## Use Eigen together with KLU

mhuck
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### Use Eigen together with KLU

Mon Jul 08, 2013 12:08 pm
Hello,

in my simulation I need a large (and sparse) electrical equivalent circuit. At the Moment I am using the Eigen library for Managing the matrixes and solving the equations Systems.
However I found that for the case of a unsymmetrical Matrix KLU is much faster than SuperLU. Since Eigen works well I want to keep using it.
Can I use the Eigen::SparseMatrx class with KLU?

My guess is yes, since you can bring the SparseMatrix Class in the compressed-column form. The question is what are the needed Arrays for KLU (I refer to the KLU User Guide chapter 5.4 ):
1. n: size of the Matrix this one is clear
2. Ap this may be OuterStarts
3. Ai this may be InnerIndices
4. Ax this may be Values
For the Ap,Ai,Ax I am not sure which SparseMatrix methods I need to use. I think it should be outerIndexPtr, innerIndexPtr and valuePtr, but I am really unsure.
I wont answer until 21.07, but be free to discuss
ggael
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### Re: Use Eigen together with KLU

Mon Jul 08, 2013 6:29 pm
You guessed right, and you can find similar example in the code of the CholmodSupport and SPQRSupport modules. I'd also be curious to know how does BICGSTAB<MatrixXd,IncompleteLUT<double> > would perform on such problems.
mhuck
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### Re: Use Eigen together with KLU

Tue Jul 09, 2013 6:50 am
Ok,

thanks. I will try that when I am back.
mhuck
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### Re: Use Eigen together with KLU

Wed Jul 24, 2013 1:29 pm
I'm running in some trouble with KLU.
I try to do the sample in the KLU Userguide p18 with a Eigen Matrix (the sample itself with C Arrays works fine)
my code:
Code: Select all
`int main (void){klu_symbolic *Symbolic ;klu_numeric *Numeric ;klu_common Common ;int i ;klu_defaults (&Common) ;test.resize(5,5);test.reserve(12);test.insert(0,0)=2;test.insert(0,1)=3;test.insert(1,0)=3;test.insert(1,2)=4;test.insert(1,4)=6;test.insert(2,1)=-1;test.insert(2,2)=-3;test.insert(2,3)=2;test.insert(3,2)=1;test.insert(4,1)=4;test.insert(4,2)=2;test.insert(4,4)=1;test.finalize();test.makeCompressed();std::cout<<test;Symbolic = klu_analyze (test.size(), test.outerIndexPtr(), test.innerIndexPtr(), &Common) ;Numeric = klu_factor (test.outerIndexPtr(), test.innerIndexPtr(), test.valuePtr(), Symbolic, &Common) ;klu_solve (Symbolic, Numeric, 5, 1, b, &Common) ;klu_free_symbolic (&Symbolic, &Common) ;klu_free_numeric (&Numeric, &Common) ;for (i = 0 ; i < n ; i++) printf ("x [%d] = %g\n", i, b [i]) ;return (0) ;}`

where test is a Eigen::SparseMatrix<double>.

The Output ist:
Code: Select all
`Nonzero entries:(2,0) (3,1) (3,0) (-1,2) (4,4) (4,1) (-3,2) (1,3) (2,4) (2,2) (6,1) (1,4) Outer pointers:0 2 5 9 10  \$2 3 0 0 0 3 0 4 0 6 0 -1 -3 2 0 0 0 1 0 0 0 4 2 0 1  `

Then it crashes in the klu_analyze function.
What I have noticed ist that in the KLU sample there is an additional outerIndex : 12 , is there a reason why it isnt there in Eigen ?
Dee33
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### Re: Use Eigen together with KLU

Wed Jul 24, 2013 1:49 pm
Everything seems normal in your output. We don't just output the last term (which is the number of nonzeros). But you can find it in the array test.outerIndexPtr()
mhuck
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### Re: Use Eigen together with KLU

Wed Jul 24, 2013 2:16 pm
I found my error: test.size is the number of elements but I need the number of columns /rows.

