GCC Code Coverage Report

Directory: ./
File: src/eps/interface/epsdefault.c
Date: 2025-11-19 04:19:03
Exec Total Coverage
Lines: 224 235 95.3%
Functions: 14 14 100.0%
Branches: 630 1066 59.1%
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 6957 PetscErrorCode EPSBackTransform_Default(EPS eps)
18 {
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 6957 PetscFunctionBegin;
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 6957 PetscCall(STBackTransform(eps->st,eps->nconv,eps->eigr,eps->eigi));
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 1316 PetscFunctionReturn(PETSC_SUCCESS);
22 }
23
24 /*
25 EPSComputeVectors_Hermitian - Copies the Lanczos vectors as eigenvectors
26 using purification for generalized eigenproblems.
27 */
28 2985 PetscErrorCode EPSComputeVectors_Hermitian(EPS eps)
29 {
30 2985 PetscBool iscayley,indef;
31 2985 Mat B,C;
32
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 2985 PetscFunctionBegin;
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 2985 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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 2337 PetscCall(PetscObjectTypeCompare((PetscObject)eps->st,STCAYLEY,&iscayley));
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 2337 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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 581 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);
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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 }
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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 3535 PetscErrorCode EPSComputeVectors_Schur(EPS eps)
106 {
107 3535 PetscInt i;
108 3535 Mat Z;
109 3535 Vec z;
110
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 3535 PetscFunctionBegin;
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 3535 if (eps->ishermitian) {
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 552 if (eps->isgeneralized && !eps->ispositive) PetscCall(EPSComputeVectors_Indefinite(eps));
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 552 else PetscCall(EPSComputeVectors_Hermitian(eps));
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 552 PetscFunctionReturn(PETSC_SUCCESS);
116 }
117
118 /* right eigenvectors */
119
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 2983 PetscCall(DSVectors(eps->ds,DS_MAT_X,NULL,NULL));
120
121 /* V = V * Z */
122
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 2983 PetscCall(DSGetMat(eps->ds,DS_MAT_X,&Z));
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 2983 PetscCall(BVMultInPlace(eps->V,Z,0,eps->nconv));
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 2983 PetscCall(DSRestoreMat(eps->ds,DS_MAT_X,&Z));
125
126 /* Purify eigenvectors */
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 2983 if (eps->purify) PetscCall(EPS_Purify(eps,eps->nconv));
128
129 /* Fix eigenvectors if balancing was used */
130
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 2983 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) */
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 2983 if (eps->purify || (eps->balance!=EPS_BALANCE_NONE && eps->D)) PetscCall(BVNormalize(eps->V,eps->eigi));
140
141 /* left eigenvectors */
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 2983 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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 557 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 44652 PetscErrorCode EPSSetWorkVecs(EPS eps,PetscInt nw)
189 {
190 44652 Vec t;
191
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 44652 PetscFunctionBegin;
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 44652 PetscValidHeaderSpecific(eps,EPS_CLASSID,1);
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 44652 PetscValidLogicalCollectiveInt(eps,nw,2);
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 44652 PetscCheck(nw>0,PetscObjectComm((PetscObject)eps),PETSC_ERR_ARG_OUTOFRANGE,"nw must be > 0: nw = %" PetscInt_FMT,nw);
196
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 44652 if (eps->nwork < nw) {
197
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 7406 PetscCall(VecDestroyVecs(eps->nwork,&eps->work));
198 7406 eps->nwork = nw;
199
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 7406 PetscCall(BVGetColumn(eps->V,0,&t));
200
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 7406 PetscCall(VecDuplicateVecs(t,nw,&eps->work));
201
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 7406 PetscCall(BVRestoreColumn(eps->V,0,&t));
202 }
203
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 8821 PetscFunctionReturn(PETSC_SUCCESS);
204 }
205
206 /*
207 EPSSetWhichEigenpairs_Default - Sets the default value for which,
208 depending on the ST.
