[eigen] Using LU with matrix-views and blocks

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Hi eigen-users,

   I have a requirement for performing A \ b where A is a submatrix of a 
larger matrix, created using MatrixXd::block(...). I can use A.lu().solve() 
but can't seem to be able to use FullPivLu<Block<MatrixXd> > to store the LU 
factorization of A and then use it repeatedly. Is this be design?

  I also don't seem to be able to store the LU factorization when I create a 
matrix view using Map<MatrixXd>.

    Have these features been suggested for implementation? Can they be 
considered for inclusion?

   I am writing code that needs to be high-performance so I conservatively 
pre-allocate my matrices in a constructor and later use submatrix portions 
later (whose dimensions show bounded change with each iteration). 
Alternatively, I conservatively pre-allocate a large std::vector<double> in 
my CTOR and try to use Map<MatrixXd> with the correct dynamic size. In either 
case, I need to use the LU.

   Problems mostly seem to be venial compilation issues:

1. missing enums
2. missing default CTORs for Block and Map (probably by design)
3. DenseStorageBase::_check_template_params() failure because correcting
    issue #1 above with missing enum defs fails static assert (probably
    because I don't understand Eigen well enough yet to assign correct

  Any ideas/tips?


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