Actual source code: test1.c
slepc-3.11.2 2019-07-30
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: */
11: static char help[] = "Test the solution of a SVD without calling SVDSetFromOptions (based on ex8.c).\n\n"
12: "The command line options are:\n"
13: " -n <n>, where <n> = matrix dimension.\n"
14: " -type <svd_type> = svd type to test.\n\n";
16: #include <slepcsvd.h>
18: /*
19: This example computes the singular values of an nxn Grcar matrix,
20: which is a nonsymmetric Toeplitz matrix:
22: | 1 1 1 1 |
23: | -1 1 1 1 1 |
24: | -1 1 1 1 1 |
25: | . . . . . |
26: A = | . . . . . |
27: | -1 1 1 1 1 |
28: | -1 1 1 1 |
29: | -1 1 1 |
30: | -1 1 |
32: */
34: int main(int argc,char **argv)
35: {
36: Mat A; /* Grcar matrix */
37: SVD svd; /* singular value solver context */
38: PetscInt N=30,Istart,Iend,i,col[5],nconv1,nconv2;
39: PetscScalar value[] = { -1, 1, 1, 1, 1 };
40: PetscReal sigma_1,sigma_n;
41: char svdtype[30] = "cross",epstype[30] = "";
42: PetscBool flg;
43: EPS eps;
46: SlepcInitialize(&argc,&argv,(char*)0,help);if (ierr) return ierr;
48: PetscOptionsGetInt(NULL,NULL,"-n",&N,NULL);
49: PetscOptionsGetString(NULL,NULL,"-type",svdtype,30,NULL);
50: PetscOptionsGetString(NULL,NULL,"-epstype",epstype,30,&flg);
51: PetscPrintf(PETSC_COMM_WORLD,"\nEstimate the condition number of a Grcar matrix, n=%D",N);
52: PetscPrintf(PETSC_COMM_WORLD,"\n\n");
54: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
55: Generate the matrix
56: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
58: MatCreate(PETSC_COMM_WORLD,&A);
59: MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,N,N);
60: MatSetFromOptions(A);
61: MatSetUp(A);
63: MatGetOwnershipRange(A,&Istart,&Iend);
64: for (i=Istart;i<Iend;i++) {
65: col[0]=i-1; col[1]=i; col[2]=i+1; col[3]=i+2; col[4]=i+3;
66: if (i==0) {
67: MatSetValues(A,1,&i,4,col+1,value+1,INSERT_VALUES);
68: } else {
69: MatSetValues(A,1,&i,PetscMin(5,N-i+1),col,value,INSERT_VALUES);
70: }
71: }
73: MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
74: MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
76: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
77: Create the singular value solver and set the solution method
78: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
80: /*
81: Create singular value context
82: */
83: SVDCreate(PETSC_COMM_WORLD,&svd);
85: /*
86: Set operator
87: */
88: SVDSetOperator(svd,A);
90: /*
91: Set solver parameters at runtime
92: */
93: SVDSetType(svd,svdtype);
94: if (flg) {
95: PetscObjectTypeCompare((PetscObject)svd,SVDCROSS,&flg);
96: if (flg) {
97: SVDCrossGetEPS(svd,&eps);
98: EPSSetType(eps,epstype);
99: }
100: PetscObjectTypeCompare((PetscObject)svd,SVDCYCLIC,&flg);
101: if (flg) {
102: SVDCyclicGetEPS(svd,&eps);
103: EPSSetType(eps,epstype);
104: }
105: }
106: SVDSetDimensions(svd,1,PETSC_DEFAULT,PETSC_DEFAULT);
107: SVDSetTolerances(svd,1e-6,1000);
109: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
110: Compute the singular values
111: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
113: /*
114: First request the largest singular value
115: */
116: SVDSetWhichSingularTriplets(svd,SVD_LARGEST);
117: SVDSolve(svd);
118: /*
119: Get number of converged singular values
120: */
121: SVDGetConverged(svd,&nconv1);
122: /*
123: Get converged singular values: largest singular value is stored in sigma_1.
124: In this example, we are not interested in the singular vectors
125: */
126: if (nconv1 > 0) {
127: SVDGetSingularTriplet(svd,0,&sigma_1,NULL,NULL);
128: } else {
129: PetscPrintf(PETSC_COMM_WORLD," Unable to compute large singular value!\n\n");
130: }
132: /*
133: Request the smallest singular value
134: */
135: SVDSetWhichSingularTriplets(svd,SVD_SMALLEST);
136: SVDSolve(svd);
137: /*
138: Get number of converged triplets
139: */
140: SVDGetConverged(svd,&nconv2);
141: /*
142: Get converged singular values: smallest singular value is stored in sigma_n.
143: As before, we are not interested in the singular vectors
144: */
145: if (nconv2 > 0) {
146: SVDGetSingularTriplet(svd,0,&sigma_n,NULL,NULL);
147: } else {
148: PetscPrintf(PETSC_COMM_WORLD," Unable to compute small singular value!\n\n");
149: }
151: /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
152: Display solution and clean up
153: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
154: if (nconv1 > 0 && nconv2 > 0) {
155: PetscPrintf(PETSC_COMM_WORLD," Computed singular values: sigma_1=%.4f, sigma_n=%.4f\n",(double)sigma_1,(double)sigma_n);
156: PetscPrintf(PETSC_COMM_WORLD," Estimated condition number: sigma_1/sigma_n=%.4f\n\n",(double)(sigma_1/sigma_n));
157: }
159: /*
160: Free work space
161: */
162: SVDDestroy(&svd);
163: MatDestroy(&A);
164: SlepcFinalize();
165: return ierr;
166: }
168: /*TEST
170: test:
171: suffix: 1
172: args: -type {{lanczos trlanczos cross cyclic lapack}}
174: test:
175: suffix: 1_cross_gd
176: args: -type cross -epstype gd
177: output_file: output/test1_1.out
179: test:
180: suffix: 1_cyclic_gd
181: args: -type cyclic -epstype gd
182: output_file: output/test1_1.out
184: test:
185: suffix: 1_primme
186: args: -type primme
187: requires: primme
188: output_file: output/test1_1.out
190: TEST*/