209 */
210 1238 PetscErrorCode EPSSetWhichEigenpairs_Default(EPS eps)
211 {
212 1238 PetscBool target;
213
214
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 1238 PetscFunctionBegin;
215
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 1238 PetscCall(PetscObjectTypeCompareAny((PetscObject)eps->st,&target,STSINVERT,STCAYLEY,""));
216
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 1238 if (target) eps->which = EPS_TARGET_MAGNITUDE;
217 1006 else eps->which = EPS_LARGEST_MAGNITUDE;
218
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 232 PetscFunctionReturn(PETSC_SUCCESS);
219 }
220
221 /*
222 EPSConvergedRelative - Checks convergence relative to the eigenvalue.
223 */
224 469544 PetscErrorCode EPSConvergedRelative(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
225 {
226 469544 PetscReal w;
227
228
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 469544 PetscFunctionBegin;
229 469544 w = SlepcAbsEigenvalue(eigr,eigi);
230
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 469544 *errest = (w!=0.0)? res/w: PETSC_MAX_REAL;
231
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 469544 PetscFunctionReturn(PETSC_SUCCESS);
232 }
233
234 /*
235 EPSConvergedAbsolute - Checks convergence absolutely.
236 */
237 27554 PetscErrorCode EPSConvergedAbsolute(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
238 {
239
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 27554 PetscFunctionBegin;
240 27554 *errest = res;
241
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 27554 PetscFunctionReturn(PETSC_SUCCESS);
242 }
243
244 /*
245 EPSConvergedNorm - Checks convergence relative to the eigenvalue and
246 the matrix norms.
247 */
248 56321 PetscErrorCode EPSConvergedNorm(EPS eps,PetscScalar eigr,PetscScalar eigi,PetscReal res,PetscReal *errest,void *ctx)
249 {
250 56321 PetscReal w;
251
252
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 56321 PetscFunctionBegin;
253 56321 w = SlepcAbsEigenvalue(eigr,eigi);
254 56321 *errest = res / (eps->nrma + w*eps->nrmb);
255
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 56321 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 437090 PetscErrorCode EPSStoppingBasic(EPS eps,PetscInt its,PetscInt max_it,PetscInt nconv,PetscInt nev,EPSConvergedReason *reason,void *ctx)
287 {
288
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 437090 PetscFunctionBegin;
289 437090 *reason = EPS_CONVERGED_ITERATING;
290
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 437090 if (nconv >= nev) {
291
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 8253 PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: %" PetscInt_FMT " eigenpairs converged at iteration %" PetscInt_FMT "\n",nconv,its));
292 8253 *reason = EPS_CONVERGED_TOL;
293
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 428837 } 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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 86600 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 (ignored here)
311 . nev - number of requested eigenpairs (ignored here)
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 9005 PetscErrorCode EPSStoppingThreshold(EPS eps,PetscInt its,PetscInt max_it,PetscInt nconv,PetscInt nev,EPSConvergedReason *reason,void *ctx)
331 {
332 9005 PetscReal thres,firstev,lastev;
333 9005 PetscBool magnit,rel;
334 9005 EPSWhich which;
335
336
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 9005 PetscFunctionBegin;
337 9005 *reason = EPS_CONVERGED_ITERATING;
338 9005 firstev = ((EPSStoppingCtx)ctx)->firstev;
339 9005 lastev = ((EPSStoppingCtx)ctx)->lastev;
340 9005 thres = ((EPSStoppingCtx)ctx)->thres;
341 9005 rel = ((EPSStoppingCtx)ctx)->threlative;
342 9005 which = ((EPSStoppingCtx)ctx)->which;
343 9005 magnit = (which==EPS_SMALLEST_MAGNITUDE || which==EPS_LARGEST_MAGNITUDE || which==EPS_TARGET_MAGNITUDE)? PETSC_TRUE: PETSC_FALSE;
344
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 9005 if (nconv && magnit && which==EPS_TARGET_MAGNITUDE && ((rel && ((thres>1.0 && lastev>thres*firstev) || (thres<1.0 && lastev<thres*firstev))) || (!rel && lastev>thres))) {
345
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 30 if (!rel) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the eigenvalue magnitude %g is above the threshold %g\n",(double)lastev,(double)thres));
346
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 20 else if (thres>1.0) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is above the threshold %g\n",(double)lastev,(double)firstev,(double)thres));
347
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 10 else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is below the threshold %g\n",(double)lastev,(double)firstev,(double)thres));
348 30 *reason = EPS_CONVERGED_TOL;
349
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 8975 } else if (nconv && magnit && ((which==EPS_LARGEST_MAGNITUDE && ((rel && lastev<thres*firstev) || (!rel && lastev<thres))) || (which==EPS_SMALLEST_MAGNITUDE && lastev>thres))) {
350
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 112 if (which==EPS_SMALLEST_MAGNITUDE) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the eigenvalue magnitude %g is above the threshold %g\n",(double)lastev,(double)thres));
