mat/interface/matrix.c

#ifndef lint
static char vcid[] = "$Id: matrix.c,v 1.90 1995/10/01 21:52:25 bsmith Exp curfman $";
#endif

/*
   This is where the abstract matrix operations are defined
*/

#include "petsc.h"
#include "matimpl.h"        /*I "mat.h" I*/
#include "vec/vecimpl.h"
#include "pinclude/pviewer.h"

extern int MatGetReorderingMethodFromOptions_Private(MatOrdering *);

/*@C
   MatGetReordering - Gets a reordering for a matrix to reduce fill or to
   improve numerical stability of LU factorization.

   Input Parameters:
.  mat - the matrix
.  type - type of reordering, one of the following:
$      ORDER_NATURAL - Natural
$      ORDER_ND - Nested Dissection
$      ORDER_1WD - One-way Dissection
$      ORDER_RCM - Reverse Cuthill-McGee
$      ORDER_QMD - Quotient Minimum Degree

   Output Parameters:
.  rperm - row permutation indices
.  cperm - column permutation indices

   Options Database Keys:
   To specify the ordering through the options database, use one of
   the following
$    -mat_order natural, -mat_order nd, -mat_order 1wd,
$    -mat_order rcm, -mat_order qmd

   Notes:
   If the column permutations and row permutations are the same,
   then MatGetReordering() returns 0 in cperm.

.keywords: matrix, set, ordering, factorization, direct, ILU, LU,
           fill, reordering, natural, Nested Dissection,
           One-way Dissection, Cholesky, Reverse Cuthill-McGee,
           Quotient Minimum Degree
@*/
int MatGetReordering(Mat mat,MatOrdering type,IS *rperm,IS *cperm)
{
  int         ierr;
  MatOrdering newtype;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.getreordering) {*rperm = 0; *cperm = 0; return 0;}
  PLogEventBegin(MAT_GetReordering,mat,0,0,0);
  if (MatGetReorderingMethodFromOptions_Private(&newtype)) type = newtype;
  ierr = (*mat->ops.getreordering)(mat,type,rperm,cperm); CHKERRQ(ierr);
  PLogEventEnd(MAT_GetReordering,mat,0,0,0);
  return 0;
}

/*@C
   MatGetRow - Gets a row of a matrix.  You MUST call MatRestoreRow()
   for each row that you get to ensure that your application does
   not bleed memory.

   Input Parameters:
.  mat - the matrix
.  row - the row to get

   Output Parameters:
.  ncols -  the number of nonzeros in the row
.  cols - if nonzero, the column numbers
.  vals - if nonzero, the values

   Notes:
   This routine is provided for people who need to have direct access
   to the structure of a matrix.  We hope that we provide enough
   high-level matrix routines that few users will need it.

   For better efficiency, set cols and/or vals to zero if you do not
   wish to extract these quantities.

.keywords: matrix, row, get, extract

.seealso: MatRestoreRow()
@*/
int MatGetRow(Mat mat,int row,int *ncols,int **cols,Scalar **vals)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  return (*mat->ops.getrow)(mat,row,ncols,cols,vals);
}

/*@C
   MatRestoreRow - Frees any temporary space allocated by MatGetRow().

   Input Parameters:
.  mat - the matrix
.  row - the row to get
.  ncols, cols - the number of nonzeros and their columns
.  vals - if nonzero the column values

.keywords: matrix, row, restore

.seealso:  MatGetRow()
@*/
int MatRestoreRow(Mat mat,int row,int *ncols,int **cols,Scalar **vals)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.restorerow) return 0;
  return (*mat->ops.restorerow)(mat,row,ncols,cols,vals);
}
/*@
   MatView - Visualizes a matrix object.

   Input Parameters:
.  mat - the matrix
.  ptr - visualization context

   Notes:
   The available visualization contexts include
$     STDOUT_VIEWER_SELF - standard output (default)
$     STDOUT_VIEWER_WORLD - synchronized standard
$       output where only the first processor opens
$       the file.  All other processors send their
$       data to the first processor to print.

   The user can open alternative vistualization contexts with
$    ViewerFileOpenASCII() - output to a specified file
$    DrawOpenX() - output nonzero matrix structure to
$         an X window display
$    ViewerMatlabOpen() - output matrix to Matlab viewer.
$         Currently only the sequential dense and AIJ
$         matrix types support the Matlab viewer.

   The user can call ViewerFileSetFormat() to specify the output
   format.  Available formats include
$    FILE_FORMAT_DEFAULT - default, prints matrix contents
$    FILE_FORMAT_IMPL - implementation-specific format
$      (which is in many cases the same as the default)
$    FILE_FORMAT_INFO - basic information about the matrix
$      size and structure (not the matrix entries)

.keywords: matrix, view, visualize

.seealso: ViewerFileSetFormat(), ViewerFileOpenASCII(), DrawOpenX(),
          ViewerMatlabOpen()
@*/
int MatView(Mat mat,Viewer ptr)
{
  int format, ierr, rows, cols,nz, nzalloc, mem;
  FILE *fd;
  char *cstring;
  PetscObject  vobj = (PetscObject) ptr;

  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!ptr) { /* so that viewers may be used from debuggers */
    ptr = STDOUT_VIEWER_SELF; vobj = (PetscObject) ptr;
  }
  ierr = ViewerFileGetFormat_Private(ptr,&format); CHKERRQ(ierr);
  ierr = ViewerFileGetPointer_Private(ptr,&fd); CHKERRQ(ierr);
  if (vobj->cookie == VIEWER_COOKIE && format == FILE_FORMAT_INFO &&
    (vobj->type == ASCII_FILE_VIEWER || vobj->type == ASCII_FILES_VIEWER)) {
    MPIU_fprintf(mat->comm,fd,"Matrix Object:\n");
    ierr = MatGetName(mat,&cstring); CHKERRQ(ierr);
    ierr = MatGetSize(mat,&rows,&cols); CHKERRQ(ierr);
    MPIU_fprintf(mat->comm,fd,
      "  type=%s, rows=%d, cols=%d\n",cstring,rows,cols);
    if (mat->ops.getinfo) {
      ierr = MatGetInfo(mat,MAT_LOCAL,&nz,&nzalloc,&mem); CHKERRQ(ierr);
      MPIU_fprintf(mat->comm,fd,
        "  nonzeros=%d, allocated nonzeros=%d\n",nz,nzalloc);
    }
  }
  if (mat->view) {ierr = (*mat->view)((PetscObject)mat,ptr); CHKERRQ(ierr);}
  return 0;
}
/*@C
   MatDestroy - Frees space taken by a matrix.

