1: /*
2: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
3: SLEPc - Scalable Library for Eigenvalue Problem Computations
4: Copyright (c) 2002-2019, 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: */
10: /*
11: Routines to set ST methods and options
12: */
14: #include <slepc/private/stimpl.h> /*I "slepcst.h" I*/
16: PetscBool STRegisterAllCalled = PETSC_FALSE;
17: PetscFunctionList STList = 0;
19: /*@C
20: STSetType - Builds ST for a particular spectral transformation.
22: Logically Collective on st
24: Input Parameter:
25: + st - the spectral transformation context.
26: - type - a known type
28: Options Database Key:
29: . -st_type <type> - Sets ST type
31: Use -help for a list of available transformations
33: Notes:
34: See "slepc/include/slepcst.h" for available transformations
36: Normally, it is best to use the EPSSetFromOptions() command and
37: then set the ST type from the options database rather than by using
38: this routine. Using the options database provides the user with
39: maximum flexibility in evaluating the many different transformations.
41: Level: beginner
43: .seealso: EPSSetType()
45: @*/
46: PetscErrorCode STSetType(ST st,STType type) 47: {
48: PetscErrorCode ierr,(*r)(ST);
49: PetscBool match;
55: PetscObjectTypeCompare((PetscObject)st,type,&match);
56: if (match) return(0);
58: PetscFunctionListFind(STList,type,&r);
59: if (!r) SETERRQ1(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_UNKNOWN_TYPE,"Unable to find requested ST type %s",type);
61: if (st->ops->destroy) { (*st->ops->destroy)(st); }
62: PetscMemzero(st->ops,sizeof(struct _STOps));
64: st->state = ST_STATE_INITIAL;
65: PetscObjectChangeTypeName((PetscObject)st,type);
66: (*r)(st);
67: return(0);
68: }
70: /*@C
71: STGetType - Gets the ST type name (as a string) from the ST context.
73: Not Collective
75: Input Parameter:
76: . st - the spectral transformation context
78: Output Parameter:
79: . name - name of the spectral transformation
81: Level: intermediate
83: .seealso: STSetType()
85: @*/
86: PetscErrorCode STGetType(ST st,STType *type) 87: {
91: *type = ((PetscObject)st)->type_name;
92: return(0);
93: }
95: /*@
96: STSetFromOptions - Sets ST options from the options database.
97: This routine must be called before STSetUp() if the user is to be
98: allowed to set the type of transformation.
100: Collective on st
102: Input Parameter:
103: . st - the spectral transformation context
105: Level: beginner
106: @*/
107: PetscErrorCode STSetFromOptions(ST st)108: {
110: PetscScalar s;
111: char type[256];
112: PetscBool flg;
113: const char *structure_list[3] = {"different","subset","same"};
114: STMatMode mode;
115: MatStructure mstr;
119: STRegisterAll();
120: PetscObjectOptionsBegin((PetscObject)st);
121: PetscOptionsFList("-st_type","Spectral transformation","STSetType",STList,(char*)(((PetscObject)st)->type_name?((PetscObject)st)->type_name:STSHIFT),type,256,&flg);
122: if (flg) {
123: STSetType(st,type);
124: } else if (!((PetscObject)st)->type_name) {
125: STSetType(st,STSHIFT);
126: }
128: PetscOptionsScalar("-st_shift","Value of the shift","STSetShift",st->sigma,&s,&flg);
129: if (flg) { STSetShift(st,s); }
131: PetscOptionsEnum("-st_matmode","Matrix mode for transformed matrices","STSetMatMode",STMatModes,(PetscEnum)st->matmode,(PetscEnum*)&mode,&flg);
132: if (flg) { STSetMatMode(st,mode); }
134: PetscOptionsEList("-st_matstructure","Relation of the sparsity pattern of the matrices","STSetMatStructure",structure_list,3,structure_list[st->str],(PetscInt*)&mstr,&flg);
135: if (flg) { STSetMatStructure(st,mstr); }
137: PetscOptionsBool("-st_transform","Whether transformed matrices are computed or not","STSetTransform",st->transform,&st->transform,&flg);
139: if (st->ops->setfromoptions) {
140: (*st->ops->setfromoptions)(PetscOptionsObject,st);
141: }
142: PetscObjectProcessOptionsHandlers(PetscOptionsObject,(PetscObject)st);
143: PetscOptionsEnd();
145: if (st->usesksp) {
146: STSetDefaultKSP(st);
147: KSPSetFromOptions(st->ksp);
148: }
149: return(0);
150: }
152: /*@
153: STSetMatStructure - Sets an internal MatStructure attribute to
154: indicate which is the relation of the sparsity pattern of all ST matrices.
156: Logically Collective on st
158: Input Parameters:
159: + st - the spectral transformation context
160: - str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or
161: SUBSET_NONZERO_PATTERN
163: Options Database Key:
164: . -st_matstructure <str> - Indicates the structure flag, where <str> is one
165: of 'same' (matrices have the same nonzero pattern), 'different'
166: (different nonzero pattern) or 'subset' (pattern is a subset of the
167: first one).
