Date: 2015-09-03T15:06:08+00:00

Actual source code: ex23.c
slepc-3.6.1 2015-09-03
Report Typos and Errors

      1: /*
      2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
      4:    Copyright (c) 2002-2015, Universitat Politecnica de Valencia, Spain

      6:    This file is part of SLEPc.

      8:    SLEPc is free software: you can redistribute it and/or modify it under  the
      9:    terms of version 3 of the GNU Lesser General Public License as published by
     10:    the Free Software Foundation.

     12:    SLEPc  is  distributed in the hope that it will be useful, but WITHOUT  ANY
     13:    WARRANTY;  without even the implied warranty of MERCHANTABILITY or  FITNESS
     14:    FOR  A  PARTICULAR PURPOSE. See the GNU Lesser General Public  License  for
     15:    more details.

     17:    You  should have received a copy of the GNU Lesser General  Public  License
     18:    along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
     19:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     20: */

     22: static char help[] = "Computes exp(A)*v for a matrix associated with a Markov model.\n\n"
     23:   "The command line options are:\n"
     24:   "  -m <m>, where <m> = number of grid subdivisions in each dimension.\n\n";

     26: #include <slepcmfn.h>

     28: /*
     29:    User-defined routines
     30: */
     31: PetscErrorCode MatMarkovModel(PetscInt m,Mat A);

     35: int main(int argc,char **argv)
     36: {
     37:   Mat                A;           /* problem matrix */
     38:   MFN                mfn;
     39:   FN                 f;
     40:   PetscReal          tol,norm;
     41:   PetscScalar        t=2.0;
     42:   Vec                v,y;
     43:   PetscInt           N,m=15,ncv,maxit,its;
     44:   PetscErrorCode     ierr;
     45:   PetscBool          draw_sol;
     46:   MFNConvergedReason reason;

     48:   SlepcInitialize(&argc,&argv,(char*)0,help);

     50:   PetscOptionsGetInt(NULL,"-m",&m,NULL);
     51:   PetscOptionsGetScalar(NULL,"-t",&t,NULL);
     52:   N = m*(m+1)/2;
     53:   PetscPrintf(PETSC_COMM_WORLD,"\nMarkov y=exp(t*A)*e_1, N=%D (m=%D)\n\n",N,m);

     55:   PetscOptionsHasName(PETSC_NULL,"-draw_sol",&draw_sol);

     57:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     58:             Compute the transition probability matrix, A
     59:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     61:   MatCreate(PETSC_COMM_WORLD,&A);
     62:   MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
     63:   MatSetFromOptions(A);
     64:   MatSetUp(A);
     65:   MatMarkovModel(m,A);

     67:   /* set v = e_1 */
     68:   MatCreateVecs(A,PETSC_NULL,&y);
     69:   MatCreateVecs(A,PETSC_NULL,&v);
     70:   VecSetValue(v,0,1.0,INSERT_VALUES);
     71:   VecAssemblyBegin(v);
     72:   VecAssemblyEnd(v);

     74:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     75:                 Create the solver and set various options
     76:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
     77:   /*
     78:      Create matrix function solver context
     79:   */
     80:   MFNCreate(PETSC_COMM_WORLD,&mfn);

     82:   /*
     83:      Set operator matrix, the function to compute, and other options
     84:   */
     85:   MFNSetOperator(mfn,A);
     86:   MFNGetFN(mfn,&f);
     87:   FNSetType(f,FNEXP);
     88:   FNSetScale(f,t,1.0);
     89:   MFNSetTolerances(mfn,1e-07,PETSC_DEFAULT);

     91:   /*
     92:      Set solver parameters at runtime
     93:   */
     94:   MFNSetFromOptions(mfn);

     96:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     97:                       Solve the problem, y=exp(A)*v
     98:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

    100:   MFNSolve(mfn,v,y);
    101:   MFNGetConvergedReason(mfn,&reason);
    102:   if (reason!=MFN_CONVERGED_TOL) SETERRQ(PETSC_COMM_WORLD,1,"Solver did not converge");
    103:   VecNorm(y,NORM_2,&norm);
    104:
    105:   /*
    106:      Optional: Get some information from the solver and display it
    107:   */
    108:   MFNGetIterationNumber(mfn,&its);
    109:   PetscPrintf(PETSC_COMM_WORLD," Number of iterations of the method: %D\n",its);
    110:   MFNGetDimensions(mfn,&ncv);
    111:   PetscPrintf(PETSC_COMM_WORLD," Subspace dimension: %D\n",ncv);
    112:   MFNGetTolerances(mfn,&tol,&maxit);
    113:   PetscPrintf(PETSC_COMM_WORLD," Stopping condition: tol=%.4g, maxit=%D\n",(double)tol,maxit);

    115:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    116:                     Display solution and clean up
    117:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
    118:   PetscPrintf(PETSC_COMM_WORLD," Computed vector at time t=%.4g has norm %g\n\n",(double)PetscRealPart(t),(double)norm);
    119:   if (draw_sol) {
    120:     PetscViewerDrawSetPause(PETSC_VIEWER_DRAW_WORLD,-1);
    121:     VecView(y,PETSC_VIEWER_DRAW_WORLD);
    122:   }

    124:   /*
    125:      Free work space
    126:   */
    127:   MFNDestroy(&mfn);
    128:   MatDestroy(&A);
    129:   VecDestroy(&v);
    130:   VecDestroy(&y);
    131:   SlepcFinalize();
    132:   return 0;
    133: }

    137: /*
    138:     Matrix generator for a Markov model of a random walk on a triangular grid.
    139:     See ex5.c for additional details.
    140: */
    141: PetscErrorCode MatMarkovModel(PetscInt m,Mat A)
    142: {
    143:   const PetscReal cst = 0.5/(PetscReal)(m-1);
    144:   PetscReal       pd,pu;
    145:   PetscInt        Istart,Iend,i,j,jmax,ix=0;
    146:   PetscErrorCode  ierr;

    149:   MatGetOwnershipRange(A,&Istart,&Iend);
    150:   for (i=1;i<=m;i++) {
    151:     jmax = m-i+1;
    152:     for (j=1;j<=jmax;j++) {
    153:       ix = ix + 1;
    154:       if (ix-1<Istart || ix>Iend) continue;  /* compute only owned rows */
    155:       if (j!=jmax) {
    156:         pd = cst*(PetscReal)(i+j-1);
    157:         /* north */
    158:         if (i==1) {
    159:           MatSetValue(A,ix-1,ix,2*pd,INSERT_VALUES);
    160:         } else {
    161:           MatSetValue(A,ix-1,ix,pd,INSERT_VALUES);
    162:         }
    163:         /* east */
    164:         if (j==1) {
    165:           MatSetValue(A,ix-1,ix+jmax-1,2*pd,INSERT_VALUES);
    166:         } else {
    167:           MatSetValue(A,ix-1,ix+jmax-1,pd,INSERT_VALUES);
    168:         }
    169:       }
    170:       /* south */
    171:       pu = 0.5 - cst*(PetscReal)(i+j-3);
    172:       if (j>1) {
    173:         MatSetValue(A,ix-1,ix-2,pu,INSERT_VALUES);
    174:       }
    175:       /* west */
    176:       if (i>1) {
    177:         MatSetValue(A,ix-1,ix-jmax-2,pu,INSERT_VALUES);
    178:       }
    179:     }
    180:   }
    181:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    182:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    183:   return(0);
    184: }