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1298 slepc 1
/*                      
2
 
3
   SLEPc singular value solver: "trlanczos"
4
 
5
   Method: Golub-Kahan-Lanczos bidiagonalization with thick-restart
6
 
1328 slepc 7
   Last update: Mar 2007
1298 slepc 8
 
9
*/
10
#include "src/svd/svdimpl.h"                /*I "slepcsvd.h" I*/
11
#include "slepcblaslapack.h"
12
 
13
typedef struct {
14
  PetscTruth oneside;
15
} SVD_TRLANCZOS;
16
 
17
#undef __FUNCT__  
18
#define __FUNCT__ "SVDSetUp_TRLANCZOS"
19
PetscErrorCode SVDSetUp_TRLANCZOS(SVD svd)
20
{
21
  PetscErrorCode  ierr;
1314 slepc 22
  PetscInt        N;
23
  int             i;
1298 slepc 24
 
25
  PetscFunctionBegin;
1314 slepc 26
  ierr = SVDMatGetSize(svd,PETSC_NULL,&N);CHKERRQ(ierr);
1298 slepc 27
  if (svd->ncv == PETSC_DECIDE)
1314 slepc 28
    svd->ncv = PetscMin(N,PetscMax(2*svd->nsv,10));
1298 slepc 29
  if (svd->max_it == PETSC_DECIDE)
1314 slepc 30
    svd->max_it = PetscMax(N/svd->ncv,100);
31
  if (svd->ncv!=svd->n) {  
32
    if (svd->U) {
33
      for (i=0;i<svd->n;i++) { ierr = VecDestroy(svd->U[i]); CHKERRQ(ierr); }
34
      ierr = PetscFree(svd->U);CHKERRQ(ierr);
35
    }
36
    ierr = PetscMalloc(sizeof(Vec)*svd->ncv,&svd->U);CHKERRQ(ierr);
37
    for (i=0;i<svd->ncv;i++) { ierr = SVDMatGetVecs(svd,PETSC_NULL,svd->U+i);CHKERRQ(ierr); }
38
  }
1298 slepc 39
  PetscFunctionReturn(0);
40
}
41
 
42
#undef __FUNCT__  
1328 slepc 43
#define __FUNCT__ "SVDOneSideTRLanczos"
44
static PetscErrorCode SVDOneSideTRLanczos(SVD svd,PetscReal *alpha,PetscReal *beta,PetscScalar* bb,Vec *V,Vec v,Vec* U,int nconv,int l,int n,PetscScalar* work,Vec wv,Vec wu)
45
{
46
  PetscErrorCode ierr;
47
  PetscReal      a,b;
48
  int            i,j,k=nconv+l;
49
 
50
  PetscFunctionBegin;
51
  ierr = SVDMatMult(svd,PETSC_FALSE,V[k],U[k]);CHKERRQ(ierr);
52
  if (l>0) {
53
    ierr = VecSet(wu,0.0);CHKERRQ(ierr);
54
    ierr = VecMAXPY(wu,l,bb,U+nconv);CHKERRQ(ierr);
55
    ierr = VecAXPY(U[k],-1.0,wu);CHKERRQ(ierr);
56
  }
57
  for (i=k+1;i<n;i++) {
58
    ierr = SVDMatMult(svd,PETSC_TRUE,U[i-1],V[i]);CHKERRQ(ierr);
59
    svd->dots += i;
60
    ierr = VecNormBegin(U[i-1],NORM_2,&a);CHKERRQ(ierr);
61
    ierr = VecMDotBegin(V[i],i,V,work);CHKERRQ(ierr);
62
    ierr = VecNormEnd(U[i-1],NORM_2,&a);CHKERRQ(ierr);
63
    ierr = VecMDotEnd(V[i],i,V,work);CHKERRQ(ierr);
64
 
65
    ierr = VecScale(U[i-1],1.0/a);CHKERRQ(ierr);
66
    ierr = VecScale(V[i],1.0/a);CHKERRQ(ierr);
67
    for (j=0;j<i;j++) work[j] = - work[j] / a;
68
    ierr = VecMAXPY(V[i],i,work,V);CHKERRQ(ierr);
69
 
