Difference: mkl_?cootrsv and mkl_?coosv

[sunshinekid]

Hello!

I have to solve a linear system of equations A*y=x.
My matrix A is a complex general matrix stored in coo.

When I try to call mkl_zcoosv the following way

[fortran]CALL mkl_zcoosv('N',700,1._8,'g  f',mat,row,column,i,workd(ipntr(1)),workd(ipntr(2)))[/fortran]

MKL says 'MKL ERROR: Parameter 4 was incorrect on entry to MKL_ZCOOSV'

In this thread: http://software.intel.com/en-us/forums/showthread.php?t=64052&o=a&s=lr
the first answer says that "for NIST like triangular solvers, matdescra(1) must be 'T' or 'D'".

But I thought mkl_?coosv isn't a triangular solver. And if so, what is the difference to mkl_?cootrsv ?

Regards,

S.

[Victor_Gladkikh · Accepted solution]

Hi

mkl_?coosv is also triangular solver.

There is some difference between mkl_?coosv and mkl_?cootrsv/mkl_cspblas_?cootrsv.
The first is different interface of these functions.
The second is different functionality:
------------------------------------------------------------------------------------------------------------------
mkl_?cootrsv and mkl_cspblas_?cootrsv routines solve a system of linear equations with matrix-vector operations:

y = inv(A) * x or y = inv(A')*x,

where x and y are vectors, A is a sparse upper or lower triangular matrix with unit or non-unit main diagonal, A' is the transpose of A.

------------------------------------------------------------------------------------------------------------------
The mkl_?coosv routine solves a system of linear equations with matrix-vector operations:

y := alpha*inv(A)*x or y := alpha*inv(A')*x,

where alpha is scalar, x and y are vectors, A is a sparse upper or lower triangular matrix with unit or non-unit main diagonal, A' is the transpose of A.

------------------------------------------------------------------------------------------------------------------
Also mkl_?cootrsv is one-based, mkl_cspblas_?cootrsv is zero-based, and mkl_?coosv support both one-base and zero-base.

[sunshinekid]

Thank you very much!
I didn't get it from the reference manual.

Then I need another possibilty to solve my system... Maybe PARDISO or DSS... I'll see...

[Sergey_Solovev__Inte]

Hi,
the best way to solve sparse system is using PARDISO. It is flexible, has good performance and scalability. Please try to use PARDISO. If you have any questions you are welcome!

[sunshinekid]

Quoting Sergey Solovev (Intel)

Please try to use PARDISO.

I implemented PARDISO successfully, and I'm satisfied.
The performance is really good, even for small matrices of dimension 700x700.

Thank you for your efforts!

S.