Actual source code: test9.c

slepc-3.14.0 2020-09-30
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  1: /*
  2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
  4:    Copyright (c) 2002-2020, Universitat Politecnica de Valencia, Spain

  6:    This file is part of SLEPc.
  7:    SLEPc is distributed under a 2-clause BSD license (see LICENSE).
  8:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  9: */

 11: static char help[] = "Eigenvalue problem associated with a Markov model of a random walk on a triangular grid. "
 12:   "It is a standard nonsymmetric eigenproblem with real eigenvalues and the rightmost eigenvalue is known to be 1.\n"
 13:   "This example illustrates how the user can set the initial vector.\n\n"
 14:   "The command line options are:\n"
 15:   "  -m <m>, where <m> = number of grid subdivisions in each dimension.\n\n";

 17: #include <slepceps.h>

 19: /*
 20:    User-defined routines
 21: */
 22: PetscErrorCode MatMarkovModel(PetscInt m,Mat A);
 23: PetscErrorCode MyEigenSort(PetscScalar ar,PetscScalar ai,PetscScalar br,PetscScalar bi,PetscInt *r,void *ctx);

 25: /*
 26:    Check if computed eigenvectors have unit norm
 27: */
 28: PetscErrorCode CheckNormalizedVectors(EPS eps)
 29: {
 31:   PetscInt       i,nconv;
 32:   Mat            A;
 33:   Vec            xr,xi;
 34:   PetscReal      error=0.0,normr;
 35: #if !defined(PETSC_USE_COMPLEX)
 36:   PetscReal      normi;
 37: #endif

 40:   EPSGetConverged(eps,&nconv);
 41:   if (nconv>0) {
 42:     EPSGetOperators(eps,&A,NULL);
 43:     MatCreateVecs(A,&xr,&xi);
 44:     for (i=0;i<nconv;i++) {
 45:       EPSGetEigenvector(eps,i,xr,xi);
 46: #if defined(PETSC_USE_COMPLEX)
 47:       VecNorm(xr,NORM_2,&normr);
 48:       error = PetscMax(error,PetscAbsReal(normr-PetscRealConstant(1.0)));
 49: #else
 50:       VecNormBegin(xr,NORM_2,&normr);
 51:       VecNormBegin(xi,NORM_2,&normi);
 52:       VecNormEnd(xr,NORM_2,&normr);
 53:       VecNormEnd(xi,NORM_2,&normi);
 54:       error = PetscMax(error,PetscAbsReal(SlepcAbsEigenvalue(normr,normi)-PetscRealConstant(1.0)));
 55: #endif
 56:     }
 57:     VecDestroy(&xr);
 58:     VecDestroy(&xi);
 59:     if (error>100*PETSC_MACHINE_EPSILON) {
 60:       PetscPrintf(PETSC_COMM_WORLD,"Vectors are not normalized. Error=%g\n",(double)error);
 61:     }
 62:   }
 63:   return(0);
 64: }

 66: int main(int argc,char **argv)
 67: {
 68:   Vec            v0;              /* initial vector */
 69:   Mat            A;               /* operator matrix */
 70:   EPS            eps;             /* eigenproblem solver context */
 71:   PetscReal      tol=1000*PETSC_MACHINE_EPSILON;
 72:   PetscInt       N,m=15,nev;
 73:   PetscScalar    origin=0.0;
 74:   PetscBool      flg,delay,skipnorm=PETSC_FALSE;

 77:   SlepcInitialize(&argc,&argv,(char*)0,help);if (ierr) return ierr;

 79:   PetscOptionsGetInt(NULL,NULL,"-m",&m,NULL);
 80:   N = m*(m+1)/2;
 81:   PetscPrintf(PETSC_COMM_WORLD,"\nMarkov Model, N=%D (m=%D)\n\n",N,m);
 82:   PetscOptionsGetBool(NULL,NULL,"-skipnorm",&skipnorm,NULL);

 84:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 85:      Compute the operator matrix that defines the eigensystem, Ax=kx
 86:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

 88:   MatCreate(PETSC_COMM_WORLD,&A);
 89:   MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
 90:   MatSetFromOptions(A);
 91:   MatSetUp(A);
 92:   MatMarkovModel(m,A);

 94:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 95:                 Create the eigensolver and set various options
 96:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

 98:   /*
 99:      Create eigensolver context
100:   */
101:   EPSCreate(PETSC_COMM_WORLD,&eps);

