Date: 2015-09-03T15:05:08+00:00

Actual source code: ex5.c
slepc-3.6.1 2015-09-03
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      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[] = "Eigenvalue problem associated with a Markov model of a random walk on a triangular grid. "
     23:   "It is a standard nonsymmetric eigenproblem with real eigenvalues and the rightmost eigenvalue is known to be 1.\n"
     24:   "This example illustrates how the user can set the initial vector.\n\n"
     25:   "The command line options are:\n"
     26:   "  -m <m>, where <m> = number of grid subdivisions in each dimension.\n\n";

     28: #include <slepceps.h>

     30: /*
     31:    User-defined routines
     32: */
     33: PetscErrorCode MatMarkovModel(PetscInt m,Mat A);

     37: int main(int argc,char **argv)
     38: {
     39:   Vec            v0;              /* initial vector */
     40:   Mat            A;               /* operator matrix */
     41:   EPS            eps;             /* eigenproblem solver context */
     42:   EPSType        type;
     43:   PetscInt       N,m=15,nev;
     44:   PetscBool      terse;

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

     49:   PetscOptionsGetInt(NULL,"-m",&m,NULL);
     50:   N = m*(m+1)/2;
     51:   PetscPrintf(PETSC_COMM_WORLD,"\nMarkov Model, N=%D (m=%D)\n\n",N,m);

     53:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     54:      Compute the operator matrix that defines the eigensystem, Ax=kx
     55:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     57:   MatCreate(PETSC_COMM_WORLD,&A);
     58:   MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
     59:   MatSetFromOptions(A);
     60:   MatSetUp(A);
     61:   MatMarkovModel(m,A);

     63:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     64:                 Create the eigensolver and set various options
     65:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     67:   /*
     68:      Create eigensolver context
     69:   */
     70:   EPSCreate(PETSC_COMM_WORLD,&eps);

     72:   /*
     73:      Set operators. In this case, it is a standard eigenvalue problem
     74:   */
     75:   EPSSetOperators(eps,A,NULL);
     76:   EPSSetProblemType(eps,EPS_NHEP);

     78:   /*
     79:      Set solver parameters at runtime
     80:   */
     81:   EPSSetFromOptions(eps);

     83:   /*
     84:      Set the initial vector. This is optional, if not done the initial
     85:      vector is set to random values
     86:   */
     87:   MatCreateVecs(A,&v0,NULL);
     88:   VecSet(v0,1.0);
     89:   EPSSetInitialSpace(eps,1,&v0);

     91:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     92:                       Solve the eigensystem
     93:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     95:   EPSSolve(eps);

     97:   /*
     98:      Optional: Get some information from the solver and display it
     99:   */
    100:   EPSGetType(eps,&type);
    101:   PetscPrintf(PETSC_COMM_WORLD," Solution method: %s\n\n",type);
    102:   EPSGetDimensions(eps,&nev,NULL,NULL);
    103:   PetscPrintf(PETSC_COMM_WORLD," Number of requested eigenvalues: %D\n",nev);

    105:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    106:                     Display solution and clean up
    107:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

    109:   /* show detailed info unless -terse option is given by user */
    110:   PetscOptionsHasName(NULL,"-terse",&terse);
    111:   if (terse) {
    112:     EPSErrorView(eps,EPS_ERROR_RELATIVE,NULL);
    113:   } else {
    114:     PetscViewerPushFormat(PETSC_VIEWER_STDOUT_WORLD,PETSC_VIEWER_ASCII_INFO_DETAIL);
    115:     EPSReasonView(eps,PETSC_VIEWER_STDOUT_WORLD);
    116:     EPSErrorView(eps,EPS_ERROR_RELATIVE,PETSC_VIEWER_STDOUT_WORLD);
    117:     PetscViewerPopFormat(PETSC_VIEWER_STDOUT_WORLD);
    118:   }
    119:   EPSDestroy(&eps);
    120:   MatDestroy(&A);
    121:   VecDestroy(&v0);
    122:   SlepcFinalize();
    123:   return 0;
    124: }

    128: /*
    129:     Matrix generator for a Markov model of a random walk on a triangular grid.

    131:     This subroutine generates a test matrix that models a random walk on a
    132:     triangular grid. This test example was used by G. W. Stewart ["{SRRIT} - a
    133:     FORTRAN subroutine to calculate the dominant invariant subspaces of a real
    134:     matrix", Tech. report. TR-514, University of Maryland (1978).] and in a few
    135:     papers on eigenvalue problems by Y. Saad [see e.g. LAA, vol. 34, pp. 269-295
    136:     (1980) ]. These matrices provide reasonably easy test problems for eigenvalue
    137:     algorithms. The transpose of the matrix  is stochastic and so it is known
    138:     that one is an exact eigenvalue. One seeks the eigenvector of the transpose
    139:     associated with the eigenvalue unity. The problem is to calculate the steady
    140:     state probability distribution of the system, which is the eigevector
    141:     associated with the eigenvalue one and scaled in such a way that the sum all
    142:     the components is equal to one.

    144:     Note: the code will actually compute the transpose of the stochastic matrix
    145:     that contains the transition probabilities.
    146: */
    147: PetscErrorCode MatMarkovModel(PetscInt m,Mat A)
    148: {
    149:   const PetscReal cst = 0.5/(PetscReal)(m-1);
    150:   PetscReal       pd,pu;
    151:   PetscInt        Istart,Iend,i,j,jmax,ix=0;
    152:   PetscErrorCode  ierr;

    155:   MatGetOwnershipRange(A,&Istart,&Iend);
    156:   for (i=1;i<=m;i++) {
    157:     jmax = m-i+1;
    158:     for (j=1;j<=jmax;j++) {
    159:       ix = ix + 1;
    160:       if (ix-1<Istart || ix>Iend) continue;  /* compute only owned rows */
    161:       if (j!=jmax) {
    162:         pd = cst*(PetscReal)(i+j-1);
    163:         /* north */
    164:         if (i==1) {
    165:           MatSetValue(A,ix-1,ix,2*pd,INSERT_VALUES);
    166:         } else {
    167:           MatSetValue(A,ix-1,ix,pd,INSERT_VALUES);
    168:         }
    169:         /* east */
    170:         if (j==1) {
    171:           MatSetValue(A,ix-1,ix+jmax-1,2*pd,INSERT_VALUES);
    172:         } else {
    173:           MatSetValue(A,ix-1,ix+jmax-1,pd,INSERT_VALUES);
    174:         }
    175:       }
    176:       /* south */
    177:       pu = 0.5 - cst*(PetscReal)(i+j-3);
    178:       if (j>1) {
    179:         MatSetValue(A,ix-1,ix-2,pu,INSERT_VALUES);
    180:       }
    181:       /* west */
    182:       if (i>1) {
    183:         MatSetValue(A,ix-1,ix-jmax-2,pu,INSERT_VALUES);
    184:       }
    185:     }
    186:   }
    187:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
    188:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
    189:   return(0);
    190: }