   Input Parameter:
.  mat - the matrix

.keywords: matrix, destroy
@*/
int MatDestroy(Mat mat)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  return (*mat->destroy)((PetscObject)mat);
}
/*@
   MatValidMatrix - Returns 1 if a valid matrix else 0.

   Input Parameter:
.  m - the matrix to check

.keywords: matrix, valid
@*/
int MatValidMatrix(Mat m)
{
  if (!m) return 0;
  if (m->cookie != MAT_COOKIE) return 0;
  return 1;
}

/*@
   MatSetValues - Inserts or adds a block of values into a matrix.
   These values may be cached, so MatAssemblyBegin() and MatAssemblyEnd()
   MUST be called after all calls to MatSetValues() have been completed.

   Input Parameters:
.  mat - the matrix
.  v - a logically two-dimensional array of values
.  m, indexm - the number of rows and their global indices
.  n, indexn - the number of columns and their global indices
.  addv - either ADD_VALUES or INSERT_VALUES, where
$     ADD_VALUES - adds values to any existing entries
$     INSERT_VALUES - replaces existing entries with new values

   Notes:
   By default the values, v, are row-oriented and unsorted.
   See MatSetOptions() for other options.

   Calls to MatSetValues() with the INSERT_VALUES and ADD_VALUES
   options cannot be mixed without intervening calls to the assembly
   routines.

.keywords: matrix, insert, add, set, values

.seealso: MatSetOptions(), MatAssemblyBegin(), MatAssemblyEnd()
@*/
int MatSetValues(Mat mat,int m,int *idxm,int n,int *idxn,Scalar *v,
                                                        InsertMode addv)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PLogEventBegin(MAT_SetValues,mat,0,0,0);
  ierr = (*mat->ops.setvalues)(mat,m,idxm,n,idxn,v,addv);CHKERRQ(ierr);
  PLogEventEnd(MAT_SetValues,mat,0,0,0);
  return 0;
}

/* --------------------------------------------------------*/
/*@
   MatMult - Computes matrix-vector product.

   Input Parameters:
.  mat - the matrix
.  x   - the vector to be multilplied

   Output Parameters:
.  y - the result

.keywords: matrix, multiply, matrix-vector product

.seealso: MatMultTrans(), MatMultAdd(), MatMultTransAdd()
@*/
int MatMult(Mat mat,Vec x,Vec y)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);PETSCVALIDHEADERSPECIFIC(y,VEC_COOKIE);
  PLogEventBegin(MAT_Mult,mat,x,y,0);
  ierr = (*mat->ops.mult)(mat,x,y); CHKERRQ(ierr);
  PLogEventEnd(MAT_Mult,mat,x,y,0);
  return 0;
}
/*@
   MatMultTrans - Computes matrix transpose times a vector.

   Input Parameters:
.  mat - the matrix
.  x   - the vector to be multilplied

   Output Parameters:
.  y - the result

.keywords: matrix, multiply, matrix-vector product, transpose

.seealso: MatMult(), MatMultAdd(), MatMultTransAdd()
@*/
int MatMultTrans(Mat mat,Vec x,Vec y)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE); PETSCVALIDHEADERSPECIFIC(y,VEC_COOKIE);
  PLogEventBegin(MAT_MultTrans,mat,x,y,0);
  ierr = (*mat->ops.multtrans)(mat,x,y); CHKERRQ(ierr);
  PLogEventEnd(MAT_MultTrans,mat,x,y,0);
  return 0;
}
/*@
    MatMultAdd -  Computes v3 = v2 + A * v1.

  Input Parameters:
.    mat - the matrix
.    v1, v2 - the vectors

  Output Parameters:
.    v3 - the result

.keywords: matrix, multiply, matrix-vector product, add

.seealso: MatMultTrans(), MatMult(), MatMultTransAdd()
@*/
int MatMultAdd(Mat mat,Vec v1,Vec v2,Vec v3)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);PETSCVALIDHEADERSPECIFIC(v1,VEC_COOKIE);
  PETSCVALIDHEADERSPECIFIC(v2,VEC_COOKIE); PETSCVALIDHEADERSPECIFIC(v3,VEC_COOKIE);
  PLogEventBegin(MAT_MultAdd,mat,v1,v2,v3);
  ierr = (*mat->ops.multadd)(mat,v1,v2,v3); CHKERRQ(ierr);
  PLogEventEnd(MAT_MultAdd,mat,v1,v2,v3);
  return 0;
}
/*@
    MatMultTransAdd - Computes v3 = v2 + A' * v1.