Sorry and thanks.
ggael
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### Re: Use Eigen together with KLU

Wed Jul 24, 2013 2:45 pm
Feel free to share your KLU wrapper once it's ready! I'm sure it will be useful to many people.
mhuck
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### Re: Use Eigen together with KLU

Fri Aug 09, 2013 1:20 pm
Ok here is my wrapper. It is not sophisticated, because I have no time to test it with various templates.
At the current state it works only for x,b as vector (in Ax=b) but I think you can expand it to matrices if you Change the "1" in :
Code: Select all
`m_ok = klu_solve(m_symbolic_klu, m_nummeric_klu, m_MatSize, 1, _RhS.data(), &m_common_klu);`

I hope it helps some one.
Code: Select all
`/* * KLUWrapper.hpp * *  Edited on: Aug 9, 2013 *      Author: Moritz Huck */#ifndef KLUWRAPPER_HPP_#define KLUWRAPPER_HPP_#include  "Eigen/src/Core/util/Meta.h"#include "klu.h"namespace Eigen {template<typename _valuetype>class KLU_Wrapper: public Eigen::internal::noncopyable {public:   typedef Eigen::SparseMatrix<_valuetype> _SparseMatrix;   void analyzePattern( _SparseMatrix Matrix){      eigen_assert(Matrix.isCompressed() && "call makeCompressed() on the Matrix first!");      m_MatSize=Matrix.innerSize();      printf("NodesSize Solver = %i",m_MatSize);      analyze_symbolic(m_MatSize,Matrix);      m_analised_Pattern=true;   }   void factorize(_SparseMatrix Matrix){      eigen_assert(Matrix.isCompressed() && "call makeCompressed() on the Matrix first!");      eigen_assert(m_analised_Pattern && "You must first call analyzePattern()");      if(m_is_factored){refactor_nummeric(Matrix);}      else{factor_nummeric(Matrix);}      m_factorized=true;   }   Eigen::VectorXd solve(Eigen::VectorXd _RhS){      eigen_assert(m_factorized && "You must first call analyzePattern() and factorize()" );      m_ok = klu_solve(m_symbolic_klu, m_nummeric_klu, m_MatSize, 1, _RhS.data(), &m_common_klu);            if (m_ok == 0) throw std::runtime_error("klu_solvefehlgeschlagen");            return _RhS;   }   KLU_Wrapper():m_symbolic_is_done(false),m_is_factored(false),m_analised_Pattern(false),m_factorized(false),m_ok(1)   {      m_ok = klu_defaults(&m_common_klu);      if (m_ok == 0) throw std::runtime_error("klu_defaults failed");   }   virtual ~KLU_Wrapper(){      klu_free_symbolic(&m_symbolic_klu,&m_common_klu);      klu_free_numeric(&m_nummeric_klu,&m_common_klu);   }protected:            klu_numeric* m_nummeric_klu;            klu_symbolic* m_symbolic_klu;            klu_common m_common_klu;            int m_ok;            int m_MatSize;            bool m_symbolic_is_done;            bool m_is_factored;            bool m_analised_Pattern;            bool m_factorized;            void factor_nummeric( _SparseMatrix Matrix){                  m_nummeric_klu = klu_factor(Matrix.outerIndexPtr(), Matrix.innerIndexPtr(), Matrix.valuePtr(),                           m_symbolic_klu, &m_common_klu);                     if (m_nummeric_klu == NULL) {throw std::runtime_error("klu_factor failed");}                     else {m_is_factored=true;} //::TODO:: write real exception               }            void analyze_symbolic(int MatSize, _SparseMatrix Matrix){                  m_symbolic_klu = klu_analyze(MatSize, Matrix.outerIndexPtr(), Matrix.innerIndexPtr(), &m_common_klu);                  if (m_symbolic_klu == NULL) {                        std::cout << m_symbolic_klu << std::endl;                        std::cout << m_common_klu.status << std::endl;                        throw std::runtime_error("klu_analyze failed");                     } //::TODO:: write real exception                  else{m_symbolic_is_done=true;}               }            void refactor_nummeric(_SparseMatrix Matrix){               m_ok = klu_refactor( Matrix.outerIndexPtr(),  Matrix.innerIndexPtr(),  Matrix.valuePtr(), m_symbolic_klu,                           m_nummeric_klu, &m_common_klu);                     if (m_ok == 0) throw std::runtime_error("klu_refactor failed");                     //::TODO:: write real exception            }};} /* namespace Eigen */#endif /* KLUWRAPPER_HPP_ */`

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