103:   /*
104:      Set operators. In this case, it is a standard eigenvalue problem
105:   */
106:   EPSSetOperators(eps,A,NULL);
107:   EPSSetProblemType(eps,EPS_NHEP);
108:   EPSSetTolerances(eps,tol,PETSC_DEFAULT);

110:   /*
111:      Set the custom comparing routine in order to obtain the eigenvalues
112:      closest to the target on the right only
113:   */
114:   EPSSetEigenvalueComparison(eps,MyEigenSort,&origin);


117:   /*
118:      Set solver parameters at runtime
119:   */
120:   EPSSetFromOptions(eps);
121:   PetscObjectTypeCompare((PetscObject)eps,EPSARNOLDI,&flg);
122:   if (flg) {
123:     EPSArnoldiGetDelayed(eps,&delay);
124:     if (delay) {
125:       PetscPrintf(PETSC_COMM_WORLD," Warning: delayed reorthogonalization may be unstable\n");
126:     }
127:   }

129:   /*
130:      Set the initial vector. This is optional, if not done the initial
131:      vector is set to random values
132:   */
133:   MatCreateVecs(A,&v0,NULL);
134:   VecSetValue(v0,0,-1.5,INSERT_VALUES);
135:   VecSetValue(v0,1,2.1,INSERT_VALUES);
136:   VecAssemblyBegin(v0);
137:   VecAssemblyEnd(v0);
138:   EPSSetInitialSpace(eps,1,&v0);

140:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
141:                       Solve the eigensystem
142:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

144:   EPSSolve(eps);
145:   EPSGetDimensions(eps,&nev,NULL,NULL);
146:   PetscPrintf(PETSC_COMM_WORLD," Number of requested eigenvalues: %D\n",nev);

148:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
149:                     Display solution and clean up
150:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

152:   EPSErrorView(eps,EPS_ERROR_RELATIVE,NULL);
153:   if (!skipnorm) { CheckNormalizedVectors(eps); }
154:   EPSDestroy(&eps);
155:   MatDestroy(&A);
156:   VecDestroy(&v0);
157:   SlepcFinalize();
158:   return ierr;
159: }

161: PetscErrorCode MatMarkovModel(PetscInt m,Mat A)
162: {
163:   const PetscReal cst = 0.5/(PetscReal)(m-1);
164:   PetscReal       pd,pu;
165:   PetscInt        Istart,Iend,i,j,jmax,ix=0;
166:   PetscErrorCode  ierr;

169:   MatGetOwnershipRange(A,&Istart,&Iend);
170:   for (i=1;i<=m;i++) {
171:     jmax = m-i+1;
172:     for (j=1;j<=jmax;j++) {
173:       ix = ix + 1;
174:       if (ix-1<Istart || ix>Iend) continue;  /* compute only owned rows */
175:       if (j!=jmax) {
176:         pd = cst*(PetscReal)(i+j-1);
177:         /* north */
178:         if (i==1) {
179:           MatSetValue(A,ix-1,ix,2*pd,INSERT_VALUES);
180:         } else {
181:           MatSetValue(A,ix-1,ix,pd,INSERT_VALUES);
182:         }
183:         /* east */
184:         if (j==1) {
185:           MatSetValue(A,ix-1,ix+jmax-1,2*pd,INSERT_VALUES);
186:         } else {
187:           MatSetValue(A,ix-1,ix+jmax-1,pd,INSERT_VALUES);
188:         }
189:       }
190:       /* south */
191:       pu = 0.5 - cst*(PetscReal)(i+j-3);
192:       if (j>1) {
193:         MatSetValue(A,ix-1,ix-2,pu,INSERT_VALUES);
194:       }
195:       /* west */
196:       if (i>1) {
197:         MatSetValue(A,ix-1,ix-jmax-2,pu,INSERT_VALUES);
198:       }
199:     }
200:   }
201:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
202:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
203:   return(0);
204: }

206: /*
207:     Function for user-defined eigenvalue ordering criterion.