  Input Parameters:
.    mat - the matrix
.    v1, v2 - the vectors

  Output Parameters:
.    v3 - the result

.keywords: matrix, multiply, matrix-vector product, transpose, add

.seealso: MatMultTrans(), MatMultAdd(), MatMult()
@*/
int MatMultTransAdd(Mat mat,Vec v1,Vec v2,Vec v3)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE); PETSCVALIDHEADERSPECIFIC(v1,VEC_COOKIE);
  PETSCVALIDHEADERSPECIFIC(v2,VEC_COOKIE); PETSCVALIDHEADERSPECIFIC(v3,VEC_COOKIE);
  if (!mat->ops.multtransadd) SETERRQ(PETSC_ERR_SUP,"MatMultTransAdd");
  PLogEventBegin(MAT_MultTransAdd,mat,v1,v2,v3);
  ierr = (*mat->ops.multtransadd)(mat,v1,v2,v3); CHKERRQ(ierr);
  PLogEventEnd(MAT_MultTransAdd,mat,v1,v2,v3);
  return 0;
}
/* ------------------------------------------------------------*/
/*@
   MatGetInfo - Returns information about matrix storage (number of
   nonzeros, memory).

   Input Parameters:
.  mat - the matrix

   Output Parameters:
.  flag - flag indicating the type of parameters to be returned
$    flag = MAT_LOCAL: local matrix
$    flag = MAT_GLOBAL_MAX: maximum over all processors
$    flag = MAT_GLOBAL_SUM: sum over all processors
.   nz - the number of nonzeros
.   nzalloc - the number of allocated nonzeros
.   mem - the memory used (in bytes)

.keywords: matrix, get, info, storage, nonzeros, memory
@*/
int MatGetInfo(Mat mat,MatInfoType flag,int *nz,int *nzalloc,int *mem)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.getinfo) SETERRQ(PETSC_ERR_SUP,"MatGetInfo");
  return  (*mat->ops.getinfo)(mat,flag,nz,nzalloc,mem);
}
/* ----------------------------------------------------------*/
/*@
   MatLUFactor - Performs in-place LU factorization of matrix.

   Input Parameters:
.  mat - the matrix
.  row - row permutation
.  col - column permutation
.  f - expected fill as ratio of original fill.

.keywords: matrix, factor, LU, in-place

.seealso: MatLUFactorSymbolic(), MatLUFactorNumeric(), MatCholeskyFactor()
@*/
int MatLUFactor(Mat mat,IS row,IS col,double f)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.lufactor) SETERRQ(PETSC_ERR_SUP,"MatLUFactor");
  PLogEventBegin(MAT_LUFactor,mat,row,col,0);
  ierr = (*mat->ops.lufactor)(mat,row,col,f); CHKERRQ(ierr);
  PLogEventEnd(MAT_LUFactor,mat,row,col,0);
  return 0;
}
/*@
   MatLUFactorSymbolic - Performs symbolic LU factorization of matrix.
   Call this routine before calling MatLUFactorNumeric().

   Input Parameters:
.  mat - the matrix
.  row, col - row and column permutations
.  f - expected fill as ratio of the original number of nonzeros,
       for example 3.0; choosing this parameter well can result in
       more efficient use of time and space.

   Output Parameters:
.  fact - new matrix that has been symbolically factored

.keywords: matrix, factor, LU, symbol CHKERRQ(ierr);ic

.seealso: MatLUFactor(), MatLUFactorNumeric(), MatCholeskyFactor()
@*/
int MatLUFactorSymbolic(Mat mat,IS row,IS col,double f,Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!fact) SETERRQ(1,"MatLUFactorSymbolic:Missing factor matrix argument");
  if (!mat->ops.lufactorsymbolic) SETERRQ(PETSC_ERR_SUP,"MatLUFactorSymbolic");
  OptionsGetDouble(0,"-mat_lu_fill",&f);
  PLogEventBegin(MAT_LUFactorSymbolic,mat,row,col,0);
  ierr = (*mat->ops.lufactorsymbolic)(mat,row,col,f,fact); CHKERRQ(ierr);
  PLogEventEnd(MAT_LUFactorSymbolic,mat,row,col,0);
  return 0;
}
/*@
   MatLUFactorNumeric - Performs numeric LU factorization of a matrix.
   Call this routine after first calling MatLUFactorSymbolic().

   Input Parameters:
.  mat - the matrix
.  row, col - row and  column permutations

   Output Parameters:
.  fact - symbolically factored matrix that must have been generated
          by MatLUFactorSymbolic()

   Notes:
   See MatLUFactor() for in-place factorization.  See
   MatCholeskyFactorNumeric() for the symmetric, positive definite case.

.keywords: matrix, factor, LU, numeric

.seealso: MatLUFactorSymbolic(), MatLUFactor(), MatCholeskyFactor()
@*/
int MatLUFactorNumeric(Mat mat,Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!fact) SETERRQ(1,"MatLUFactorNumeric:Missing factor matrix argument");
  if (!mat->ops.lufactornumeric) SETERRQ(PETSC_ERR_SUP,"MatLUFactorNumeric");
  PLogEventBegin(MAT_LUFactorNumeric,mat,*fact,0,0);
  ierr = (*mat->ops.lufactornumeric)(mat,fact); CHKERRQ(ierr);
  PLogEventEnd(MAT_LUFactorNumeric,mat,*fact,0,0);
  return 0;
}
/*@
   MatCholeskyFactor - Performs in-place Cholesky factorization of a
   symmetric matrix.

   Input Parameters:
.  mat - the matrix
.  perm - row and column permutations
.  f - expected fill as ratio of original fill

   Notes:
   See MatLUFactor() for the nonsymmetric case.  See also
   MatCholeskyFactorSymbolic(), and MatCholeskyFactorNumeric().

.keywords: matrix, factor, in-place, Cholesky

.seealso: MatLUFactor(), MatCholeskyFactorSymbolic()
.seealso: MatCholeskyFactorNumeric()
@*/
int MatCholeskyFactor(Mat mat,IS perm,double f)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.choleskyfactor) SETERRQ(PETSC_ERR_SUP,"MatCholeskyFactor");
  PLogEventBegin(MAT_CholeskyFactor,mat,perm,0,0);
  ierr = (*mat->ops.choleskyfactor)(mat,perm,f); CHKERRQ(ierr);
  PLogEventEnd(MAT_CholeskyFactor,mat,perm,0,0);
  return 0;
}
/*@
   MatCholeskyFactorSymbolic - Performs symbolic Cholesky factorization
   of a symmetric matrix.

