Date: 2020-01-13T12:01:07+00:00

Actual source code: ex15.c
slepc-3.12.2 2020-01-13
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      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[] = "Singular value decomposition of the Lauchli matrix.\n"
     12:   "The command line options are:\n"
     13:   "  -n <n>, where <n> = matrix dimension.\n"
     14:   "  -mu <mu>, where <mu> = subdiagonal value.\n\n";

     16: #include <slepcsvd.h>

     18: int main(int argc,char **argv)
     19: {
     20:   Mat            A;               /* operator matrix */
     21:   Vec            u,v;             /* left and right singular vectors */
     22:   SVD            svd;             /* singular value problem solver context */
     23:   SVDType        type;
     24:   PetscReal      error,tol,sigma,mu=PETSC_SQRT_MACHINE_EPSILON;
     25:   PetscInt       n=100,i,j,Istart,Iend,nsv,maxit,its,nconv;

     28:   SlepcInitialize(&argc,&argv,(char*)0,help);if (ierr) return ierr;

     30:   PetscOptionsGetInt(NULL,NULL,"-n",&n,NULL);
     31:   PetscOptionsGetReal(NULL,NULL,"-mu",&mu,NULL);
     32:   PetscPrintf(PETSC_COMM_WORLD,"\nLauchli singular value decomposition, (%D x %D) mu=%g\n\n",n+1,n,(double)mu);

     34:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     35:                           Build the Lauchli matrix
     36:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     38:   MatCreate(PETSC_COMM_WORLD,&A);
     39:   MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,n+1,n);
     40:   MatSetFromOptions(A);
     41:   MatSetUp(A);

     43:   MatGetOwnershipRange(A,&Istart,&Iend);
     44:   for (i=Istart;i<Iend;i++) {
     45:     if (i == 0) {
     46:       for (j=0;j<n;j++) {
     47:         MatSetValue(A,0,j,1.0,INSERT_VALUES);
     48:       }
     49:     } else {
     50:       MatSetValue(A,i,i-1,mu,INSERT_VALUES);
     51:     }
     52:   }

     54:   MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
     55:   MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
     56:   MatCreateVecs(A,&v,&u);

     58:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     59:           Create the singular value solver and set various options
     60:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     62:   /*
     63:      Create singular value solver context
     64:   */
     65:   SVDCreate(PETSC_COMM_WORLD,&svd);

     67:   /*
     68:      Set operator
     69:   */
     70:   SVDSetOperator(svd,A);

     72:   /*
     73:      Use thick-restart Lanczos as default solver
     74:   */
     75:   SVDSetType(svd,SVDTRLANCZOS);

     77:   /*
     78:      Set solver parameters at runtime
     79:   */
     80:   SVDSetFromOptions(svd);

     82:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
     83:                       Solve the singular value system
     84:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

     86:   SVDSolve(svd);
     87:   SVDGetIterationNumber(svd,&its);
     88:   PetscPrintf(PETSC_COMM_WORLD," Number of iterations of the method: %D\n",its);

     90:   /*
     91:      Optional: Get some information from the solver and display it
     92:   */
     93:   SVDGetType(svd,&type);
     94:   PetscPrintf(PETSC_COMM_WORLD," Solution method: %s\n\n",type);
     95:   SVDGetDimensions(svd,&nsv,NULL,NULL);
     96:   PetscPrintf(PETSC_COMM_WORLD," Number of requested singular values: %D\n",nsv);
     97:   SVDGetTolerances(svd,&tol,&maxit);
     98:   PetscPrintf(PETSC_COMM_WORLD," Stopping condition: tol=%.4g, maxit=%D\n",(double)tol,maxit);

    100:   /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    101:                     Display solution and clean up
    102:      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

    104:   /*
    105:      Get number of converged singular triplets
    106:   */
    107:   SVDGetConverged(svd,&nconv);
    108:   PetscPrintf(PETSC_COMM_WORLD," Number of converged approximate singular triplets: %D\n\n",nconv);

    110:   if (nconv>0) {
    111:     /*
    112:        Display singular values and relative errors
    113:     */
    114:     PetscPrintf(PETSC_COMM_WORLD,
    115:          "          sigma           relative error\n"
    116:          "  --------------------- ------------------\n");
    117:     for (i=0;i<nconv;i++) {
    118:       /*
    119:          Get converged singular triplets: i-th singular value is stored in sigma
    120:       */
    121:       SVDGetSingularTriplet(svd,i,&sigma,u,v);

    123:       /*
    124:          Compute the error associated to each singular triplet
    125:       */
    126:       SVDComputeError(svd,i,SVD_ERROR_RELATIVE,&error);

    128:       PetscPrintf(PETSC_COMM_WORLD,"       % 6f      ",(double)sigma);
    129:       PetscPrintf(PETSC_COMM_WORLD," % 12g\n",(double)error);
    130:     }
    131:     PetscPrintf(PETSC_COMM_WORLD,"\n");
    132:   }

    134:   /*
    135:      Free work space
    136:   */
    137:   SVDDestroy(&svd);
    138:   MatDestroy(&A);
    139:   VecDestroy(&u);
    140:   VecDestroy(&v);
    141:   SlepcFinalize();
    142:   return ierr;
    143: }

    145: /*TEST

    147:    test:
    148:       suffix: 1
    149:       filter: sed -e "s/[0-9]\.[0-9]*e[+-]\([0-9]*\)/removed/g"
    150:       requires: double

    152: TEST*/