Thank you very much.
> El 05/04/2011, a las 05:07, Gong Ding escribi?:
>
>
>
> > Hi,
>
> > I use slepc eigen value solver to evaluate the eigen values of jacobian
> > matrix on each nonlinear iteration.
>
> >
>
> > First, I create the EPS structure and set the operator to jacobian matrix.
> > And then call the EPSSolve in the SNES build
>
> > jacobian matrix functuion. This method create EPS once, call EPSSolve many
> > times. However, it seems EPSSolve only work at
>
> > first time, and result eigen value never changes (however, relative error
> > becomes larger and larger) in the following solve procedure.
>
> >
>
> > Then I create EPS each time in the SNES build jacobian matrix functuion, do
> > EPSSolve and delete EPS at the end of function.
>
> > This method gives eigen value for the jacobian matrix with small relative
> > error ~1e-8. Of course, create and destroy the EPS solver each time
>
> > is not efficient.
>
> >
>
> > Does something get wrong in the first method?
>
>
>
> You will always solve the same eigenproblem, unless you call EPSSetOperators
> every time the matrix changes.
>
> When EPSSetOperators is called, the EPS object is reset and therefore
> EPSSetUp will be called in the next EPSSolve. So basically the first approach
> will have the same cost as the second approach.
>
>
>
> By the way, you are not using STSINVERT correctly. You should set
> EPS_TARGET_MAGNITUDE (instead of EPS_SMALLEST_MAGNITUDE) together with
> target=0.0 with EPSSetTarget.
>
>
>
> Jose
>
>
>
> >
>
> > The code I used is attached here
>
> >
>
> > // create the EPS solver for smallest and largest eigen value
>
> > EPS eps_s;
>
> > EPS eps_l;
>
> >
>
> > FVM_NonlinearSolver & nonlinear_solver = dynamic_cast<FVM_NonlinearSolver
> > &>(_solver);
>
> > Mat & J = nonlinear_solver.jacobian_matrix();
>
> >
>
> > // create eigen value problem solver
>
> > EPSCreate(PETSC_COMM_WORLD, &eps_s);
>
> > EPSCreate(PETSC_COMM_WORLD, &eps_l);
>
> > // Set operator
>
> > EPSSetOperators(eps_s, J, PETSC_NULL);
>
> > EPSSetOperators(eps_l, J, PETSC_NULL);
>
> >
>
> > // calculate smallest and largest eigen value
>
> > EPSSetWhichEigenpairs(eps_s, EPS_SMALLEST_MAGNITUDE);
>
> > EPSSetWhichEigenpairs(eps_l, EPS_LARGEST_MAGNITUDE);
>
> >
>
> > // shift-and-invert spectral transformation to enhance convergence of
> > eigenvalues near zero
>
> > ST st_s;
>
> > EPSGetST(eps_s, &st_s);
>
> > STSetType(st_s, STSINVERT);
>
> >
>
> >
>
> > // Set solver parameters at runtime
>
> > EPSSetFromOptions(eps_s);
>
> > EPSSetFromOptions(eps_l);
>
> >
>
> >
>
> > ///////////////////////////////////////////////////////////////////////////////
>
> >
>
> > // this part is called after jacobian matrix assemly
>
> >
>
> > PetscScalar kr_s, ki_s;
>
> > PetscScalar kr_l, ki_l;
>
> > PetscReal error_s;
>
> > PetscReal error_l;
>
> > PetscInt nconv_s;
>
> > PetscInt nconv_l;
>
> >
>
> > // get the smallest eigen value
>
> > EPSSolve( eps_s );
>
> > EPSGetConverged( eps_s, &nconv_s );
>
> > if( nconv_s > 0 )
>
> > {
>
> > EPSGetEigenvalue( eps_s, 0, &kr_s, &ki_s );
>
> > EPSComputeRelativeError( eps_s, 0, &error_s );
>
> > }
>
> >
>
> > // get the largest eigen value
>
> > EPSSolve( eps_l );
>
> > EPSGetConverged( eps_l, &nconv_l );
>
> > if( nconv_l > 0 )
>
> > {
>
> > EPSGetEigenvalue( eps_l, 0, &kr_l, &ki_l );
>
> > EPSComputeRelativeError( eps_l, 0, &error_l );
>
> > }
>
> >
>
> >
>
>
>
>