   Input Parameters:
.  mat - the matrix
.  perm - row and column permutations
.  f - expected fill as ratio of original

   Output Parameter:
.  fact - the factored matrix

   Notes:
   See MatLUFactorSymbolic() for the nonsymmetric case.  See also
   MatCholeskyFactor() and MatCholeskyFactorNumeric().

.keywords: matrix, factor, factorization, symbolic, Cholesky

.seealso: MatLUFactorSymbolic()
.seealso: MatCholeskyFactor(), MatCholeskyFactorNumeric()
@*/
int MatCholeskyFactorSymbolic(Mat mat,IS perm,double f,Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!fact)
    SETERRQ(1,"MatCholeskyFactorSymbolic:Missing factor matrix argument");
  if (!mat->ops.choleskyfactorsymbolic)SETERRQ(PETSC_ERR_SUP,"MatCholeskyFactorSymbolic");
  PLogEventBegin(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
  ierr = (*mat->ops.choleskyfactorsymbolic)(mat,perm,f,fact); CHKERRQ(ierr);
  PLogEventEnd(MAT_CholeskyFactorSymbolic,mat,perm,0,0);
  return 0;
}
/*@
   MatCholeskyFactorNumeric - Performs numeric Cholesky factorization
   of a symmetric matrix. Call this routine after first calling
   MatCholeskyFactorSymbolic().

   Input Parameter:
.  mat - the initial matrix

   Output Parameter:
.  fact - the factored matrix

.keywords: matrix, factor, numeric, Cholesky

.seealso: MatCholeskyFactorSymbolic(), MatCholeskyFactor()
.seealso: MatLUFactorNumeric()
@*/
int MatCholeskyFactorNumeric(Mat mat,Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!fact)
    SETERRQ(1,"MatCholeskyFactorNumeric:Missing factor matrix argument");
  if (!mat->ops.choleskyfactornumeric) SETERRQ(PETSC_ERR_SUP,"MatCholeskyFactorNumeric");
  PLogEventBegin(MAT_CholeskyFactorNumeric,mat,*fact,0,0);
  ierr = (*mat->ops.choleskyfactornumeric)(mat,fact); CHKERRQ(ierr);
  PLogEventEnd(MAT_CholeskyFactorNumeric,mat,*fact,0,0);
  return 0;
}
/* ----------------------------------------------------------------*/
/*@
   MatSolve - Solves A x = b, given a factored matrix.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector

   Output Parameter:
.  x - the result vector

.keywords: matrix, linear system, solve, LU, Cholesky, triangular solve

.seealso: MatSolveAdd(), MatSolveTrans(), MatSolveTransAdd()
@*/
int MatSolve(Mat mat,Vec b,Vec x)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatSolve:Unfactored matrix");
  if (!mat->ops.solve) SETERRQ(PETSC_ERR_SUP,"MatSolve");
  PLogEventBegin(MAT_Solve,mat,b,x,0);
  ierr = (*mat->ops.solve)(mat,b,x); CHKERRQ(ierr);
  PLogEventEnd(MAT_Solve,mat,b,x,0);
  return 0;
}

/* @
   MatForwardSolve - Solves L x = b, given a factored matrix, A = LU.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector

   Output Parameter:
.  x - the result vector

   Notes:
   MatSolve() should be used for most applications, as it performs
   a forward solve followed by a backward solve.

.keywords: matrix, forward, LU, Cholesky, triangular solve

.seealso: MatSolve(), MatBackwardSolve()
@ */
int MatForwardSolve(Mat mat,Vec b,Vec x)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatForwardSolve:Unfactored matrix");
  if (!mat->ops.forwardsolve) SETERRQ(PETSC_ERR_SUP,"MatForwardSolve");
  PLogEventBegin(MAT_ForwardSolve,mat,b,x,0);
  ierr = (*mat->ops.forwardsolve)(mat,b,x); CHKERRQ(ierr);
  PLogEventEnd(MAT_ForwardSolve,mat,b,x,0);
  return 0;
}

/* @
   MatBackwardSolve - Solves U x = b, given a factored matrix, A = LU.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector

   Output Parameter:
.  x - the result vector

   Notes:
   MatSolve() should be used for most applications, as it performs
   a forward solve followed by a backward solve.

.keywords: matrix, backward, LU, Cholesky, triangular solve

.seealso: MatSolve(), MatForwardSolve()
@ */
int MatBackwardSolve(Mat mat,Vec b,Vec x)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatBackwardSolve:Unfactored matrix");
  if (!mat->ops.backwardsolve) SETERRQ(PETSC_ERR_SUP,"MatBackwardSolve");
  PLogEventBegin(MAT_BackwardSolve,mat,b,x,0);
  ierr = (*mat->ops.backwardsolve)(mat,b,x); CHKERRQ(ierr);
  PLogEventEnd(MAT_BackwardSolve,mat,b,x,0);
  return 0;
}

/*@
   MatSolveAdd - Computes x = y + inv(A)*b, given a factored matrix.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector
.  y - the vector to be added to

