Actual source code: test16.c

slepc-3.20.2 2024-03-15
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  1: /*
  2:    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  3:    SLEPc - Scalable Library for Eigenvalue Problem Computations
  4:    Copyright (c) 2002-, 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[] = "Tests multiple calls to SVDSolve with equal matrix size (GSVD).\n\n"
 12:   "The command line options are:\n"
 13:   "  -m <m>, where <m> = number of rows of A.\n"
 14:   "  -n <n>, where <n> = number of columns of A.\n"
 15:   "  -p <p>, where <p> = number of rows of B.\n\n";

 17: #include <slepcsvd.h>

 19: /*
 20:    This example solves two GSVD problems for the bidiagonal matrices

 22:               |  1  2                     |       |  1                        |
 23:               |     1  2                  |       |  2  1                     |
 24:               |        1  2               |       |     2  1                  |
 25:          A1 = |          .  .             |  A2 = |       .  .                |
 26:               |             .  .          |       |          .  .             |
 27:               |                1  2       |       |             2  1          |
 28:               |                   1  2    |       |                2  1       |

 30:    with B = tril(ones(p,n))
 31:  */

 33: int main(int argc,char **argv)
 34: {
 35:   Mat            A1,A2,B;
 36:   SVD            svd;
 37:   PetscInt       m=15,n=20,p=21,Istart,Iend,i,j,d,col[2];
 38:   PetscScalar    valsa[] = { 1, 2 }, valsb[] = { 2, 1 };

 40:   PetscFunctionBeginUser;
 41:   PetscCall(SlepcInitialize(&argc,&argv,(char*)0,help));
 42:   PetscCall(PetscOptionsGetInt(NULL,NULL,"-m",&m,NULL));
 43:   PetscCall(PetscOptionsGetInt(NULL,NULL,"-n",&n,NULL));
 44:   PetscCall(PetscOptionsGetInt(NULL,NULL,"-p",&p,NULL));
 45:   PetscCall(PetscPrintf(PETSC_COMM_WORLD,"\nGeneralized singular value decomposition, (%" PetscInt_FMT "+%" PetscInt_FMT ")x%" PetscInt_FMT "\n\n",m,p,n));

 47:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 48:                      Generate the matrices
 49:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

 51:   PetscCall(MatCreate(PETSC_COMM_WORLD,&A1));
 52:   PetscCall(MatSetSizes(A1,PETSC_DECIDE,PETSC_DECIDE,m,n));
 53:   PetscCall(MatSetFromOptions(A1));
 54:   PetscCall(MatSetUp(A1));
 55:   PetscCall(MatGetOwnershipRange(A1,&Istart,&Iend));
 56:   for (i=Istart;i<Iend;i++) {
 57:     col[0]=i; col[1]=i+1;
 58:     if (i<n-1) PetscCall(MatSetValues(A1,1,&i,2,col,valsa,INSERT_VALUES));
 59:     else if (i==n-1) PetscCall(MatSetValue(A1,i,col[0],valsa[0],INSERT_VALUES));
 60:   }
 61:   PetscCall(MatAssemblyBegin(A1,MAT_FINAL_ASSEMBLY));
 62:   PetscCall(MatAssemblyEnd(A1,MAT_FINAL_ASSEMBLY));

 64:   PetscCall(MatCreate(PETSC_COMM_WORLD,&A2));
 65:   PetscCall(MatSetSizes(A2,PETSC_DECIDE,PETSC_DECIDE,m,n));
 66:   PetscCall(MatSetFromOptions(A2));
 67:   PetscCall(MatSetUp(A2));
 68:   PetscCall(MatGetOwnershipRange(A2,&Istart,&Iend));
 69:   for (i=Istart;i<Iend;i++) {
 70:     col[0]=i-1; col[1]=i;
 71:     if (i==0) PetscCall(MatSetValue(A2,i,col[1],valsb[1],INSERT_VALUES));
 72:     else if (i<n) PetscCall(MatSetValues(A2,1,&i,2,col,valsb,INSERT_VALUES));
 73:     else if (i==n) PetscCall(MatSetValue(A2,i,col[0],valsb[0],INSERT_VALUES));
 74:   }
 75:   PetscCall(MatAssemblyBegin(A2,MAT_FINAL_ASSEMBLY));
 76:   PetscCall(MatAssemblyEnd(A2,MAT_FINAL_ASSEMBLY));

