GCC Code Coverage Report

Directory: ./
File: src/eps/interface/epsdefault.c
Date: 2026-05-04 03:58:11
Exec Total Coverage
Lines: 235 253 92.9%
Functions: 14 14 100.0%
Branches: 647 1112 58.2%
Line Branch Exec Source
1 /*
2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
3 SLEPc - Scalable Library for Eigenvalue Problem Computations
4 Copyright (c) 2002-, Universitat Politecnica de Valencia, Spain
5
6 This file is part of SLEPc.
7 SLEPc is distributed under a 2-clause BSD license (see LICENSE).
8 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
9 */
10 /*
11 This file contains some simple default routines for common operations
12 */
13
14 #include <slepc/private/epsimpl.h> /*I "slepceps.h" I*/
15 #include <slepcvec.h>
16
17 7533 PetscErrorCode EPSBackTransform_Default(EPS eps)
18 {
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 7533 PetscFunctionBegin;
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 7533 PetscCall(STBackTransform(eps->st,eps->nconv,eps->eigr,eps->eigi));
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 1421 PetscFunctionReturn(PETSC_SUCCESS);
22 }
23
24 /*
25 EPSComputeVectors_Hermitian - Copies the Lanczos vectors as eigenvectors
26 using purification for generalized eigenproblems.
27 */
28 3006 PetscErrorCode EPSComputeVectors_Hermitian(EPS eps)
29 {
30 3006 PetscBool iscayley,indef;
31 3006 Mat B,C;
32
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 3006 PetscFunctionBegin;
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 3006 if (eps->purify) {
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 648 PetscCall(EPS_Purify(eps,eps->nconv));
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 648 PetscCall(BVNormalize(eps->V,NULL));
37 } else {
38 /* In the case of Cayley transform, eigenvectors need to be B-normalized */
39
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 2358 PetscCall(PetscObjectTypeCompare((PetscObject)eps->st,STCAYLEY,&iscayley));
40
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 2358 if (iscayley && eps->isgeneralized) {
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 10 PetscCall(STGetMatrix(eps->st,1,&B));
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 10 PetscCall(BVGetMatrix(eps->V,&C,&indef));
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 10 PetscCheck(!indef,PetscObjectComm((PetscObject)eps),PETSC_ERR_ARG_WRONGSTATE,"The inner product should not be indefinite");
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 10 PetscCall(BVSetMatrix(eps->V,B,PETSC_FALSE));
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 10 PetscCall(BVNormalize(eps->V,NULL));
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 10 PetscCall(BVSetMatrix(eps->V,C,PETSC_FALSE)); /* restore original matrix */
47 }
48 }
49
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 584 PetscFunctionReturn(PETSC_SUCCESS);
50 }
51
52 /*
53 EPSComputeVectors_Indefinite - similar to the Schur version but
54 for indefinite problems
55 */
56 121 PetscErrorCode EPSComputeVectors_Indefinite(EPS eps)
57 {
58 121 PetscInt n;
59 121 Mat X;
60
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 121 PetscFunctionBegin;
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 121 PetscCall(DSGetDimensions(eps->ds,&n,NULL,NULL,NULL));
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 121 PetscCall(DSVectors(eps->ds,DS_MAT_X,NULL,NULL));
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 121 PetscCall(DSGetMat(eps->ds,DS_MAT_X,&X));
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 121 PetscCall(BVMultInPlace(eps->V,X,0,n));
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 121 PetscCall(DSRestoreMat(eps->ds,DS_MAT_X,&X));
67
68 /* purification */
69
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 121 if (eps->purify) PetscCall(EPS_Purify(eps,eps->nconv));
70
71 /* normalization */
72
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 121 PetscCall(BVNormalize(eps->V,eps->eigi));
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 29 PetscFunctionReturn(PETSC_SUCCESS);