   Output Parameter:
.  x - the result vector

.keywords: matrix, linear system, solve, LU, Cholesky, add

.seealso: MatSolve(), MatSolveTrans(), MatSolveTransAdd()
@*/
int MatSolveAdd(Mat mat,Vec b,Vec y,Vec x)
{
  Scalar one = 1.0;
  Vec    tmp;
  int    ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);PETSCVALIDHEADERSPECIFIC(y,VEC_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatSolveAdd:Unfactored matrix");
  PLogEventBegin(MAT_SolveAdd,mat,b,x,y);
  if (mat->ops.solveadd)  {
    ierr = (*mat->ops.solveadd)(mat,b,y,x); CHKERRQ(ierr);
  }
  else {
    /* do the solve then the add manually */
    if (x != y) {
      ierr = MatSolve(mat,b,x); CHKERRQ(ierr);
      ierr = VecAXPY(&one,y,x); CHKERRQ(ierr);
    }
    else {
      ierr = VecDuplicate(x,&tmp); CHKERRQ(ierr);
      PLogObjectParent(mat,tmp);
      ierr = VecCopy(x,tmp); CHKERRQ(ierr);
      ierr = MatSolve(mat,b,x); CHKERRQ(ierr);
      ierr = VecAXPY(&one,tmp,x); CHKERRQ(ierr);
      ierr = VecDestroy(tmp); CHKERRQ(ierr);
    }
  }
  PLogEventEnd(MAT_SolveAdd,mat,b,x,y);
  return 0;
}
/*@
   MatSolveTrans - Solves A' x = b, given a factored matrix.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector

   Output Parameter:
.  x - the result vector

.keywords: matrix, linear system, solve, LU, Cholesky, transpose

.seealso: MatSolve(), MatSolveAdd(), MatSolveTransAdd()
@*/
int MatSolveTrans(Mat mat,Vec b,Vec x)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatSolveTrans:Unfactored matrix");
  if (!mat->ops.solvetrans) SETERRQ(PETSC_ERR_SUP,"MatSolveTrans");
  PLogEventBegin(MAT_SolveTrans,mat,b,x,0);
  ierr = (*mat->ops.solvetrans)(mat,b,x); CHKERRQ(ierr);
  PLogEventEnd(MAT_SolveTrans,mat,b,x,0);
  return 0;
}
/*@
   MatSolveTransAdd - Computes x = y + inv(trans(A)) b, given a
                      factored matrix.

   Input Parameters:
.  mat - the factored matrix
.  b - the right-hand-side vector
.  y - the vector to be added to

   Output Parameter:
.  x - the result vector

.keywords: matrix, linear system, solve, LU, Cholesky, transpose, add

.seealso: MatSolve(), MatSolveAdd(), MatSolveTrans()
@*/
int MatSolveTransAdd(Mat mat,Vec b,Vec y,Vec x)
{
  Scalar one = 1.0;
  int    ierr;
  Vec    tmp;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);PETSCVALIDHEADERSPECIFIC(y,VEC_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->factor) SETERRQ(1,"MatSolveTransAdd:Unfactored matrix");
  PLogEventBegin(MAT_SolveTransAdd,mat,b,x,y);
  if (mat->ops.solvetransadd) {
    ierr = (*mat->ops.solvetransadd)(mat,b,y,x); CHKERRQ(ierr);
  }
  else {
    /* do the solve then the add manually */
    if (x != y) {
      ierr = MatSolveTrans(mat,b,x); CHKERRQ(ierr);
      ierr = VecAXPY(&one,y,x); CHKERRQ(ierr);
    }
    else {
      ierr = VecDuplicate(x,&tmp); CHKERRQ(ierr);
      PLogObjectParent(mat,tmp);
      ierr = VecCopy(x,tmp); CHKERRQ(ierr);
      ierr = MatSolveTrans(mat,b,x); CHKERRQ(ierr);
      ierr = VecAXPY(&one,tmp,x); CHKERRQ(ierr);
      ierr = VecDestroy(tmp); CHKERRQ(ierr);
    }
  }
  PLogEventEnd(MAT_SolveTransAdd,mat,b,x,y);
  return 0;
}
/* ----------------------------------------------------------------*/

/*@
   MatRelax - Computes one relaxation sweep.

   Input Parameters:
.  mat - the matrix
.  b - the right hand side
.  omega - the relaxation factor
.  flag - flag indicating the type of SOR, one of
$     SOR_FORWARD_SWEEP
$     SOR_BACKWARD_SWEEP
$     SOR_SYMMETRIC_SWEEP (SSOR method)
$     SOR_LOCAL_FORWARD_SWEEP
$     SOR_LOCAL_BACKWARD_SWEEP
$     SOR_LOCAL_SYMMETRIC_SWEEP (local SSOR)
$     SOR_APPLY_UPPER, SOR_APPLY_LOWER - applies
$       upper/lower triangular part of matrix to
$       vector (with omega)
$     SOR_ZERO_INITIAL_GUESS - zero initial guess
.  shift -  diagonal shift
.  its - the number of iterations

   Output Parameters:
.  x - the solution (can contain an initial guess)

   Notes:
   SOR_LOCAL_FORWARD_SWEEP, SOR_LOCAL_BACKWARD_SWEEP, and
   SOR_LOCAL_SYMMETRIC_SWEEP perform seperate independent smoothings
   on each processor.

   Application programmers will not generally use MatRelax() directly,
   but instead will employ the SLES/PC interface.

   Notes for Advanced Users:
   The flags are implemented as bitwise inclusive or operations.
   For example, use (SOR_ZERO_INITIAL_GUESS | SOR_SYMMETRIC_SWEEP)
   to specify a zero initial guess for SSOR.

.keywords: matrix, relax, relaxation, sweep
@*/
int MatRelax(Mat mat,Vec b,double omega,MatSORType flag,double shift,
             int its,Vec x)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(b,VEC_COOKIE);  PETSCVALIDHEADERSPECIFIC(x,VEC_COOKIE);
  if (!mat->ops.relax) SETERRQ(PETSC_ERR_SUP,"MatRelax");
  PLogEventBegin(MAT_Relax,mat,b,x,0);
  ierr =(*mat->ops.relax)(mat,b,omega,flag,shift,its,x); CHKERRQ(ierr);
  PLogEventEnd(MAT_Relax,mat,b,x,0);
  return 0;
}

/*@C
   MatConvert - Converts a matrix to another matrix, either of the same
   or different type.