 78:   PetscCall(MatCreate(PETSC_COMM_WORLD,&B));
 79:   PetscCall(MatSetSizes(B,PETSC_DECIDE,PETSC_DECIDE,p,n));
 80:   PetscCall(MatSetFromOptions(B));
 81:   PetscCall(MatSetUp(B));
 82:   PetscCall(MatGetOwnershipRange(B,&Istart,&Iend));
 83:   d = PetscMax(0,n-p);
 84:   for (i=Istart;i<Iend;i++) {
 85:     for (j=PetscMax(0,i-5);j<=PetscMin(i,n-1);j++) PetscCall(MatSetValue(B,i,j+d,1.0,INSERT_VALUES));
 86:   }
 87:   PetscCall(MatAssemblyBegin(B,MAT_FINAL_ASSEMBLY));
 88:   PetscCall(MatAssemblyEnd(B,MAT_FINAL_ASSEMBLY));

 90:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
 91:          Create the singular value solver, set options and solve
 92:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

 94:   PetscCall(SVDCreate(PETSC_COMM_WORLD,&svd));
 95:   PetscCall(SVDSetOperators(svd,A1,B));
 96:   PetscCall(SVDSetFromOptions(svd));
 97:   PetscCall(SVDSolve(svd));
 98:   PetscCall(SVDErrorView(svd,SVD_ERROR_NORM,NULL));

100:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
101:                        Solve second problem
102:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

104:   PetscCall(SVDSetOperators(svd,A2,B));
105:   PetscCall(SVDSolve(svd));
106:   PetscCall(SVDErrorView(svd,SVD_ERROR_NORM,NULL));

108:   /* Free work space */
109:   PetscCall(SVDDestroy(&svd));
110:   PetscCall(MatDestroy(&A1));
111:   PetscCall(MatDestroy(&A2));
112:   PetscCall(MatDestroy(&B));
113:   PetscCall(SlepcFinalize());
114:   return 0;
115: }

117: /*TEST

119:    testset:
120:       args: -svd_nsv 3
121:       requires: !single
122:       output_file: output/test16_1.out
123:       test:
124:          suffix: 1_lapack
125:          args: -svd_type lapack
126:       test:
127:          suffix: 1_cross
128:          args: -svd_type cross -svd_cross_explicitmatrix {{0 1}}
129:       test:
130:          suffix: 1_cyclic
131:          args: -svd_type cyclic -svd_cyclic_explicitmatrix {{0 1}}
132:       test:
133:          suffix: 1_trlanczos
134:          args: -svd_type trlanczos -svd_trlanczos_gbidiag {{single lower}} -svd_trlanczos_ksp_rtol 1e-10
135:          requires: double
136:       test:
137:          suffix: 1_trlanczos_par
138:          nsize: 2
139:          args: -svd_type trlanczos -ds_parallel {{redundant synchronized}}

141:    testset:
142:       args: -svd_nsv 3 -mat_type aijcusparse
143:       requires: cuda !single
144:       output_file: output/test16_1.out
145:       test:
146:          suffix: 2_cross
147:          args: -svd_type cross -svd_cross_explicitmatrix {{0 1}}
148:       test:
149:          suffix: 2_cyclic
150:          args: -svd_type cyclic -svd_cyclic_explicitmatrix {{0 1}}
151:       test:
152:          suffix: 2_trlanczos
153:          args: -svd_type trlanczos -svd_trlanczos_gbidiag {{single lower}} -svd_trlanczos_ksp_rtol 1e-10
154:          requires: double

156: TEST*/