74 }
75
76 /*
77 EPSComputeVectors_Twosided - Adjust left eigenvectors in generalized problems: y = B^-* y.
78 */
79 199 PetscErrorCode EPSComputeVectors_Twosided(EPS eps)
80 {
81 199 PetscInt i;
82 199 Vec w,y;
83
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 199 PetscFunctionBegin;
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 199 if (!eps->twosided || !eps->isgeneralized) PetscFunctionReturn(PETSC_SUCCESS);
86
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 41 PetscCall(EPSSetWorkVecs(eps,1));
87 41 w = eps->work[0];
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 246 for (i=0;i<eps->nconv;i++) {
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 205 PetscCall(BVCopyVec(eps->W,i,w));
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 205 PetscCall(BVGetColumn(eps->W,i,&y));
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 205 PetscCall(STMatSolveHermitianTranspose(eps->st,w,y));
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 205 PetscCall(BVRestoreColumn(eps->W,i,&y));
93 }
94
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 9 PetscFunctionReturn(PETSC_SUCCESS);
95 }
96
97 /*
98 EPSComputeVectors_Schur - Compute eigenvectors from the vectors
99 provided by the eigensolver. This version is intended for solvers
100 that provide Schur vectors. Given the partial Schur decomposition
101 OP*V=V*T, the following steps are performed:
102 1) compute eigenvectors of T: T*Z=Z*D
103 2) compute eigenvectors of OP: X=V*Z
104 */
105 3621 PetscErrorCode EPSComputeVectors_Schur(EPS eps)
106 {
107 3621 PetscInt i;
108 3621 Mat Z;
109 3621 Vec z;
110
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 3621 PetscFunctionBegin;
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 3621 if (eps->ishermitian) {
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 562 if (eps->isgeneralized && !eps->ispositive) PetscCall(EPSComputeVectors_Indefinite(eps));
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 562 else PetscCall(EPSComputeVectors_Hermitian(eps));
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 562 PetscFunctionReturn(PETSC_SUCCESS);
116 }
117
118 /* right eigenvectors */
119
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 3059 PetscCall(DSVectors(eps->ds,DS_MAT_X,NULL,NULL));
120
121 /* V = V * Z */
122
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 3059 PetscCall(DSGetMat(eps->ds,DS_MAT_X,&Z));
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 3059 PetscCall(BVMultInPlace(eps->V,Z,0,eps->nconv));
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 3059 PetscCall(DSRestoreMat(eps->ds,DS_MAT_X,&Z));
125
126 /* Purify eigenvectors */
127
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 3059 if (eps->purify) PetscCall(EPS_Purify(eps,eps->nconv));
128
129 /* Fix eigenvectors if balancing was used */
130
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 3059 if (eps->balance!=EPS_BALANCE_NONE && eps->D) {
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 740 for (i=0;i<eps->nconv;i++) {
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 620 PetscCall(BVGetColumn(eps->V,i,&z));
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 620 PetscCall(VecPointwiseDivide(z,z,eps->D));
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 620 PetscCall(BVRestoreColumn(eps->V,i,&z));
135 }
136 }
137
138 /* normalize eigenvectors (when using purification or balancing) */
139
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 3059 if (eps->purify || (eps->balance!=EPS_BALANCE_NONE && eps->D)) PetscCall(BVNormalize(eps->V,eps->eigi));
140
141 /* left eigenvectors */
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 3059 if (eps->twosided) {
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 159 PetscCall(DSVectors(eps->ds,DS_MAT_Y,NULL,NULL));
144 /* W = W * Z */
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 159 PetscCall(DSGetMat(eps->ds,DS_MAT_Y,&Z));
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 159 PetscCall(BVMultInPlace(eps->W,Z,0,eps->nconv));
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 159 PetscCall(DSRestoreMat(eps->ds,DS_MAT_Y,&Z));
148 /* Fix left eigenvectors if balancing was used */
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 159 if (eps->balance!=EPS_BALANCE_NONE && eps->D) {
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 100 for (i=0;i<eps->nconv;i++) {
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 80 PetscCall(BVGetColumn(eps->W,i,&z));
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 80 PetscCall(VecPointwiseMult(z,z,eps->D));
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 80 PetscCall(BVRestoreColumn(eps->W,i,&z));
154 }
155 }
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 159 PetscCall(EPSComputeVectors_Twosided(eps));
157 /* normalize */
158
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 159 PetscCall(BVNormalize(eps->W,eps->eigi));
159 #if !defined(PETSC_USE_COMPLEX)
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 450 for (i=0;i<eps->nconv-1;i++) {
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 361 if (eps->eigi[i] != 0.0) {
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 24 if (eps->eigi[i] > 0.0) PetscCall(BVScaleColumn(eps->W,i+1,-1.0));
163 24 i++;
164 }
165 }
166 #endif
167 }
168
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 580 PetscFunctionReturn(PETSC_SUCCESS);