   Input Parameters:
.  mat - the matrix
.  newtype - new matrix type.  Use MATSAME to create a new matrix of the
   same type as the original matrix.

   Output Parameter:
.  M - pointer to place new matrix

.keywords: matrix, copy, convert
@*/
int MatConvert(Mat mat,MatType newtype,Mat *M)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!M) SETERRQ(1,"MatConvert:Bad address");
  if (newtype == mat->type || newtype == MATSAME) {
    if (mat->ops.copyprivate) {
      PLogEventBegin(MAT_Convert,mat,0,0,0);
      ierr = (*mat->ops.copyprivate)(mat,M); CHKERRQ(ierr);
      PLogEventEnd(MAT_Convert,mat,0,0,0);
      return 0;
    }
  }
  if (!mat->ops.convert) SETERRQ(PETSC_ERR_SUP,"MatConvert");
  PLogEventBegin(MAT_Convert,mat,0,0,0);
  if (mat->ops.convert) {
    ierr = (*mat->ops.convert)(mat,newtype,M); CHKERRQ(ierr);
  }
  PLogEventEnd(MAT_Convert,mat,0,0,0);
  return 0;
}

/*@
   MatGetDiagonal - Gets the diagonal of a matrix.

   Input Parameters:
.  mat - the matrix

   Output Parameters:
.  v - the vector for storing the diagonal

.keywords: matrix, get, diagonal
@*/
int MatGetDiagonal(Mat mat,Vec v)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PETSCVALIDHEADERSPECIFIC(v,VEC_COOKIE);
  if (mat->ops.getdiagonal) return (*mat->ops.getdiagonal)(mat,v);
  SETERRQ(PETSC_ERR_SUP,"MatGetDiagonal");
}

/*@
   MatTranspose - Computes an in-place or out-of-place transpose of a matrix.

   Input Parameters:
.  mat - the matrix to transpose

   Output Parameters:
.   B - the transpose -  pass in zero for an in-place transpose

.keywords: matrix, transpose
@*/
int MatTranspose(Mat mat,Mat *B)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (mat->ops.transpose) return (*mat->ops.transpose)(mat,B);
  SETERRQ(PETSC_ERR_SUP,"MatTranspose");
}

/*@
   MatEqual - Compares two matrices.  Returns 1 if two matrices are equal.

   Input Parameters:
.  mat1 - the first matrix
.  mat2 - the second matrix

   Returns:
   Returns 1 if the matrices are equal; returns 0 otherwise.

.keywords: matrix, equal, equivalent
@*/
int MatEqual(Mat mat1,Mat mat2)
{
  PETSCVALIDHEADERSPECIFIC(mat1,MAT_COOKIE); PETSCVALIDHEADERSPECIFIC(mat2,MAT_COOKIE);
  if (mat1->ops.equal) return (*mat1->ops.equal)(mat1,mat2);
  SETERRQ(PETSC_ERR_SUP,"MatEqual");
}

/*@
   MatScale - Scales a matrix on the left and right by diagonal
   matrices that are stored as vectors.  Either of the two scaling
   matrices can be null.

   Input Parameters:
.  mat - the matrix to be scaled
.  l - the left scaling vector
.  r - the right scaling vector

.keywords: matrix, scale
@*/
int MatScale(Mat mat,Vec l,Vec r)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.scale) SETERRQ(PETSC_ERR_SUP,"MatScale");
  if (l) PETSCVALIDHEADERSPECIFIC(l,VEC_COOKIE);
  if (r) PETSCVALIDHEADERSPECIFIC(r,VEC_COOKIE);
  PLogEventBegin(MAT_Scale,mat,0,0,0);
  ierr = (*mat->ops.scale)(mat,l,r); CHKERRQ(ierr);
  PLogEventEnd(MAT_Scale,mat,0,0,0);
  return 0;
}

/*@
   MatNorm - Calculates various norms of a matrix.

   Input Parameters:
.  mat - the matrix
.  type - the type of norm, NORM_1, NORM_2, NORM_FROBENIUS, NORM_INFINITY

   Output Parameters:
.  norm - the resulting norm

.keywords: matrix, norm, Frobenius
@*/
int MatNorm(Mat mat,MatNormType type,double *norm)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!norm) SETERRQ(1,"MatNorm:bad addess for value");
  if (mat->ops.norm) return (*mat->ops.norm)(mat,type,norm);
  SETERRQ(PETSC_ERR_SUP,"MatNorm:Not for this matrix type");
}

/*@
   MatAssemblyBegin - Begins assembling the matrix.  This routine should
   be called after completing all calls to MatSetValues().

   Input Parameters:
.  mat - the matrix
.  type - type of assembly, either FLUSH_ASSEMBLY or FINAL_ASSEMBLY

   Notes:
   MatSetValues() generally caches the values.  The matrix is ready to
   use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
   Use FLUSH_ASSEMBLY when switching between ADD_VALUES and SetValues; use
   FINAL_ASSEMBLY for the final assembly before the matrix is used.

.keywords: matrix, assembly, assemble, begin

.seealso: MatAssemblyEnd(), MatSetValues()
@*/
int MatAssemblyBegin(Mat mat,MatAssemblyType type)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PLogEventBegin(MAT_AssemblyBegin,mat,0,0,0);
  if (mat->ops.assemblybegin) {ierr = (*mat->ops.assemblybegin)(mat,type); CHKERRQ(ierr);}
  PLogEventEnd(MAT_AssemblyBegin,mat,0,0,0);
  return 0;
}
/*@
   MatAssemblyEnd - Completes assembling the matrix.  This routine should
   be called after all calls to MatSetValues() and after MatAssemblyBegin().

