Actual source code: test1.c
1: /*
2: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
3: SLEPc - Scalable Library for Eigenvalue Problem Computations
4: Copyright (c) 2002-2011, Universitat Politecnica de Valencia, Spain
6: This file is part of SLEPc.
7:
8: SLEPc is free software: you can redistribute it and/or modify it under the
9: terms of version 3 of the GNU Lesser General Public License as published by
10: the Free Software Foundation.
12: SLEPc is distributed in the hope that it will be useful, but WITHOUT ANY
13: WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
14: FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for
15: more details.
17: You should have received a copy of the GNU Lesser General Public License
18: along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
19: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
20: */
22: static char help[] = "Test the solution of a SVD without calling SVDSetFromOptions (based on ex8.c).\n\n"
23: "The command line options are:\n"
24: " -n <n>, where <n> = matrix dimension.\n"
25: " -type <svd_type> = svd type to test.\n\n";
27: #include <slepcsvd.h>
29: /*
30: This example computes the singular values of an nxn Grcar matrix,
31: which is a nonsymmetric Toeplitz matrix:
33: | 1 1 1 1 |
34: | -1 1 1 1 1 |
35: | -1 1 1 1 1 |
36: | . . . . . |
37: A = | . . . . . |
38: | -1 1 1 1 1 |
39: | -1 1 1 1 |
40: | -1 1 1 |
41: | -1 1 |
43: */
47: int main(int argc,char **argv)
48: {
49: Mat A; /* Grcar matrix */
50: SVD svd; /* singular value solver context */
51: PetscInt N=30,Istart,Iend,i,col[5],nconv1,nconv2;
52: PetscScalar value[] = { -1, 1, 1, 1, 1 };
53: PetscReal sigma_1,sigma_n;
54: char svdtype[30] = "cross", epstype[30] = "";
55: PetscBool flg;
56: EPS eps;
59: SlepcInitialize(&argc,&argv,(char*)0,help);
61: PetscOptionsGetInt(PETSC_NULL,"-n",&N,PETSC_NULL);
62: PetscOptionsGetString(PETSC_NULL,"-type",svdtype,30,PETSC_NULL);
63: PetscOptionsGetString(PETSC_NULL,"-epstype",epstype,30,&flg);
64: PetscPrintf(PETSC_COMM_WORLD,"\nEstimate the condition number of a Grcar matrix, n=%D",N);
65: PetscPrintf(PETSC_COMM_WORLD,"\nSVD type: %s",svdtype);
66: if (flg) {
67: PetscPrintf(PETSC_COMM_WORLD,"\nEPS type: %s",epstype);
68: }
69: PetscPrintf(PETSC_COMM_WORLD,"\n\n");
70:
72: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
73: Generate the matrix
74: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
76: MatCreate(PETSC_COMM_WORLD,&A);
77: MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
78: MatSetFromOptions(A);
80: MatGetOwnershipRange(A,&Istart,&Iend);
81: for (i=Istart;i<Iend;i++) {
82: col[0]=i-1; col[1]=i; col[2]=i+1; col[3]=i+2; col[4]=i+3;
83: if (i==0) {
84: MatSetValues(A,1,&i,4,col+1,value+1,INSERT_VALUES);
85: } else {
86: MatSetValues(A,1,&i,PetscMin(5,N-i+1),col,value,INSERT_VALUES);
87: }
88: }
90: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
91: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
93: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
94: Create the singular value solver and set the solution method
95: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
97: /*
98: Create singular value context
99: */
100: SVDCreate(PETSC_COMM_WORLD,&svd);
102: /*
103: Set operator
104: */
105: SVDSetOperator(svd,A);
107: /*
108: Set solver parameters at runtime
109: */
110: SVDSetType(svd,svdtype);
111: if (flg) {
112: PetscTypeCompare((PetscObject)svd,SVDCROSS,&flg);
113: if (flg) {
114: SVDCrossGetEPS(svd,&eps);
115: EPSSetType(eps,epstype);
116: }
117: PetscTypeCompare((PetscObject)svd,SVDCYCLIC,&flg);
118: if (flg) {
119: SVDCyclicGetEPS(svd,&eps);
120: EPSSetType(eps,epstype);
121: }
122: }
123: SVDSetDimensions(svd,1,PETSC_IGNORE,PETSC_IGNORE);
125: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
126: Solve the eigensystem
127: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
129: /*
130: First request an eigenvalue from one end of the spectrum
131: */
132: SVDSetWhichSingularTriplets(svd,SVD_LARGEST);
133: SVDSolve(svd);
134: /*
135: Get number of converged singular values
136: */
137: SVDGetConverged(svd,&nconv1);
138: /*
139: Get converged singular values: largest singular value is stored in sigma_1.
140: In this example, we are not interested in the singular vectors
141: */
142: if (nconv1 > 0) {
143: SVDGetSingularTriplet(svd,0,&sigma_1,PETSC_NULL,PETSC_NULL);
144: } else {
145: PetscPrintf(PETSC_COMM_WORLD," Unable to compute large singular value!\n\n");
146: }
148: /*
149: Request an eigenvalue from the other end of the spectrum
150: */
151: SVDSetWhichSingularTriplets(svd,SVD_SMALLEST);
152: SVDSolve(svd);
153: /*
154: Get number of converged eigenpairs
155: */
156: SVDGetConverged(svd,&nconv2);
157: /*
158: Get converged singular values: smallest singular value is stored in sigma_n.
159: As before, we are not interested in the singular vectors
160: */
161: if (nconv2 > 0) {
162: SVDGetSingularTriplet(svd,0,&sigma_n,PETSC_NULL,PETSC_NULL);
163: } else {
164: PetscPrintf(PETSC_COMM_WORLD," Unable to compute small singular value!\n\n");
165: }
167: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
168: Display solution and clean up
169: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
170: if (nconv1 > 0 && nconv2 > 0) {
171: PetscPrintf(PETSC_COMM_WORLD," Computed singular values: sigma_1=%6F, sigma_n=%6F\n",sigma_1,sigma_n);
172: PetscPrintf(PETSC_COMM_WORLD," Estimated condition number: sigma_1/sigma_n=%6F\n\n",sigma_1/sigma_n);
173: }
174:
175: /*
176: Free work space
177: */
178: SVDDestroy(&svd);
179: MatDestroy(&A);
180: SlepcFinalize();
181: return 0;
182: }