169 }
170
171 /*@
172 EPSSetWorkVecs - Sets a number of work vectors into an `EPS` object.
173
174 Collective
175
176 Input Parameters:
177 + eps - the linear eigensolver context
178 - nw - number of work vectors to allocate
179
180 Developer Note:
181 This is `SLEPC_EXTERN` because it may be required by user plugin `EPS`
182 implementations.
183
184 Level: developer
185
186 .seealso: [](ch:eps), `EPSSetUp()`
187 @*/
188 46591 PetscErrorCode EPSSetWorkVecs(EPS eps,PetscInt nw)
189 {
190 46591 Vec t;
191
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 46591 PetscFunctionBegin;
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 46591 PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
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 46591 PetscValidLogicalCollectiveInt(eps,nw,2);
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 46591 PetscCheck(nw>0,PetscObjectComm((PetscObject)eps),PETSC_ERR_ARG_OUTOFRANGE,"nw must be > 0: nw = %" PetscInt_FMT,nw);
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 46591 if (eps->nwork < nw) {
197
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 7907 PetscCall(VecDestroyVecs(eps->nwork,&eps->work));
198 7907 eps->nwork = nw;
199
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 7907 PetscCall(BVGetColumn(eps->V,0,&t));
200
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 7907 PetscCall(VecDuplicateVecs(t,nw,&eps->work));
201
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 7907 PetscCall(BVRestoreColumn(eps->V,0,&t));
202 }
203
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 9145 PetscFunctionReturn(PETSC_SUCCESS);
204 }
205
206 /*
207 EPSSetWhichEigenpairs_Default - Sets the default value for which,
208 depending on the ST.
209 */
210 1243 PetscErrorCode EPSSetWhichEigenpairs_Default(EPS eps)
211 {
212 1243 PetscBool target;
213
214
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 1243 PetscFunctionBegin;
215
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 1243 PetscCall(PetscObjectTypeCompareAny((PetscObject)eps->st,&target,STSINVERT,STCAYLEY,""));
216
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 1243 if (target) eps->which = EPS_TARGET_MAGNITUDE;
217 1016 else eps->which = EPS_LARGEST_MAGNITUDE;
218
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 233 PetscFunctionReturn(PETSC_SUCCESS);
219 }
220
221 /*
222 EPSConvergedRelative - Checks convergence relative to the eigenvalue.
223 */
224 478337 PetscErrorCode EPSConvergedRelative(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
225 {
226 478337 PetscReal w;
227
228
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 478337 PetscFunctionBegin;
229 478337 w = SlepcAbsEigenvalue(eigr,eigi);
230
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 478337 *errest = (w!=0.0)? res/w: PETSC_MAX_REAL;
231
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 478337 PetscFunctionReturn(PETSC_SUCCESS);
232 }
233
234 /*
235 EPSConvergedAbsolute - Checks convergence absolutely.
236 */
237 27744 PetscErrorCode EPSConvergedAbsolute(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
238 {
239
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 27744 PetscFunctionBegin;
240 27744 *errest = res;
241
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 27744 PetscFunctionReturn(PETSC_SUCCESS);
242 }
243
244 /*
245 EPSConvergedNorm - Checks convergence relative to the eigenvalue and
246 the matrix norms.
