Actual source code: stset.c
1: /*
2: Routines to set ST methods and options.
4: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
5: SLEPc - Scalable Library for Eigenvalue Problem Computations
6: Copyright (c) 2002-2011, Universitat Politecnica de Valencia, Spain
8: This file is part of SLEPc.
9:
10: SLEPc is free software: you can redistribute it and/or modify it under the
11: terms of version 3 of the GNU Lesser General Public License as published by
12: the Free Software Foundation.
14: SLEPc is distributed in the hope that it will be useful, but WITHOUT ANY
15: WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
16: FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for
17: more details.
19: You should have received a copy of the GNU Lesser General Public License
20: along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
21: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
22: */
24: #include <private/stimpl.h> /*I "slepcst.h" I*/
26: PetscBool STRegisterAllCalled = PETSC_FALSE;
27: PetscFList STList = 0;
31: /*@C
32: STSetType - Builds ST for a particular spectral transformation.
34: Logically Collective on ST
36: Input Parameter:
37: + st - the spectral transformation context.
38: - type - a known type
40: Options Database Key:
41: . -st_type <type> - Sets ST type
43: Use -help for a list of available transformations
45: Notes:
46: See "slepc/include/slepcst.h" for available transformations
48: Normally, it is best to use the EPSSetFromOptions() command and
49: then set the ST type from the options database rather than by using
50: this routine. Using the options database provides the user with
51: maximum flexibility in evaluating the many different transformations.
53: Level: intermediate
55: .seealso: EPSSetType()
57: @*/
58: PetscErrorCode STSetType(ST st,const STType type)
59: {
60: PetscErrorCode ierr,(*r)(ST);
61: PetscBool match;
67: PetscTypeCompare((PetscObject)st,type,&match);
68: if (match) return(0);
70: PetscFListFind(STList,((PetscObject)st)->comm,type,PETSC_TRUE,(void (**)(void))&r);
71: if (!r) SETERRQ1(((PetscObject)st)->comm,PETSC_ERR_ARG_UNKNOWN_TYPE,"Unable to find requested ST type %s",type);
73: if (st->ops->destroy) { (*st->ops->destroy)(st); }
74: PetscMemzero(st->ops,sizeof(struct _STOps));
76: st->setupcalled = 0;
77: PetscObjectChangeTypeName((PetscObject)st,type);
78: (*r)(st);
79: return(0);
80: }
84: /*@C
85: STGetType - Gets the ST type name (as a string) from the ST context.
87: Not Collective
89: Input Parameter:
90: . st - the spectral transformation context
92: Output Parameter:
93: . name - name of the spectral transformation
95: Level: intermediate
97: .seealso: STSetType()
99: @*/
100: PetscErrorCode STGetType(ST st,const STType *type)
101: {
105: *type = ((PetscObject)st)->type_name;
106: return(0);
107: }
111: /*@
112: STSetFromOptions - Sets ST options from the options database.
113: This routine must be called before STSetUp() if the user is to be
114: allowed to set the type of transformation.
116: Collective on ST
118: Input Parameter:
119: . st - the spectral transformation context
121: Level: beginner
122: @*/
123: PetscErrorCode STSetFromOptions(ST st)
124: {
126: PetscInt i;
127: PetscScalar s;
128: char type[256];
129: PetscBool flg;
130: const char *mode_list[3] = {"copy","inplace","shell"};
131: const char *structure_list[3] = {"same","different","subset"};
135: if (!STRegisterAllCalled) { STRegisterAll(PETSC_NULL); }
136: PetscOptionsBegin(((PetscObject)st)->comm,((PetscObject)st)->prefix,"Spectral Transformation (ST) Options","ST");
137: PetscOptionsList("-st_type","Spectral Transformation type","STSetType",STList,(char*)(((PetscObject)st)->type_name?((PetscObject)st)->type_name:STSHIFT),type,256,&flg);
138: if (flg) {
139: STSetType(st,type);
140: }
141: /*
142: Set the type if it was never set.
143: */
144: if (!((PetscObject)st)->type_name) {
145: STSetType(st,STSHIFT);
146: }
148: PetscOptionsScalar("-st_shift","Value of the shift","STSetShift",st->sigma,&s,&flg);
149: if (flg) { STSetShift(st,s); }
151: PetscOptionsEList("-st_matmode","Shift matrix mode","STSetMatMode",mode_list,3,mode_list[st->shift_matrix],&i,&flg);
152: if (flg) { st->shift_matrix = (STMatMode)i; }
154: PetscOptionsEList("-st_matstructure","Shift nonzero pattern","STSetMatStructure",structure_list,3,structure_list[st->str],&i,&flg);
155: if (flg) {
156: switch (i) {
157: case 0: STSetMatStructure(st,SAME_NONZERO_PATTERN); break;
158: case 1: STSetMatStructure(st,DIFFERENT_NONZERO_PATTERN); break;
159: case 2: STSetMatStructure(st,SUBSET_NONZERO_PATTERN); break;
160: }
161: }
162:
163: if (st->ops->setfromoptions) {
164: (*st->ops->setfromoptions)(st);
165: }
166: PetscObjectProcessOptionsHandlers((PetscObject)st);
167: PetscOptionsEnd();
168: KSPSetFromOptions(st->ksp);
169: return(0);
170: }
174: /*@
175: STSetMatStructure - Sets an internal MatStructure attribute to
176: indicate which is the relation of the sparsity pattern of the two matrices
177: A and B constituting the generalized eigenvalue problem.