70
    ierr = IPOrthogonalizeCGS(svd->ip,i,PETSC_NULL,V,V[i],work,PETSC_NULL,&b,wv);CHKERRQ(ierr);
71
    ierr = VecScale(V[i],1.0/b);CHKERRQ(ierr);
72
 
73
    ierr = SVDMatMult(svd,PETSC_FALSE,V[i],U[i]);CHKERRQ(ierr);
74
    ierr = VecAXPY(U[i],-b,U[i-1]);CHKERRQ(ierr);
75
 
76
    alpha[i-k-1] = a;
77
    beta[i-k-1] = b;
78
  }
79
  ierr = SVDMatMult(svd,PETSC_TRUE,U[n-1],v);CHKERRQ(ierr);
80
  svd->dots += i;
81
  ierr = VecNormBegin(U[n-1],NORM_2,&a);CHKERRQ(ierr);
82
  ierr = VecMDotBegin(v,n,V,work);CHKERRQ(ierr);
83
  ierr = VecNormEnd(U[n-1],NORM_2,&a);CHKERRQ(ierr);
84
  ierr = VecMDotEnd(v,n,V,work);CHKERRQ(ierr);
85
 
86
  ierr = VecScale(U[n-1],1.0/a);CHKERRQ(ierr);
87
  ierr = VecScale(v,1.0/a);CHKERRQ(ierr);
88
  for (j=0;j<n;j++) work[j] = - work[j] / a;
89
  ierr = VecMAXPY(v,n,work,V);CHKERRQ(ierr);
90
 
91
  ierr = IPOrthogonalizeCGS(svd->ip,n,PETSC_NULL,V,v,work,PETSC_NULL,&b,wv);CHKERRQ(ierr);
92
 
93
  alpha[n-k-1] = a;
94
  beta[n-k-1] = b;
95
  PetscFunctionReturn(0);
96
}
97
 
98
#undef __FUNCT__  
1298 slepc 99
#define __FUNCT__ "SVDSolve_TRLANCZOS"
100
PetscErrorCode SVDSolve_TRLANCZOS(SVD svd)
101
{
102
  PetscErrorCode ierr;
103
  SVD_TRLANCZOS  *lanczos = (SVD_TRLANCZOS *)svd->data;
1328 slepc 104
  PetscReal      *alpha,*beta,norm;
1298 slepc 105
  PetscScalar    *b,*Q,*PT;
1328 slepc 106
  PetscInt       *perm;
107
  int            i,j,k,l,m,n,nwork=0;
108
  Vec            v,wv,wu,*workV,*workU,*permV,*permU;
109
  PetscTruth     conv;
1298 slepc 110
 
111
  PetscFunctionBegin;
112
  /* allocate working space */
1307 slepc 113
  ierr = PetscMalloc(sizeof(PetscReal)*svd->n,&alpha);CHKERRQ(ierr);
114
  ierr = PetscMalloc(sizeof(PetscReal)*svd->n,&beta);CHKERRQ(ierr);
115
  ierr = PetscMalloc(sizeof(PetscScalar)*svd->n,&b);CHKERRQ(ierr);
116
  ierr = PetscMalloc(sizeof(PetscScalar)*svd->n*svd->n,&Q);CHKERRQ(ierr);
117
  ierr = PetscMalloc(sizeof(PetscScalar)*svd->n*svd->n,&PT);CHKERRQ(ierr);
1328 slepc 118
  ierr = VecDuplicate(svd->V[0],&v);CHKERRQ(ierr);
119
  ierr = VecDuplicate(svd->V[0],&wv);CHKERRQ(ierr);
120
  ierr = VecDuplicate(svd->U[0],&wu);CHKERRQ(ierr);
121
  ierr = PetscMalloc(sizeof(Vec)*svd->n,&workV);CHKERRQ(ierr);
122
  ierr = PetscMalloc(sizeof(Vec)*svd->n,&workU);CHKERRQ(ierr);
1298 slepc 123
 
124
  /* normalize start vector */
1328 slepc 125
  ierr = VecCopy(svd->vec_initial,svd->V[0]);CHKERRQ(ierr);
126
  ierr = VecNormalize(svd->V[0],&norm);CHKERRQ(ierr);
1298 slepc 127
 
128
  l = 0;
129
  while (svd->reason == SVD_CONVERGED_ITERATING) {
130
    svd->its++;
131
 