247 */
248 56367 PetscErrorCode EPSConvergedNorm(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
249 {
250 56367 PetscReal w;
251
252
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 56367 PetscFunctionBegin;
253 56367 w = SlepcAbsEigenvalue(eigr,eigi);
254 56367 *errest = res / (eps->nrma + w*eps->nrmb);
255
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 56367 PetscFunctionReturn(PETSC_SUCCESS);
256 }
257
258 /*@C
259 EPSStoppingBasic - Default routine to determine whether the outer eigensolver
260 iteration must be stopped.
261
262 Collective
263
264 Input Parameters:
265 + eps - the linear eigensolver context
266 . its - current number of iterations
267 . max_it - maximum number of iterations
268 . nconv - number of currently converged eigenpairs
269 . nev - number of requested eigenpairs
270 - ctx - context (not used here)
271
272 Output Parameter:
273 . reason - result of the stopping test
274
275 Notes:
276 `EPSStoppingBasic()` will stop if all requested eigenvalues are converged, or if
277 the maximum number of iterations has been reached.
278
279 This is the default stopping test.
280 Use `EPSSetStoppingTest()` to provide your own test instead of using this one.
281
282 Level: advanced
283
284 .seealso: [](ch:eps), `EPSSetStoppingTest()`, `EPSStoppingThreshold()`, `EPSConvergedReason`, `EPSGetConvergedReason()`
285 @*/
286 437928 PetscErrorCode EPSStoppingBasic(EPS eps,PetscInt its,PetscInt max_it,PetscInt nconv,PetscInt nev,EPSConvergedReason *reason,void *ctx)
287 {
288
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 437928 PetscFunctionBegin;
289 437928 *reason = EPS_CONVERGED_ITERATING;
290
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 437928 if (nconv >= nev) {
291
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 8598 PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: %" PetscInt_FMT " eigenpairs converged at iteration %" PetscInt_FMT "\n",nconv,its));
292 8598 *reason = EPS_CONVERGED_TOL;
293
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 429330 } else if (its >= max_it) {
294 130 *reason = EPS_DIVERGED_ITS;
295
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 130 PetscCall(PetscInfo(eps,"Linear eigensolver iteration reached maximum number of iterations (%" PetscInt_FMT ")\n",its));
296 }
297
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 86810 PetscFunctionReturn(PETSC_SUCCESS);
298 }
299
300 /*@C
301 EPSStoppingThreshold - Routine to determine whether the outer eigenvalue solver
302 iteration must be stopped, according to some threshold for the computed values.
303
304 Collective
305
306 Input Parameters:
307 + eps - the linear eigensolver context
308 . its - current number of iterations
309 . max_it - maximum number of iterations
310 . nconv - number of currently converged eigenpairs
311 . nev - number of requested eigenpairs
312 - ctx - context containing additional data (`EPSStoppingCtx`)
313
314 Output Parameter:
315 . reason - result of the stopping test
316
317 Notes:
318 `EPSStoppingThreshold()` will stop when one of the computed eigenvalues is not
319 above/below the threshold given at `EPSSetThreshold()`. If a number of wanted
320 eigenvalues has been specified via `EPSSetDimensions()` then it is also taken into
321 account, and the solver will stop when one of the two conditions (threshold or
322 number of converged values) is met.
