/*
   - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
   SLEPc - Scalable Library for Eigenvalue Problem Computations
   Copyright (c) 2002-2012, Universitat Politecnica de Valencia, Spain

   This file is part of SLEPc.
      
   SLEPc is free software: you can redistribute it and/or modify it under  the
   terms of version 3 of the GNU Lesser General Public License as published by
   the Free Software Foundation.

   SLEPc  is  distributed in the hope that it will be useful, but WITHOUT  ANY 
   WARRANTY;  without even the implied warranty of MERCHANTABILITY or  FITNESS 
   FOR  A  PARTICULAR PURPOSE. See the GNU Lesser General Public  License  for 
   more details.

   You  should have received a copy of the GNU Lesser General  Public  License
   along with SLEPc. If not, see <http://www.gnu.org/licenses/>.
   - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
*/

static char help[] = "Singular value decomposition of the Lauchli matrix.\n"
  "The command line options are:\n"
  "  -n <n>, where <n> = matrix dimension.\n"
  "  -mu <mu>, where <mu> = subdiagonal value.\n\n";

#include <slepcsvd.h>

#undef __FUNCT__
#define __FUNCT__ "main"
int main(int argc,char **argv)
{
  Mat            A;               /* operator matrix */
  Vec            u,v;             /* left and right singular vectors */
  SVD            svd;             /* singular value problem solver context */
  const SVDType  type;
  PetscReal      error,tol,sigma,mu=PETSC_SQRT_MACHINE_EPSILON;
  PetscInt       n=100,i,j,Istart,Iend,nsv,maxit,its,nconv;
  PetscErrorCode ierr;

  SlepcInitialize(&argc,&argv,(char*)0,help);

  ierr = PetscOptionsGetInt(PETSC_NULL,"-n",&n,PETSC_NULL);CHKERRQ(ierr);
  ierr = PetscOptionsGetReal(PETSC_NULL,"-mu",&mu,PETSC_NULL);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD,"\nLauchli singular value decomposition, (%D x %D) mu=%G\n\n",n+1,n,mu);CHKERRQ(ierr);

  /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
                          Build the Lauchli matrix
     - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

  ierr = MatCreate(PETSC_COMM_WORLD,&A);CHKERRQ(ierr);
  ierr = MatSetSizes(A,PETSC_DECIDE,PETSC_DECIDE,n+1,n);CHKERRQ(ierr);
  ierr = MatSetFromOptions(A);CHKERRQ(ierr);
  ierr = MatSetUp(A);CHKERRQ(ierr);

  ierr = MatGetOwnershipRange(A,&Istart,&Iend);CHKERRQ(ierr);
  for (i=Istart;i<Iend;i++) {
    if (i == 0) {
      for (j=0;j<n;j++) {
        ierr = MatSetValue(A,0,j,1.0,INSERT_VALUES);CHKERRQ(ierr);
      }
    } else {
      ierr = MatSetValue(A,i,i-1,mu,INSERT_VALUES);CHKERRQ(ierr);
    }
  }
  
  ierr = MatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
  ierr = MatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);CHKERRQ(ierr);
  ierr = MatGetVecs(A,&v,&u);CHKERRQ(ierr);

  /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
          Create the singular value solver and set various options
     - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

  /* 
     Create singular value solver context
  */
  ierr = SVDCreate(PETSC_COMM_WORLD,&svd);CHKERRQ(ierr);

  /* 
     Set operator
  */
  ierr = SVDSetOperator(svd,A);CHKERRQ(ierr);
  
  /*
     Use thick-restart Lanczos as default solver
  */
  ierr = SVDSetType(svd,SVDTRLANCZOS);CHKERRQ(ierr);

  /*
     Set solver parameters at runtime
  */
  ierr = SVDSetFromOptions(svd);CHKERRQ(ierr);

  /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
                      Solve the singular value system
     - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

  ierr = SVDSolve(svd);CHKERRQ(ierr);
  ierr = SVDGetIterationNumber(svd,&its);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD," Number of iterations of the method: %D\n",its);CHKERRQ(ierr);

  /*
     Optional: Get some information from the solver and display it
  */
  ierr = SVDGetType(svd,&type);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD," Solution method: %s\n\n",type);CHKERRQ(ierr);
  ierr = SVDGetDimensions(svd,&nsv,PETSC_NULL,PETSC_NULL);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD," Number of requested singular values: %D\n",nsv);CHKERRQ(ierr);
  ierr = SVDGetTolerances(svd,&tol,&maxit);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD," Stopping condition: tol=%.4G, maxit=%D\n",tol,maxit);CHKERRQ(ierr);

  /* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
                    Display solution and clean up
     - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */

  /* 
     Get number of converged singular triplets
  */
  ierr = SVDGetConverged(svd,&nconv);CHKERRQ(ierr);
  ierr = PetscPrintf(PETSC_COMM_WORLD," Number of converged approximate singular triplets: %D\n\n",nconv);CHKERRQ(ierr);

  if (nconv>0) {
    /*
       Display singular values and relative errors
    */
    ierr = PetscPrintf(PETSC_COMM_WORLD,
         "          sigma           relative error\n"
         "  --------------------- ------------------\n");CHKERRQ(ierr);
    for (i=0;i<nconv;i++) {
      /* 
         Get converged singular triplets: i-th singular value is stored in sigma
      */
      ierr = SVDGetSingularTriplet(svd,i,&sigma,u,v);CHKERRQ(ierr);

      /*
         Compute the error associated to each singular triplet 
      */
      ierr = SVDComputeRelativeError(svd,i,&error);CHKERRQ(ierr);

      ierr = PetscPrintf(PETSC_COMM_WORLD,"       % 6F      ",sigma);CHKERRQ(ierr);
      ierr = PetscPrintf(PETSC_COMM_WORLD," % 12G\n",error);CHKERRQ(ierr);
    }
    ierr = PetscPrintf(PETSC_COMM_WORLD,"\n");CHKERRQ(ierr);
  }
  
  /* 
     Free work space
  */
  ierr = SVDDestroy(&svd);CHKERRQ(ierr);
  ierr = MatDestroy(&A);CHKERRQ(ierr);
  ierr = VecDestroy(&u);CHKERRQ(ierr);
  ierr = VecDestroy(&v);CHKERRQ(ierr);
  ierr = SlepcFinalize();CHKERRQ(ierr);
  return 0;
}