169: Notes:
170: By default, the sparsity patterns are assumed to be different. If the
171: patterns are equal or a subset then it is recommended to set this attribute
172: for efficiency reasons (in particular, for internal MatAXPY() operations).
174: This function has no effect in the case of standard eigenproblems.
176: Level: advanced
178: .seealso: STSetMatrices(), MatAXPY()
179: @*/
180: PetscErrorCode STSetMatStructure(ST st,MatStructure str)181: {
185: switch (str) {
186: case SAME_NONZERO_PATTERN:
187: case DIFFERENT_NONZERO_PATTERN:
188: case SUBSET_NONZERO_PATTERN:
189: st->str = str;
190: break;
191: default:192: SETERRQ(PetscObjectComm((PetscObject)st),PETSC_ERR_ARG_OUTOFRANGE,"Invalid matrix structure flag");
193: }
194: return(0);
195: }
197: /*@
198: STGetMatStructure - Gets the internal MatStructure attribute to
199: indicate which is the relation of the sparsity pattern of the matrices.
201: Not Collective
203: Input Parameters:
204: . st - the spectral transformation context
206: Output Parameters:
207: . str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or
208: SUBSET_NONZERO_PATTERN
210: Level: advanced
212: .seealso: STSetMatStructure(), STSetMatrices(), MatAXPY()
213: @*/
214: PetscErrorCode STGetMatStructure(ST st,MatStructure *str)215: {
219: *str = st->str;
220: return(0);
221: }
223: /*@
224: STSetMatMode - Sets a flag to indicate how the transformed matrices are
225: being stored in the spectral transformations.
227: Logically Collective on st
229: Input Parameters:
230: + st - the spectral transformation context
231: - mode - the mode flag, one of ST_MATMODE_COPY,
232: ST_MATMODE_INPLACE, or ST_MATMODE_SHELL234: Options Database Key:
235: . -st_matmode <mode> - Indicates the mode flag, where <mode> is one of
236: 'copy', 'inplace', 'shell' (see explanation below).
238: Notes:
239: By default (ST_MATMODE_COPY), a copy of matrix A is made and then
240: this copy is modified explicitly, e.g. A <- (A - s B).
242: With ST_MATMODE_INPLACE, the original matrix A is modified at STSetUp()
243: and changes are reverted at the end of the computations. With respect to
244: the previous one, this mode avoids a copy of matrix A. However, a
245: drawback is that the recovered matrix might be slightly different
246: from the original one (due to roundoff).
248: With ST_MATMODE_SHELL, the solver works with an implicit shell
249: matrix that represents the shifted matrix. This mode is the most efficient
250: in creating the shifted matrix but it places serious limitations to the
251: linear solves performed in each iteration of the eigensolver (typically,
252: only interative solvers with Jacobi preconditioning can be used).
254: In the two first modes the efficiency of the computation
255: can be controlled with STSetMatStructure().
257: Level: intermediate
259: .seealso: STSetMatrices(), STSetMatStructure(), STGetMatMode(), STMatMode260: @*/
261: PetscErrorCode STSetMatMode(ST st,STMatMode mode)262: {
266: if (st->matmode != mode) {
267: st->matmode = mode;
268: st->state = ST_STATE_INITIAL;
269: }
270: return(0);
271: }
273: /*@
274: STGetMatMode - Gets a flag that indicates how the transformed matrices
275: are stored in spectral transformations.
277: Not Collective
279: Input Parameter:
280: . st - the spectral transformation context
282: Output Parameter:
283: . mode - the mode flag
285: Level: intermediate
287: .seealso: STSetMatMode(), STMatMode288: @*/
289: PetscErrorCode STGetMatMode(ST st,STMatMode *mode)290: {
294: *mode = st->matmode;
295: return(0);
296: }
298: /*@
299: STSetTransform - Sets a flag to indicate whether the transformed matrices are
300: computed or not.
302: Logically Collective on st
304: Input Parameters:
305: + st - the spectral transformation context
306: - flg - the boolean flag
308: Options Database Key:
309: . -st_transform <bool> - Activate/deactivate the computation of matrices.
311: Notes:
312: This flag is intended for the case of polynomial eigenproblems solved
313: via linearization. If this flag is off (default) the spectral transformation
314: is applied to the linearization (handled by the eigensolver), otherwise
315: it is applied to the original problem.
317: Level: developer
319: .seealso: STMatSolve(), STMatMult(), STSetMatStructure(), STGetTransform()
320: @*/
321: PetscErrorCode STSetTransform(ST st,PetscBool flg)322: {
326: if (st->transform != flg) {
327: st->transform = flg;
328: st->state = ST_STATE_INITIAL;
329: }
330: return(0);
331: }
333: /*@
334: STGetTransform - Gets a flag that that indicates whether the transformed
335: matrices are computed or not.
337: Not Collective
339: Input Parameter:
340: . st - the spectral transformation context
342: Output Parameter:
343: . flg - the flag
345: Level: developer
347: .seealso: STSetTransform()
348: @*/
349: PetscErrorCode STGetTransform(ST st,PetscBool *flg)350: {
354: *flg = st->transform;
355: return(0);
356: }