132
    /* inner loop */
1328 slepc 133
    if (lanczos->oneside) {
134
      ierr = SVDOneSideTRLanczos(svd,alpha,beta,b+svd->nconv,svd->V,v,svd->U,svd->nconv,l,svd->n,PT,wv,wu);CHKERRQ(ierr);
135
    } else {
136
      ierr = SVDTwoSideLanczos(svd,alpha,beta,svd->V,v,svd->U,svd->nconv+l,svd->n,PT,wv,wu);CHKERRQ(ierr);
1298 slepc 137
    }
1328 slepc 138
    ierr = VecScale(v,1.0/beta[svd->n-svd->nconv-l-1]);CHKERRQ(ierr);
139
 
1298 slepc 140
    /* compute SVD of general matrix */
1328 slepc 141
    n = svd->n - svd->nconv;
1298 slepc 142
    /* first l columns */
143
    for (j=0;j<l;j++) {
144
      for (i=0;i<j;i++) Q[j*n+i] = 0.0;    
1328 slepc 145
      Q[j*n+j] = svd->sigma[svd->nconv+j];
1298 slepc 146
      for (i=j+1;i<n;i++) Q[j*n+i] = 0.0;
147
    }
148
    /* l+1 column */
1328 slepc 149
    for (i=0;i<l;i++) Q[l*n+i] = b[i+svd->nconv];
150
    Q[l*n+l] = alpha[0];
1298 slepc 151
    for (i=l+1;i<n;i++) Q[l*n+i] = 0.0;
152
    /* rest of matrix */
153
    for (j=l+1;j<n;j++) {
154
      for (i=0;i<j-1;i++) Q[j*n+i] = 0.0;
1328 slepc 155
      Q[j*n+j-1] = beta[j-l-1];
156
      Q[j*n+j] = alpha[j-l];
1298 slepc 157
      for (i=j+1;i<n;i++) Q[j*n+i] = 0.0;
158
    }
1328 slepc 159
    ierr = SVDDense(n,n,Q,alpha,PETSC_NULL,PT);CHKERRQ(ierr);
1298 slepc 160
 
161
    /* compute error estimates */
1328 slepc 162
    k = 0;
163
    conv = PETSC_TRUE;
164
    for (i=svd->nconv;i<svd->n;i++) {
165
      if (svd->which == SVD_SMALLEST) j = n-i+svd->nconv-1;
166
      else j = i-svd->nconv;
167
      svd->sigma[i] = alpha[j];
168
      b[i] = Q[j*n+n-1]*beta[n-l-1];
169
      svd->errest[i] = PetscAbsScalar(b[i]);
170
      if (alpha[j] > svd->tol) svd->errest[i] /= alpha[j];
171
      if (conv) {
172
        if (svd->errest[i] < svd->tol) k++;
173
        else conv = PETSC_FALSE;
1304 slepc 174
      }
1298 slepc 175
    }
176
 
1328 slepc 177
    /* check convergence and update l */
178
    if (svd->its >= svd->max_it) svd->reason = SVD_DIVERGED_ITS;
179
    if (svd->nconv+k >= svd->nsv) svd->reason = SVD_CONVERGED_TOL;
180
    if (svd->reason != SVD_CONVERGED_ITERATING) l = 0;
181
    else l = PetscMax((svd->n - svd->nconv - k) / 2,1);
1300 slepc 182
 
1328 slepc 183
    /* allocate work space for converged singular and restart vectors */
184
    if (nwork<k+l) {
185
      for (i=nwork;i<k+l;i++) {
186
        ierr = SVDMatGetVecs(svd,workV+i,workU+i);CHKERRQ(ierr);
1300 slepc 187
      }
1328 slepc 188
      nwork = k+l;
1298 slepc 189
    }
190
 
1328 slepc 191
    /* compute converged singular vectors and restart vectors*/
192
    for (i=0;i<k+l;i++) {
193
      if (svd->which == SVD_SMALLEST) j = n-i-1;
194
      else j = i;
195
      ierr = VecSet(workV[i],0.0);CHKERRQ(ierr);
196
      for (m=0;m<n;m++) {
197
        ierr = VecAXPY(workV[i],PT[m*n+j],svd->V[m+svd->nconv]);CHKERRQ(ierr);
198
      }      
199
      ierr = VecSet(workU[i],0.0);CHKERRQ(ierr);
200
      ierr = VecMAXPY(workU[i],n,Q+j*n,svd->U+svd->nconv);CHKERRQ(ierr);
201
    }
202
 
