Eigen::FullPivLU<MatrixXd> lu(I);       // Allocates 5 temps
>> lu.compute(A);                          // Allocates 2 extra temps
>> Eigen::JacobiSVD<MatrixXd> svd(I);      // Allocates 4 temps
>> svd.compute(A);                         // Allocates 1 extra temp
>> I haven't checked where the extra allocations in compute() come from, but my
>> question is, can and will they be prevented for 3.x? or is this an
>> intentional design decision? (and if so, for what reasons).
>> TIA,
>> Adolfo
>> [1]
>> --
>> Adolfo Rodríguez Tsouroukdissian, Ph. D.
>> Robotics engineer
>> Tel. +34.93.414.53.47
>> Fax.+
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