323
324 Use `EPSSetStoppingTest()` to provide your own test instead of using this one.
325
326 Level: advanced
327
328 .seealso: [](ch:eps), `EPSSetStoppingTest()`, `EPSStoppingBasic()`, `EPSSetThreshold()`, `EPSSetDimensions()`, `EPSConvergedReason`, `EPSGetConvergedReason()`
329 @*/
330 11290 PetscErrorCode EPSStoppingThreshold(EPS eps,PetscInt its,PetscInt max_it,PetscInt nconv,PetscInt nev,EPSConvergedReason *reason,void *ctx)
331 {
332 11290 PetscReal thres,firstev,firstnc,errest;
333 11290 PetscBool magnit,rel;
334 11290 PetscInt napprox;
335 11290 EPSWhich which;
336
337
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 11290 PetscFunctionBegin;
338 11290 *reason = EPS_CONVERGED_ITERATING;
339 11290 firstev = ((EPSStoppingCtx)ctx)->firstev;
340 11290 firstnc = ((EPSStoppingCtx)ctx)->firstnc;
341 11290 errest = ((EPSStoppingCtx)ctx)->errest;
342 11290 thres = ((EPSStoppingCtx)ctx)->thres;
343 11290 rel = ((EPSStoppingCtx)ctx)->threlative;
344 11290 napprox = ((EPSStoppingCtx)ctx)->napprox;
345 11290 which = ((EPSStoppingCtx)ctx)->which;
346
347
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 11290 if (!nconv) { /* no converged eigenvalues yet */
348
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 4572 if (its >= max_it) {
349 ✗ PetscCall(PetscInfo(eps,"Linear eigensolver iteration reached maximum number of iterations (%" PetscInt_FMT ")\n",its));
350 ✗ *reason = EPS_DIVERGED_ITS;
351 }
352
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 4572 PetscFunctionReturn(PETSC_SUCCESS);
353 }
354
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 6718 if (nconv==napprox) { /* all approximations converged, very unusual */
355 ✗ PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: all available eigenvalue approximations have converged\n"));
356 ✗ *reason = EPS_CONVERGED_TOL;
357 ✗ PetscFunctionReturn(PETSC_SUCCESS);
358 }
359 6718 magnit = (which==EPS_SMALLEST_MAGNITUDE || which==EPS_LARGEST_MAGNITUDE || which==EPS_TARGET_MAGNITUDE)? PETSC_TRUE: PETSC_FALSE;
360
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 6718 if (magnit && which==EPS_TARGET_MAGNITUDE && ((rel && ((thres>1.0 && firstnc-errest>thres*firstev) || (thres<1.0 && firstnc+errest<thres*firstev))) || (!rel && firstnc-errest>thres))) {
361
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 74 if (its==((EPSStoppingCtx)ctx)->its+1) {
362
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 37 if (!rel) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the approximation magnitude %g (minus error estimate %g) is above the threshold %g\n",(double)firstnc,(double)errest,(double)thres));
363
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 15 else if (thres>1.0) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is above the threshold %g\n",(double)(firstnc-errest),(double)firstev,(double)thres));
364
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 10 else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is below the threshold %g\n",(double)(firstnc+errest),(double)firstev,(double)thres));
365 37 *reason = EPS_CONVERGED_TOL;
366 37 } else ((EPSStoppingCtx)ctx)->its = its; /* wait until next iteration */
367
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 6644 } else if (magnit && ((which==EPS_LARGEST_MAGNITUDE && ((rel && firstnc+errest<thres*firstev) || (!rel && firstnc+errest<thres))) || (which==EPS_SMALLEST_MAGNITUDE && firstnc-errest>thres))) {
368
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 697 if (its==((EPSStoppingCtx)ctx)->its+1) {
369
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 336 if (which==EPS_SMALLEST_MAGNITUDE) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the approximation magnitude %g (minus error estimate %g) is above the threshold %g\n",(double)firstnc,(double)errest,(double)thres));
370
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 246 else if (!rel) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the approximation magnitude %g (plus error estimate %g) is below the threshold %g\n",(double)firstnc,(double)errest,(double)thres));
371
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 42 else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is below the threshold %g\n",(double)(firstnc+errest),(double)firstev,(double)thres));
372 336 *reason = EPS_CONVERGED_TOL;
373 361 } else ((EPSStoppingCtx)ctx)->its = its; /* wait until next iteration */
374
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 568 } else if (!magnit && ((which==EPS_LARGEST_REAL && firstnc+errest<thres) || (which==EPS_SMALLEST_REAL && firstnc-errest>thres))) {
375 ✗ if (its==((EPSStoppingCtx)ctx)->its+1) {