1298 slepc 203
    /* copy the last vector to be the next initial vector */
204
    if (svd->reason == SVD_CONVERGED_ITERATING) {
1328 slepc 205
      ierr = VecCopy(v,svd->V[svd->nconv+k+l]);CHKERRQ(ierr);
1298 slepc 206
    }
207
 
1328 slepc 208
    /* copy converged singular vectors and restart vectors from temporary space */
209
    for (i=0;i<k+l;i++) {
210
      ierr = VecCopy(workV[i],svd->V[i+svd->nconv]);CHKERRQ(ierr);
211
      ierr = VecCopy(workU[i],svd->U[i+svd->nconv]);CHKERRQ(ierr);
212
    }
213
 
214
    svd->nconv += k;
215
    SVDMonitor(svd,svd->its,svd->nconv,svd->sigma,svd->errest,svd->n);
1298 slepc 216
  }
217
 
218
  /* sort singular triplets */
219
  ierr = PetscMalloc(sizeof(PetscInt)*svd->nconv,&perm);CHKERRQ(ierr);
1328 slepc 220
  ierr = PetscMalloc(sizeof(Vec)*svd->nconv,&permV);CHKERRQ(ierr);
221
  ierr = PetscMalloc(sizeof(Vec)*svd->nconv,&permU);CHKERRQ(ierr);
1298 slepc 222
  for (i=0;i<svd->nconv;i++) {
223
    alpha[i] = svd->sigma[i];
224
    beta[i] = svd->errest[i];
1328 slepc 225
    permV[i] = svd->V[i];
226
    permU[i] = svd->U[i];
1298 slepc 227
    perm[i] = i;
228
  }
1328 slepc 229
  ierr = PetscSortRealWithPermutation(svd->nconv,svd->sigma,perm);CHKERRQ(ierr);
230
  for (i=0;i<svd->nconv;i++) {
1298 slepc 231
    if (svd->which == SVD_SMALLEST) j = perm[i];
1300 slepc 232
    else j = perm[svd->nconv-i-1];
1328 slepc 233
    svd->sigma[i] = alpha[j];
234
    svd->errest[i] = beta[j];
235
    svd->V[i] = permV[j];
236
    svd->U[i] = permU[j];
1298 slepc 237
  }
238
 
239
  /* free working space */
1328 slepc 240
  ierr = VecDestroy(v);CHKERRQ(ierr);
241
  ierr = VecDestroy(wv);CHKERRQ(ierr);
242
  ierr = VecDestroy(wu);CHKERRQ(ierr);
243
  for (i=0;i<nwork;i++) { ierr = VecDestroy(workV[i]);CHKERRQ(ierr); }
244
  ierr = PetscFree(workV);CHKERRQ(ierr);
245
  for (i=0;i<nwork;i++) { ierr = VecDestroy(workU[i]);CHKERRQ(ierr); }
246
  ierr = PetscFree(workU);CHKERRQ(ierr);
1298 slepc 247
 
248
  ierr = PetscFree(alpha);CHKERRQ(ierr);
249
  ierr = PetscFree(beta);CHKERRQ(ierr);
250
  ierr = PetscFree(b);CHKERRQ(ierr);
251
  ierr = PetscFree(Q);CHKERRQ(ierr);
252
  ierr = PetscFree(PT);CHKERRQ(ierr);
253
  ierr = PetscFree(perm);CHKERRQ(ierr);
1328 slepc 254
  ierr = PetscFree(permV);CHKERRQ(ierr);
255
  ierr = PetscFree(permU);CHKERRQ(ierr);
1298 slepc 256
  PetscFunctionReturn(0);
257
}
258
 
259
#undef __FUNCT__  
260
#define __FUNCT__ "SVDSetFromOptions_TRLANCZOS"
261
PetscErrorCode SVDSetFromOptions_TRLANCZOS(SVD svd)
262
{
263
  PetscErrorCode ierr;
264
  SVD_TRLANCZOS  *lanczos = (SVD_TRLANCZOS *)svd->data;
265
 
