Though I don't know if they have sparse algorithms. But they have a good base something there to help you get started making one...
On Wednesday, August 10, 2016 at 2:20:54 PM UTC-7, Chris Rackauckas wrote: > > 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. >> >>