376 ✗ if (which==EPS_LARGEST_REAL) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: approximation %g (plus error estimate %g) is below the threshold %g\n",(double)firstnc,(double)errest,(double)thres));
377 ✗ else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: approximation %g (minus error estimate %g) is above the threshold %g\n",(double)firstnc,(double)errest,(double)thres));
378 ✗ *reason = EPS_CONVERGED_TOL;
379 ✗ } else ((EPSStoppingCtx)ctx)->its = its; /* wait until next iteration */
380
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 5947 } else if (nev && nconv >= nev) {
381
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 4 PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: %" PetscInt_FMT " eigenpairs converged at iteration %" PetscInt_FMT "\n",nconv,its));
382 4 *reason = EPS_CONVERGED_TOL;
383
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 5943 } else if (its >= max_it) {
384 ✗ PetscCall(PetscInfo(eps,"Linear eigensolver iteration reached maximum number of iterations (%" PetscInt_FMT ")\n",its));
385 ✗ *reason = EPS_DIVERGED_ITS;
386 }
387
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 1353 PetscFunctionReturn(PETSC_SUCCESS);
388 }
389
390 /*
391 EPSComputeRitzVector - Computes the current Ritz vector.
392
393 Simple case (complex scalars or real scalars with Zi=NULL):
394 x = V*Zr (V is a basis of nv vectors, Zr has length nv)
395
396 Split case:
397 x = V*Zr y = V*Zi (Zr and Zi have length nv)
398 */
399 4191 PetscErrorCode EPSComputeRitzVector(EPS eps,PetscScalar *Zr,PetscScalar *Zi,BV V,Vec x,Vec y)
400 {
401 4191 PetscInt l,k;
402 4191 PetscReal norm;
403 #if !defined(PETSC_USE_COMPLEX)
404 2060 Vec z;
405 #endif
406
407
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 4191 PetscFunctionBegin;
408 /* compute eigenvector */
409
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 4191 PetscCall(BVGetActiveColumns(V,&l,&k));
410
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 4191 PetscCall(BVSetActiveColumns(V,0,k));
411
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 4191 PetscCall(BVMultVec(V,1.0,0.0,x,Zr));
412
413 /* purify eigenvector if necessary */
414
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 4191 if (eps->purify) {
415
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 265 PetscCall(STApply(eps->st,x,y));
416
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 265 if (eps->ishermitian) PetscCall(BVNormVec(eps->V,y,NORM_2,&norm));
417
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 145 else PetscCall(VecNorm(y,NORM_2,&norm));
418
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 265 PetscCall(VecScale(y,1.0/norm));
419
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 265 PetscCall(VecCopy(y,x));
420 }
421 /* fix eigenvector if balancing is used */
422
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 4191 if (!eps->ishermitian && eps->balance!=EPS_BALANCE_NONE && eps->D) PetscCall(VecPointwiseDivide(x,x,eps->D));
423 #if !defined(PETSC_USE_COMPLEX)
424 /* compute imaginary part of eigenvector */
425
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 2060 if (Zi) {
426
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 125 PetscCall(BVMultVec(V,1.0,0.0,y,Zi));
427
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 125 if (eps->ispositive) {
428 ✗ PetscCall(BVCreateVec(V,&z));
429 ✗ PetscCall(STApply(eps->st,y,z));
430 ✗ PetscCall(VecNorm(z,NORM_2,&norm));
431 ✗ PetscCall(VecScale(z,1.0/norm));
432 ✗ PetscCall(VecCopy(z,y));
433 ✗ PetscCall(VecDestroy(&z));
434 }
435
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 125 if (eps->balance!=EPS_BALANCE_NONE && eps->D) PetscCall(VecPointwiseDivide(y,y,eps->D));
436 } else
437 #endif
438
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 4066 PetscCall(VecSet(y,0.0));
439
440 /* normalize eigenvectors (when using balancing) */
441
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 4191 if (eps->balance!=EPS_BALANCE_NONE && eps->D) {
442 #if !defined(PETSC_USE_COMPLEX)
443
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 60 if (Zi) PetscCall(VecNormalizeComplex(x,y,PETSC_TRUE,NULL));
444 else
445 #endif
446
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 80 PetscCall(VecNormalize(x,NULL));
447 }
448
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 4191 PetscCall(BVSetActiveColumns(V,l,k));
449
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 803 PetscFunctionReturn(PETSC_SUCCESS);
450 }
451
452 /*
453 EPSBuildBalance_Krylov - uses a Krylov subspace method to compute the
454 diagonal matrix to be applied for balancing in non-Hermitian problems.