266
  PetscFunctionBegin;
267
  ierr = PetscOptionsBegin(svd->comm,svd->prefix,"TRLANCZOS Singular Value Solver Options","SVD");CHKERRQ(ierr);
1328 slepc 268
  ierr = PetscOptionsTruth("-svd_trlanczos_oneside","Lanczos one-side reorthogonalization","SVDTRLanczosSetOneSideReorthogonalization",PETSC_FALSE,&lanczos->oneside,PETSC_NULL);CHKERRQ(ierr);
1298 slepc 269
  ierr = PetscOptionsEnd();CHKERRQ(ierr);
270
  PetscFunctionReturn(0);
271
}
272
EXTERN_C_BEGIN
273
 
274
#undef __FUNCT__  
275
#define __FUNCT__ "SVDTRLanczosSetOneSideReorthogonalization_TRLANCZOS"
276
PetscErrorCode SVDTRLanczosSetOneSideReorthogonalization_TRLANCZOS(SVD svd,PetscTruth oneside)
277
{
278
  SVD_TRLANCZOS *lanczos = (SVD_TRLANCZOS *)svd->data;
279
 
280
  PetscFunctionBegin;
281
  lanczos->oneside = oneside;
282
  PetscFunctionReturn(0);
283
}
284
EXTERN_C_BEGIN
285
 
286
#undef __FUNCT__
287
#define __FUNCT__ "SVDTRLanczosSetOneSideReorthogonalization"
288
PetscErrorCode SVDTRLanczosSetOneSideReorthogonalization(SVD svd,PetscTruth oneside)
289
{
290
  PetscErrorCode ierr, (*f)(SVD,PetscTruth);
291
 
292
  PetscFunctionBegin;
293
  PetscValidHeaderSpecific(svd,SVD_COOKIE,1);
294
  ierr = PetscObjectQueryFunction((PetscObject)svd,"SVDTRLanczosSetOneSideReorthogonalization_C",(void (**)())&f);CHKERRQ(ierr);
295
  if (f) {
296
    ierr = (*f)(svd,oneside);CHKERRQ(ierr);
297
  }
298
  PetscFunctionReturn(0);
299
}
300
 
301
#undef __FUNCT__  
302
#define __FUNCT__ "SVDView_TRLANCZOS"
303
PetscErrorCode SVDView_TRLANCZOS(SVD svd,PetscViewer viewer)
304
{
305
  PetscErrorCode ierr;
306
  SVD_TRLANCZOS  *lanczos = (SVD_TRLANCZOS *)svd->data;
307
 
308
  PetscFunctionBegin;
309
  ierr = PetscViewerASCIIPrintf(viewer,"Lanczos reorthogonalization: %s\n",lanczos->oneside ? "one-side" : "two-side");CHKERRQ(ierr);
310
  PetscFunctionReturn(0);
311
}
312
 
313
EXTERN_C_BEGIN
314
#undef __FUNCT__  
315
#define __FUNCT__ "SVDCreate_TRLANCZOS"
316
PetscErrorCode SVDCreate_TRLANCZOS(SVD svd)
317
{
318
  PetscErrorCode ierr;
319
  SVD_TRLANCZOS  *lanczos;
320
 
321
  PetscFunctionBegin;
322
  ierr = PetscNew(SVD_TRLANCZOS,&lanczos);CHKERRQ(ierr);
323
  PetscLogObjectMemory(svd,sizeof(SVD_TRLANCZOS));
324
  svd->data                = (void *)lanczos;
325
  svd->ops->setup          = SVDSetUp_TRLANCZOS;
326
  svd->ops->solve          = SVDSolve_TRLANCZOS;
327
  svd->ops->setfromoptions = SVDSetFromOptions_TRLANCZOS;
328
  svd->ops->view           = SVDView_TRLANCZOS;
329
  lanczos->oneside         = PETSC_FALSE;
330
  ierr = PetscObjectComposeFunctionDynamic((PetscObject)svd,"SVDTRLanczosSetOneSideReorthogonalization_C","SVDTRLanczosSetOneSideReorthogonalization_TRLANCZOS",SVDTRLanczosSetOneSideReorthogonalization_TRLANCZOS);CHKERRQ(ierr);
331
  PetscFunctionReturn(0);
332
}
333
EXTERN_C_END