   Input Parameters:
.  mat - the matrix
.  type - type of assembly, either FLUSH_ASSEMBLY or FINAL_ASSEMBLY

   Note:
   MatSetValues() generally caches the values.  The matrix is ready to
   use only after MatAssemblyBegin() and MatAssemblyEnd() have been called.
   Use FLUSH_ASSEMBLY when switching between ADD_VALUES and SetValues; use
   FINAL_ASSEMBLY for the final assembly before the matrix is used.

.keywords: matrix, assembly, assemble, end

.seealso: MatAssemblyBegin(), MatSetValues()
@*/
int MatAssemblyEnd(Mat mat,MatAssemblyType type)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  PLogEventBegin(MAT_AssemblyEnd,mat,0,0,0);
  if (mat->ops.assemblyend) {ierr = (*mat->ops.assemblyend)(mat,type); CHKERRQ(ierr);}
  PLogEventEnd(MAT_AssemblyEnd,mat,0,0,0);
  return 0;
}
/*@
   MatCompress - Tries to store the matrix in as little space as
   possible.  May fail if memory is already fully used, since it
   tries to allocate new space.

   Input Parameters:
.  mat - the matrix

.keywords: matrix, compress
@*/
int MatCompress(Mat mat)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (mat->ops.compress) return (*mat->ops.compress)(mat);
  return 0;
}
/*@
   MatSetOption - Sets a parameter option for a matrix. Some options
   may be specific to certain storage formats.  Some options
   determine how values will be inserted (or added). Sorted,
   row-oriented input will generally assemble the fastest. The default
   is row-oriented, nonsorted input.

   Input Parameters:
.  mat - the matrix
.  option - the option, one of the following:
$    ROW_ORIENTED, COLUMN_ORIENTED, ROWS_SORTED,
$    COLUMNS_SORTED, NO_NEW_NONZERO_LOCATIONS,
$    SYMMETRIC_MATRIX,
$    YES_NEW_NONZERO_LOCATIONS, and possibly others

   Notes:
   If using a Fortran 77 module to compute a matrix, one may need to
   use the column-oriented option (or convert to the row-oriented
   format).

   NO_NEW_NONZERO_LOCATIONS indicates that any add or insertion
   that will generate a new entry in the nonzero structure is ignored.
   What this means is if memory is not allocated for this particular
   lot, then the insertion is ignored. For dense matrices where
   the entire array is allocated no entries are ever ignored. This
   may not be a good idea??

.keywords: matrix, option, row-oriented, column-oriented, sorted, nonzero
@*/
int MatSetOption(Mat mat,MatOption op)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (mat->ops.setoption) return (*mat->ops.setoption)(mat,op);
  return 0;
}

/*@
   MatZeroEntries - Zeros all entries of a matrix.  For sparse matrices
   this routine retains the old nonzero structure.

   Input Parameters:
.  mat - the matrix

.keywords: matrix, zero, entries

.seealso: MatZeroRows()
@*/
int MatZeroEntries(Mat mat)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.zeroentries) SETERRQ(PETSC_ERR_SUP,"MatZeroEntries");
  PLogEventBegin(MAT_ZeroEntries,mat,0,0,0);
  ierr = (*mat->ops.zeroentries)(mat); CHKERRQ(ierr);
  PLogEventEnd(MAT_ZeroEntries,mat,0,0,0);
  return 0;
}

/*@
   MatZeroRows - Zeros all entries (except possibly the main diagonal)
   of a set of rows of a matrix.

   Input Parameters:
.  mat - the matrix
.  is - index set of rows to remove
.  diag - pointer to value put in all diagonals of eliminated rows.
          Note that diag is not a pointer to an array, but merely a
          pointer to a single value.

   Notes:
   For the AIJ and row matrix formats this removes the old nonzero
   structure, but does not release memory.  For the dense and block
   diagonal formats this does not alter the nonzero structure.

   The user can set a value in the diagonal entry (or for the AIJ and
   row formats can optionally remove the main diagonal entry from the
   nonzero structure as well, by passing a null pointer as the final
   argument).

.keywords: matrix, zero, rows, boundary conditions

.seealso: MatZeroEntries(), MatGetSubMatrix(), MatGetSubMatrixInPlace()
@*/
int MatZeroRows(Mat mat,IS is, Scalar *diag)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (mat->ops.zerorows) return (*mat->ops.zerorows)(mat,is,diag);
  SETERRQ(PETSC_ERR_SUP,"MatZeroRows");
}

/*@
   MatGetSize - Returns the numbers of rows and columns in a matrix.

   Input Parameter:
.  mat - the matrix

   Output Parameters:
.  m - the number of global rows
.  n - the number of global columns

.keywords: matrix, dimension, size, rows, columns, global, get

.seealso: MatGetLocalSize()
@*/
int MatGetSize(Mat mat,int *m,int* n)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!m || !n) SETERRQ(1,"MatGetSize:Bad address for result");
  return (*mat->ops.getsize)(mat,m,n);
}

/*@
   MatGetLocalSize - Returns the number of rows and columns in a matrix
   stored locally.  This information may be implementation dependent, so
   use with care.

   Input Parameters:
.  mat - the matrix

   Output Parameters:
.  m - the number of local rows
.  n - the number of local columns

.keywords: matrix, dimension, size, local, rows, columns, get

.seealso: MatGetSize()
@*/
int MatGetLocalSize(Mat mat,int *m,int* n)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!m || !n) SETERRQ(1,"MatGetLocalSize:Bad address for result");
  return (*mat->ops.getlocalsize)(mat,m,n);
}

/*@
   MatGetOwnershipRange - Returns the range of matrix rows owned by
   this processor, assuming that the matrix is laid out with the first
   n1 rows on the first processor, the next n2 rows on the second, etc.
   For certain parallel layouts this range may not be well-defined.