179: Logically Collective on ST
181: Input Parameters:
182: + st - the spectral transformation context
183: - str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or
184: SUBSET_NONZERO_PATTERN
186: Options Database Key:
187: . -st_matstructure <str> - Indicates the structure flag, where <str> is one
188: of 'same' (A and B have the same nonzero pattern), 'different' (A
189: and B have different nonzero pattern) or 'subset' (B's nonzero
190: pattern is a subset of A's).
192: Notes:
193: By default, the sparsity patterns are assumed to be different. If the
194: patterns are equal or a subset then it is recommended to set this attribute
195: for efficiency reasons (in particular, for internal MatAXPY() operations).
197: This function has no effect in the case of standard eigenproblems.
198:
199: Level: advanced
201: .seealso: STSetOperators(), MatAXPY()
202: @*/
203: PetscErrorCode STSetMatStructure(ST st,MatStructure str)
204: {
208: switch (str) {
209: case SAME_NONZERO_PATTERN:
210: case DIFFERENT_NONZERO_PATTERN:
211: case SUBSET_NONZERO_PATTERN:
212: st->str = str;
213: break;
214: default:
215: SETERRQ(((PetscObject)st)->comm,PETSC_ERR_ARG_OUTOFRANGE,"Invalid matrix structure flag");
216: }
217: return(0);
218: }
222: /*@
223: STGetMatStructure - Gets the internal MatStructure attribute to
224: indicate which is the relation of the sparsity pattern of the two matrices
225: A and B constituting the generalized eigenvalue problem.
227: Not Collective
229: Input Parameters:
230: . st - the spectral transformation context
232: Output Parameters:
233: . str - either SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN or
234: SUBSET_NONZERO_PATTERN
236: Note:
237: This function has no effect in the case of standard eigenproblems.
239: Level: advanced
241: .seealso: STSetMatStructure(), STSetOperators(), MatAXPY()
242: @*/
243: PetscErrorCode STGetMatStructure(ST st,MatStructure *str)
244: {
248: *str = st->str;
249: return(0);
250: }
254: /*@
255: STSetMatMode - Sets a flag to indicate how the matrix is
256: being shifted in the shift-and-invert and Cayley spectral transformations.
258: Logically Collective on ST
260: Input Parameters:
261: + st - the spectral transformation context
262: - mode - the mode flag, one of ST_MATMODE_COPY,
263: ST_MATMODE_INPLACE or ST_MATMODE_SHELL
265: Options Database Key:
266: . -st_matmode <mode> - Indicates the mode flag, where <mode> is one of
267: 'copy', 'inplace' or 'shell' (see explanation below).
269: Notes:
270: By default (ST_MATMODE_COPY), a copy of matrix A is made and then
271: this copy is shifted explicitly, e.g. A <- (A - s B).
273: With ST_MATMODE_INPLACE, the original matrix A is shifted at
274: STSetUp() and unshifted at the end of the computations. With respect to
275: the previous one, this mode avoids a copy of matrix A. However, a
276: backdraw is that the recovered matrix might be slightly different
277: from the original one (due to roundoff).
279: With ST_MATMODE_SHELL, the solver works with an implicit shell
280: matrix that represents the shifted matrix. This mode is the most efficient
281: in creating the shifted matrix but it places serious limitations to the
282: linear solves performed in each iteration of the eigensolver (typically,
283: only interative solvers with Jacobi preconditioning can be used).
284:
285: In the case of generalized problems, in the two first modes the matrix
286: A - s B has to be computed explicitly. The efficiency of this computation
287: can be controlled with STSetMatStructure().
289: Level: intermediate
291: .seealso: STSetOperators(), STSetMatStructure(), STGetMatMode(), STMatMode
292: @*/
293: PetscErrorCode STSetMatMode(ST st,STMatMode mode)
294: {
298: st->shift_matrix = mode;
299: return(0);
300: }
304: /*@C
305: STGetMatMode - Gets a flag that indicates how the matrix is being
306: shifted in the shift-and-invert and Cayley spectral transformations.
308: Not Collective
310: Input Parameter:
311: . st - the spectral transformation context
313: Output Parameter:
314: . mode - the mode flag
316: Level: intermediate
318: .seealso: STSetMatMode(), STMatMode
319: @*/
320: PetscErrorCode STGetMatMode(ST st,STMatMode *mode)
321: {
325: *mode = st->shift_matrix;
326: return(0);
327: }