351
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 42 else if (!rel) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the eigenvalue magnitude %g is below the threshold %g\n",(double)lastev,(double)thres));
352
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 42 else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: the ratio %g/%g is below the threshold %g\n",(double)lastev,(double)firstev,(double)thres));
353 112 *reason = EPS_CONVERGED_TOL;
354
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 8863 } else if (nconv && !magnit && ((which==EPS_LARGEST_REAL && lastev<thres) || (which==EPS_SMALLEST_REAL && lastev>thres))) {
355 ✗ if (which==EPS_LARGEST_REAL) PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: eigenvalue %g is below the threshold %g\n",(double)lastev,(double)thres));
356 ✗ else PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: eigenvalue %g is above the threshold %g\n",(double)lastev,(double)thres));
357 ✗ *reason = EPS_CONVERGED_TOL;
358
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 8863 } else if (nev && nconv >= nev) {
359
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 4 PetscCall(PetscInfo(eps,"Linear eigensolver finished successfully: %" PetscInt_FMT " eigenpairs converged at iteration %" PetscInt_FMT "\n",nconv,its));
360 4 *reason = EPS_CONVERGED_TOL;
361
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 8859 } else if (its >= max_it) {
362 ✗ *reason = EPS_DIVERGED_ITS;
363 ✗ PetscCall(PetscInfo(eps,"Linear eigensolver iteration reached maximum number of iterations (%" PetscInt_FMT ")\n",its));
364 }
365
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 1841 PetscFunctionReturn(PETSC_SUCCESS);
366 }
367
368 /*
369 EPSComputeRitzVector - Computes the current Ritz vector.
370
371 Simple case (complex scalars or real scalars with Zi=NULL):
372 x = V*Zr (V is a basis of nv vectors, Zr has length nv)
373
374 Split case:
375 x = V*Zr y = V*Zi (Zr and Zi have length nv)
376 */
377 4191 PetscErrorCode EPSComputeRitzVector(EPS eps,PetscScalar *Zr,PetscScalar *Zi,BV V,Vec x,Vec y)
378 {
379 4191 PetscInt l,k;
380 4191 PetscReal norm;
381 #if !defined(PETSC_USE_COMPLEX)
382 2060 Vec z;
383 #endif
384
385
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 4191 PetscFunctionBegin;
386 /* compute eigenvector */
387
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 4191 PetscCall(BVGetActiveColumns(V,&l,&k));
388
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 4191 PetscCall(BVSetActiveColumns(V,0,k));
389
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 4191 PetscCall(BVMultVec(V,1.0,0.0,x,Zr));
390
391 /* purify eigenvector if necessary */
392
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 4191 if (eps->purify) {
393
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 265 PetscCall(STApply(eps->st,x,y));
394
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 265 if (eps->ishermitian) PetscCall(BVNormVec(eps->V,y,NORM_2,&norm));
395
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 145 else PetscCall(VecNorm(y,NORM_2,&norm));
396
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 265 PetscCall(VecScale(y,1.0/norm));
397
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 265 PetscCall(VecCopy(y,x));
398 }
399 /* fix eigenvector if balancing is used */
400
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 4191 if (!eps->ishermitian && eps->balance!=EPS_BALANCE_NONE && eps->D) PetscCall(VecPointwiseDivide(x,x,eps->D));
401 #if !defined(PETSC_USE_COMPLEX)
402 /* compute imaginary part of eigenvector */
403
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 2060 if (Zi) {
404
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 125 PetscCall(BVMultVec(V,1.0,0.0,y,Zi));
405
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 125 if (eps->ispositive) {
406 ✗ PetscCall(BVCreateVec(V,&z));
407 ✗ PetscCall(STApply(eps->st,y,z));
408 ✗ PetscCall(VecNorm(z,NORM_2,&norm));
409 ✗ PetscCall(VecScale(z,1.0/norm));
410 ✗ PetscCall(VecCopy(z,y));
411 ✗ PetscCall(VecDestroy(&z));
412 }
413
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 125 if (eps->balance!=EPS_BALANCE_NONE && eps->D) PetscCall(VecPointwiseDivide(y,y,eps->D));
414 } else
415 #endif
416
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 4066 PetscCall(VecSet(y,0.0));
417
418 /* normalize eigenvectors (when using balancing) */
419
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 4191 if (eps->balance!=EPS_BALANCE_NONE && eps->D) {
420 #if !defined(PETSC_USE_COMPLEX)
421
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 60 if (Zi) PetscCall(VecNormalizeComplex(x,y,PETSC_TRUE,NULL));
422 else
423 #endif
424
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 80 PetscCall(VecNormalize(x,NULL));
425 }
426
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 4191 PetscCall(BVSetActiveColumns(V,l,k));
427
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 803 PetscFunctionReturn(PETSC_SUCCESS);
428 }
429
430 /*
431 EPSBuildBalance_Krylov - uses a Krylov subspace method to compute the
432 diagonal matrix to be applied for balancing in non-Hermitian problems.