   Input Parameters:
.  mat - the matrix

   Output Parameters:
.  m - the first local row
.  n - one more then the last local row

.keywords: matrix, get, range, ownership
@*/
int MatGetOwnershipRange(Mat mat,int *m,int* n)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!m || !n) SETERRQ(1,"MatGetOwnershipRange:Bad address for result");
  if (mat->ops.getownershiprange) return (*mat->ops.getownershiprange)(mat,m,n);
  SETERRQ(PETSC_ERR_SUP,"MatGetOwnershipRange");
}

/*@
   MatILUFactorSymbolic - Performs symbolic ILU factorization of a matrix.
   Uses levels of fill only, not drop tolerance. Use MatLUFactorNumeric()
   to complete the factorization.

   Input Parameters:
.  mat - the matrix
.  row - row permutation
.  column - column permutation
.  fill - number of levels of fill
.  f - expected fill as ratio of original fill

   Output Parameters:
.  fact - puts factor

.keywords: matrix, factor, incomplete, ILU, symbolic, fill

.seealso: MatLUFactorSymbolic(), MatLUFactorNumeric()
@*/
int MatILUFactorSymbolic(Mat mat,IS row,IS col,double f,int fill,Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (fill < 0) SETERRQ(1,"MatILUFactorSymbolic:Levels of fill negative");
  if (!fact) SETERRQ(1,"MatILUFactorSymbolic:Fact argument is missing");
  if (!mat->ops.ilufactorsymbolic) SETERRQ(PETSC_ERR_SUP,"MatILUFactorSymbolic");
  PLogEventBegin(MAT_ILUFactorSymbolic,mat,row,col,0);
  ierr = (*mat->ops.ilufactorsymbolic)(mat,row,col,f,fill,fact);
  CHKERRQ(ierr);
  PLogEventEnd(MAT_ILUFactorSymbolic,mat,row,col,0);
  return 0;
}

/*@
   MatIncompleteCholeskyFactorSymbolic - Performs symbolic incomplete
   Cholesky factorization for a symmetric matrix.  Use
   MatCholeskyFactorNumeric() to complete the factorization.

   Input Parameters:
.  mat - the matrix
.  perm - row and column permutation
.  fill - levels of fill
.  f - expected fill as ratio of original fill

   Output Parameter:
.  fact - the factored matrix

   Note:  Currently only no-fill factorization is supported.

.keywords: matrix, factor, incomplete, ICC, Cholesky, symbolic, fill

.seealso: MatCholeskyFactorNumeric(), MatCholeskyFactor()
@*/
int MatIncompleteCholeskyFactorSymbolic(Mat mat,IS perm,double f,int fill,
                                        Mat *fact)
{
  int ierr;
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (fill < 0) SETERRQ(1,"MatIncompleteCholeskyFactorSymbolic:Fill negative");
  if (!fact) SETERRQ(1,"MatIncompleteCholeskyFactorSymbolic:Missing fact argument");
  if (!mat->ops.incompletecholeskyfactorsymbolic)
    SETERRQ(PETSC_ERR_SUP,"MatIncompleteCholeskyFactorSymbolic");
  PLogEventBegin(MAT_IncompleteCholeskyFactorSymbolic,mat,perm,0,0);
  ierr = (*mat->ops.incompletecholeskyfactorsymbolic)(mat,perm,f,fill,fact);
  CHKERRQ(ierr);
  PLogEventEnd(MAT_IncompleteCholeskyFactorSymbolic,mat,perm,0,0);
  return 0;
}

/*@C
   MatGetArray - Returns a pointer to the element values in the matrix.
   This routine  is implementation dependent, and may not even work for
   certain matrix types.

   Input Parameter:
.  mat - the matrix

   Output Parameter:
.  v - the location of the values

.keywords: matrix, array, elements, values
@*/
int MatGetArray(Mat mat,Scalar **v)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!v) SETERRQ(1,"MatGetArray:Bad input, array pointer location");
  if (!mat->ops.getarray) SETERRQ(PETSC_ERR_SUP,"MatGetArraye");
  return (*mat->ops.getarray)(mat,v);
}

/*@
   MatGetSubMatrix - Extracts a submatrix from a matrix.

   Input Parameters:
.  mat - the matrix
.  irow, icol - index sets of rows and columns to extract

   Output Parameter:
.  submat - the submatrix

   Notes:
   MatGetSubMatrix() can be useful in setting boundary conditions.

.keywords: matrix, get, submatrix, boundary conditions

.seealso: MatZeroRows(), MatGetSubMatrixInPlace()
@*/
int MatGetSubMatrix(Mat mat,IS irow,IS icol,Mat *submat)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.getsubmatrix) SETERRQ(PETSC_ERR_SUP,"MatGetSubMatrix");
  return (*mat->ops.getsubmatrix)(mat,irow,icol,submat);
}

/*@
   MatGetSubMatrixInPlace - Extracts a submatrix from a matrix, returning
   the submatrix in place of the original matrix.

   Input Parameters:
.  mat - the matrix
.  irow, icol - index sets of rows and columns to extract

.keywords: matrix, get, submatrix, boundary conditions, in-place

.seealso: MatZeroRows(), MatGetSubMatrix()
@*/
int MatGetSubMatrixInPlace(Mat mat,IS irow,IS icol)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  if (!mat->ops.getsubmatrixinplace) SETERRQ(PETSC_ERR_SUP,"MatGetSubmatrixInPlace");
  return (*mat->ops.getsubmatrixinplace)(mat,irow,icol);
}

/*@
   MatGetType - Returns the type of the matrix, one of MATSEQDENSE, MATSEQAIJ, etc.

   Input Parameter:
.  mat - the matrix

   Ouput Parameter:
.  type - the matrix type

   Notes:
   The matrix types are given in petsc/include/mat.h

.keywords: matrix, get, type

.seealso:  MatGetName()
@*/
int MatGetType(Mat mat,MatType *type)
{
  PETSCVALIDHEADERSPECIFIC(mat,MAT_COOKIE);
  *type = (MatType) mat->type;
  return 0;
}