209:     Given two eigenvalues ar+i*ai and br+i*bi, the subroutine must choose
210:     one of them as the preferred one according to the criterion.
211:     In this example, the preferred value is the one furthest away from the origin.
212: */
213: PetscErrorCode MyEigenSort(PetscScalar ar,PetscScalar ai,PetscScalar br,PetscScalar bi,PetscInt *r,void *ctx)
214: {
215:   PetscScalar origin = *(PetscScalar*)ctx;
216:   PetscReal   d;

219:   d = (SlepcAbsEigenvalue(br-origin,bi) - SlepcAbsEigenvalue(ar-origin,ai))/PetscMax(SlepcAbsEigenvalue(ar-origin,ai),SlepcAbsEigenvalue(br-origin,bi));
220:   *r = d > PETSC_SQRT_MACHINE_EPSILON ? 1 : (d < -PETSC_SQRT_MACHINE_EPSILON ? -1 : PetscSign(PetscRealPart(br)));
221:   return(0);
222: }

224: /*TEST

226:    testset:
227:       args: -eps_nev 4
228:       requires: !single
229:       output_file: output/test9_1.out
230:       test:
231:          suffix: 1
232:          args: -eps_type {{krylovschur arnoldi lapack}} -eps_ncv 7 -eps_max_it 300
233:       test:
234:          suffix: 1_gd
235:          args: -eps_type gd -st_pc_type none
236:       test:
237:          suffix: 1_gd2
238:          args: -eps_type gd -eps_gd_double_expansion -st_pc_type none

240:    test:
241:       suffix: 2
242:       args: -eps_balance {{none oneside twoside}} -eps_krylovschur_locking {{0 1}} -eps_nev 4 -eps_ncv 7 -eps_max_it 500
243:       requires: double
244:       output_file: output/test9_1.out

246:    test:
247:       suffix: 3
248:       nsize: 2
249:       args: -eps_type arnoldi -eps_arnoldi_delayed -eps_largest_real -eps_nev 3 -eps_tol 1e-7 -bv_orthog_refine {{never ifneeded}} -skipnorm
250:       requires: !single
251:       output_file: output/test9_3.out

253:    test:
254:       suffix: 4
255:       args: -eps_nev 4 -eps_true_residual
256:       output_file: output/test9_1.out

258:    test:
259:       suffix: 5
260:       args: -eps_type jd -eps_nev 3 -eps_target .5 -eps_harmonic -st_ksp_type bicg -st_pc_type lu -eps_jd_minv 2
261:       requires: !single

263:    test:
264:       suffix: 5_arpack
265:       args: -eps_nev 3 -st_type sinvert -eps_target .5 -eps_type arpack -eps_ncv 10
266:       requires: arpack
267:       output_file: output/test9_5.out

269:    testset:
270:       args: -eps_type ciss -eps_tol 1e-9 -rg_type ellipse -rg_ellipse_center 0.55 -rg_ellipse_radius 0.05 -rg_ellipse_vscale 0.1 -eps_ciss_usest 0 -eps_all
271:       requires: double
272:       output_file: output/test9_6.out
273:       test:
274:          suffix: 6
275:       test:
276:          suffix: 6_hankel
277:          args: -eps_ciss_extraction hankel -eps_ciss_spurious_threshold 1e-6 -eps_ncv 64
278:       test:
279:          suffix: 6_cheby
280:          args: -eps_ciss_quadrule chebyshev
281:       test:
282:          suffix: 6_hankel_cheby
283:          args: -eps_ciss_extraction hankel -eps_ciss_quadrule chebyshev -eps_ncv 64
284:       test:
285:          suffix: 6_refine
286:          args: -eps_ciss_refine_inner 1 -eps_ciss_refine_blocksize 1
287:       test:
288:          suffix: 6_bcgs
289:          args: -eps_ciss_realmats -eps_ciss_ksp_type bcgs -eps_ciss_pc_type sor -eps_ciss_integration_points 12

291:    test:
292:       suffix: 7
293:       args: -eps_nev 4 -eps_two_sided -eps_view_vectors ::ascii_info -eps_view_values
294:       requires: !single
295:       filter: sed -e "s/\(0x[0-9a-fA-F]*\)/objectid/"

297:    test:
298:       suffix: 8
299:       args: -eps_nev 4 -eps_view_values draw -eps_monitor_lg
300:       requires: x
301:       output_file: output/test9_1.out

303:    test:
304:       suffix: 5_ksphpddm
305:       args: -eps_nev 3 -st_type sinvert -eps_target .5 -st_ksp_type hpddm -st_ksp_hpddm_type gcrodr -eps_ncv 10
306:       requires: hpddm
307:       output_file: output/test9_5.out

309:    test:
310:       suffix: 5_pchpddm
311:       args: -eps_nev 3 -st_type sinvert -eps_target .5 -st_pc_type hpddm -st_pc_hpddm_coarse_pc_type lu -eps_ncv 10
312:       requires: hpddm
313:       output_file: output/test9_5.out

315: TEST*/