[eigen] mixed-precision Cholesky? |

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*To*: eigen@xxxxxxxxxxxxxxxxxxx
*Subject*: [eigen] mixed-precision Cholesky?
*From*: Manoj Rajagopalan <rmanoj@xxxxxxxxx>
*Date*: Wed, 9 Jun 2010 19:37:33 -0400
*Organization*: EECS Dept., University of Michigan, Ann Arbor, MI, USA

Hi,
I have situation where, due to memory pressure, I need to store a symmetric
matrix as real-values but it must solve a complex RHS for a complex solution
vector. Currently I am storing the matrix as complex-type so that llt() and
ldlt() can be called without compilation errors but is the above situation
realizable in Eigen?
When invoking
real_matrix.selfadjointView<Lower>().ldlt().solveInPlace(complex_vector)
I get a gcc 4.2.4 error saying:
no matching function for call
to ‘ei_cache_friendly_product_colmajor_times_vector(Eigen::DenseIndex&,
double*, Eigen::DenseIndex,
Eigen::VectorBlock<Eigen::Matrix<std::complex<double>, 33331, 1, 0, 33331,
1>, 33331>, std::complex<double>*, std::complex<double>)’
no matching function for call
to ‘ei_cache_friendly_product_rowmajor_times_vector(double*,
Eigen::DenseIndex, std::complex<double>*, Eigen::DenseIndex&,
Eigen::VectorBlock<Eigen::Matrix<std::complex<double>, 33331, 1, 0, 33331,
1>, 33331>&, std::complex<double>)’
Would it make sense to introduce a cast<> on LLT and LDLT so that such a
mixed-precision operation becomes possible?
thanks,
Manoj