GenericSVD.jl <https://github.com/simonbyrne/GenericSVD.jl> has linear solver routines which work for generic number types (like BigFloat). You can use an SVD to solve the linear system. It's not as fast as other methods, but you may find this useful.
On Wednesday, August 10, 2016 at 12:47:10 PM UTC-7, Nicklas Andersen wrote: > > Hello > > I'm trying to solve a large, sparse and unsymmetrical linear system Ax = b. > For this task I'm using Julias *SparseMatrixCSC *type for the definition > of my matrices and Julias built in backslash ' \ ' operator for the > solution of the system. > I need *quadruple precision* and thus I've been trying to implement my > routine with the *BigFloat *type together with the SparseMatrixCSC type. > > To illustrate this, I give a simple example here: > set_bigfloat_precision(128); > A = speye(BigFloat, 2, 2); > b = ones(BigFloat, 2, 1); > x = A\b; > > If I do this I either get a StackOverFlow error: > ERROR: StackOverflowError: > in copy at array.jl:100 > in float at sparse/sparsematrix.jl:234 > in call at essentials.jl:57 (repeats 254 times) > > or the solver seems to run forever and never terminates. As the second > error indicates it seems like the sparse solver only accepts the normal > *float* types. > My question is then, is there a way to get quadruple precision with the > standard solvers in Julia, in this case UMFpack I assume ? or should I look > for something else (in this case any suggestions :) ) ? > > Regards Nicklas A. > >