455 */
456 130 PetscErrorCode EPSBuildBalance_Krylov(EPS eps)
457 {
458 130 Vec z,p,r;
459 130 PetscInt i,j;
460 130 PetscReal norma;
461 130 PetscScalar *pz,*pD;
462 130 const PetscScalar *pr,*pp;
463 130 PetscRandom rand;
464
465
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 130 PetscFunctionBegin;
466
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 130 PetscCall(EPSSetWorkVecs(eps,3));
467
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 130 PetscCall(BVGetRandomContext(eps->V,&rand));
468 130 r = eps->work[0];
469 130 p = eps->work[1];
470 130 z = eps->work[2];
471
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 130 PetscCall(VecSet(eps->D,1.0));
472
473
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 780 for (j=0;j<eps->balance_its;j++) {
474
475 /* Build a random vector of +-1's */
476
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 650 PetscCall(VecSetRandom(z,rand));
477
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 650 PetscCall(VecGetArray(z,&pz));
478
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 50950 for (i=0;i<eps->nloc;i++) {
479
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 50300 if (PetscRealPart(pz[i])<0.5) pz[i]=-1.0;
480 24425 else pz[i]=1.0;
481 }
482
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 650 PetscCall(VecRestoreArray(z,&pz));
483
484 /* Compute p=DA(D\z) */
485
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 650 PetscCall(VecPointwiseDivide(r,z,eps->D));
486
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 650 PetscCall(STApply(eps->st,r,p));
487
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 650 PetscCall(VecPointwiseMult(p,p,eps->D));
488
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 650 if (eps->balance == EPS_BALANCE_TWOSIDE) {
489
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 400 if (j==0) {
490 /* Estimate the matrix inf-norm */
491
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 80 PetscCall(VecAbs(p));
492
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 80 PetscCall(VecMax(p,NULL,&norma));
493 }
494 /* Compute r=D\(A'Dz) */
495
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 400 PetscCall(VecPointwiseMult(z,z,eps->D));
496
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 400 PetscCall(STApplyHermitianTranspose(eps->st,z,r));
497
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 400 PetscCall(VecPointwiseDivide(r,r,eps->D));
498 }
499
500 /* Adjust values of D */
501
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 650 PetscCall(VecGetArrayRead(r,&pr));
502
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 650 PetscCall(VecGetArrayRead(p,&pp));
503
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 650 PetscCall(VecGetArray(eps->D,&pD));
504
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 50950 for (i=0;i<eps->nloc;i++) {
505
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 50300 if (eps->balance == EPS_BALANCE_TWOSIDE) {
506
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 29000 if (PetscAbsScalar(pp[i])>eps->balance_cutoff*norma && pr[i]!=0.0)
507 28240 pD[i] *= PetscSqrtReal(PetscAbsScalar(pr[i]/pp[i]));
508 } else {
509
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 21300 if (pp[i]!=0.0) pD[i] /= PetscAbsScalar(pp[i]);
510 }
511 }
512
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 650 PetscCall(VecRestoreArrayRead(r,&pr));
513
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 650 PetscCall(VecRestoreArrayRead(p,&pp));
514
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 650 PetscCall(VecRestoreArray(eps->D,&pD));
515 }
516
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 26 PetscFunctionReturn(PETSC_SUCCESS);
517 }
518