Re: [eigen] mixed-precision Cholesky?

[Manoj Rajagopalan <rmanoj@xxxxxxxxx>]

I am using the default branch. I just pulled the entire eigen repo into a 
clean directory, performed 'hg branch default' followed by a make install - 
same error list indicating an invalid attempt to access a pointer at this 
line, for a CwiseUnaryOp<ei_scalar_cast_op<...> > expression.

Shall I still try the experiment you suggested?

-- Manoj


On Monday 12 July 2010 10:50:09 am Gael Guennebaud wrote:
> oh, I see you are using the 2.0 branch... indeed in this very special
> case calling the high level routines would probably work though it
> will skip the efficient matrix-vector implementation.
>
> For instance, try to replace the line containing the
> ei_cache_friendly_product_colmajor_times_vector by what is commented
> below this line, something like:
>
> // 				if (IsLowerTriangular)
> //           other.col(c).end(size-endBlock) += (lhs.block(endBlock,
> startBlock, size-endBlock, endBlock-startBlock)
> //                                           *
> other.col(c).block(startBlock,endBlock-startBlock)).lazy();
> // 				else
> //           other.col(c).end(size-endBlock) += (lhs.block(endBlock,
> startBlock, size-endBlock, endBlock-startBlock)
> //                                           *
> other.col(c).block(startBlock,endBlock-startBlock)).lazy();
>
>
> Beside I'm currently working on a true support for mixing real and
> complex with vectorization and optimal performance. See the other
> threads on that topic. So this should work out of the box soon.
>
> gael
>
> On Mon, Jul 12, 2010 at 3:56 PM, Manoj Rajagopalan <rmanoj@xxxxxxxxx> wrote:
> > On Thursday 10 June 2010 05:26:30 am Gael Guennebaud wrote:
> >> On Thu, Jun 10, 2010 at 1:37 AM, Manoj Rajagopalan <rmanoj@xxxxxxxxx> 
wrote:
> >> > 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_vecto
> >> >r)
> >> >
> >> >  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?
> >>
> >> The idea is rather to allow mixing types where it makes sense, as for
> >> products, triangular solves and the like, but I don't know when this
> >> will be available.
> >>
> >> gael
> >
> > Will replacing the call to ei_cache_friendly_product-*() with a
> > high-level Eigen GEMM routine be a simple fix for the problem? When I
> > call
> > solveInPlace() on
> >   real_matrix.cast<complex_type>().selfadjointView<Lower>().ldlt(),
> > this is the point where I get a compile error since
> > ei_cache_friendly_product*() expect each operand to have a valid
> > coeffRef() which doesn't make sense for a type-cast matrix.
> >
> > I wanted to try this myself but I got lost in the template branches that
> > specialize and optimize :-(
> >
> > thanks,
> > Manoj