433 */
434 130 PetscErrorCode EPSBuildBalance_Krylov(EPS eps)
435 {
436 130 Vec z,p,r;
437 130 PetscInt i,j;
438 130 PetscReal norma;
439 130 PetscScalar *pz,*pD;
440 130 const PetscScalar *pr,*pp;
441 130 PetscRandom rand;
442
443
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 130 PetscFunctionBegin;
444
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 130 PetscCall(EPSSetWorkVecs(eps,3));
445
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 130 PetscCall(BVGetRandomContext(eps->V,&rand));
446 130 r = eps->work[0];
447 130 p = eps->work[1];
448 130 z = eps->work[2];
449
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 130 PetscCall(VecSet(eps->D,1.0));
450
451
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 780 for (j=0;j<eps->balance_its;j++) {
452
453 /* Build a random vector of +-1's */
454
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 650 PetscCall(VecSetRandom(z,rand));
455
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 650 PetscCall(VecGetArray(z,&pz));
456
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 50950 for (i=0;i<eps->nloc;i++) {
457
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 50300 if (PetscRealPart(pz[i])<0.5) pz[i]=-1.0;
458 24425 else pz[i]=1.0;
459 }
460
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 650 PetscCall(VecRestoreArray(z,&pz));
461
462 /* Compute p=DA(D\z) */
463
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 650 PetscCall(VecPointwiseDivide(r,z,eps->D));
464
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 650 PetscCall(STApply(eps->st,r,p));
465
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 650 PetscCall(VecPointwiseMult(p,p,eps->D));
466
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 650 if (eps->balance == EPS_BALANCE_TWOSIDE) {
467
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 400 if (j==0) {
468 /* Estimate the matrix inf-norm */
469
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 80 PetscCall(VecAbs(p));
470
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 80 PetscCall(VecMax(p,NULL,&norma));
471 }
472 /* Compute r=D\(A'Dz) */
473
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 400 PetscCall(VecPointwiseMult(z,z,eps->D));
474
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 400 PetscCall(STApplyHermitianTranspose(eps->st,z,r));
475
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 400 PetscCall(VecPointwiseDivide(r,r,eps->D));
476 }
477
478 /* Adjust values of D */
479
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 650 PetscCall(VecGetArrayRead(r,&pr));
480
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 650 PetscCall(VecGetArrayRead(p,&pp));
481
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 650 PetscCall(VecGetArray(eps->D,&pD));
482
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 50950 for (i=0;i<eps->nloc;i++) {
483
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 50300 if (eps->balance == EPS_BALANCE_TWOSIDE) {
484
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 29000 if (PetscAbsScalar(pp[i])>eps->balance_cutoff*norma && pr[i]!=0.0)
485 28240 pD[i] *= PetscSqrtReal(PetscAbsScalar(pr[i]/pp[i]));
486 } else {
487
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 21300 if (pp[i]!=0.0) pD[i] /= PetscAbsScalar(pp[i]);
488 }
489 }
490
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 650 PetscCall(VecRestoreArrayRead(r,&pr));
491
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 650 PetscCall(VecRestoreArrayRead(p,&pp));
492
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 650 PetscCall(VecRestoreArray(eps->D,&pD));
493 }
494
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 26 PetscFunctionReturn(PETSC_SUCCESS);
495 }
496