tao/interface/taosolver_hj.c

#include <petsc-private/taoimpl.h> /*I "petsctao.h" I*/

#undef __FUNCT__
#define __FUNCT__ "TaoSetHessianRoutine"
/*@C
   TaoSetHessianRoutine - Sets the function to compute the Hessian as well as the location to store the matrix.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  H - Matrix used for the hessian
.  Hpre - Matrix that will be used operated on by preconditioner, can be same as H
.  hess - Hessian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Hessian evaluation routine (may be NULL)

   Calling sequence of hess:
$    hess (Tao tao,Vec x,Mat H,Mat Hpre,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  H - Hessian matrix
.  Hpre - preconditioner matrix, usually the same as H
   structure (see below)
-  ctx - [optional] user-defined Hessian context


   Notes:

   The flag can be used to eliminate unnecessary work in the preconditioner
   during the repeated solution of linear systems of the same size.  The
   available options are
$    SAME_PRECONDITIONER -
$      Hpre is identical during successive linear solves.
$      This option is intended for folks who are using
$      different Amat and Pmat matrices and want to reuse the
$      same preconditioner matrix.  For example, this option
$      saves work by not recomputing incomplete factorization
$      for ILU/ICC preconditioners.
$    SAME_NONZERO_PATTERN -
$      Hpre has the same nonzero structure during
$      successive linear solves.
$    DIFFERENT_NONZERO_PATTERN -
$      Hpre does not have the same nonzero structure.

   Caution:
   If you specify SAME_NONZERO_PATTERN, the software believes your assertion
   and does not check the structure of the matrix.  If you erroneously
   claim that the structure is the same when it actually is not, the new
   preconditioner will not function correctly.  Thus, use this optimization
   feature carefully!

   If in doubt about whether your preconditioner matrix has changed
   structure or not, use the flag DIFFERENT_NONZERO_PATTERN.

   Level: beginner

@*/
PetscErrorCode TaoSetHessianRoutine(Tao tao, Mat H, Mat Hpre, PetscErrorCode (*func)(Tao, Vec, Mat, Mat, void*), void *ctx)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (H) {
    PetscValidHeaderSpecific(H,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,H,2);
  }
  if (Hpre) {
    PetscValidHeaderSpecific(Hpre,MAT_CLASSID,3);
    PetscCheckSameComm(tao,1,Hpre,3);
  }
  if (ctx) {
    tao->user_hessP = ctx;
  }
  if (func) {
    tao->ops->computehessian = func;
  }
  if (H) {
    ierr = PetscObjectReference((PetscObject)H);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->hessian);CHKERRQ(ierr);
    tao->hessian = H;
  }
  if (Hpre) {
    ierr = PetscObjectReference((PetscObject)Hpre);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->hessian_pre);CHKERRQ(ierr);
    tao->hessian_pre = Hpre;
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeHessian"
/*@C
   TaoComputeHessian - Computes the Hessian matrix that has been
   set with TaoSetHessianRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
+  H - Hessian matrix
.  Hpre - Preconditioning matrix
-  flag - flag indicating matrix structure (SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN, or SAME_PRECONDITIONER)

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   TaoComputeHessian() is typically used within minimization
   implementations, so most users would not generally call this routine
   themselves.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetHessian()

@*/
PetscErrorCode TaoComputeHessian(Tao tao, Vec X, Mat H, Mat Hpre)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computehessian) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetHessian() first");
  ++tao->nhess;
  ierr = PetscLogEventBegin(Tao_HessianEval,tao,X,H,Hpre);CHKERRQ(ierr);
  PetscStackPush("Tao user Hessian function");
  ierr = (*tao->ops->computehessian)(tao,X,H,Hpre,tao->user_hessP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_HessianEval,tao,X,H,Hpre);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeJacobian"
/*@C
   TaoComputeJacobian - Computes the Jacobian matrix that has been
   set with TaoSetJacobianRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
+  H - Jacobian matrix
-  Hpre - Preconditioning matrix

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   TaoComputeJacobian() is typically used within minimization
   implementations, so most users would not generally call this routine
   themselves.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetJacobian()

@*/
PetscErrorCode TaoComputeJacobian(Tao tao, Vec X, Mat J, Mat Jpre)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computejacobian) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetJacobian() first");
  ++tao->njac;
  ierr = PetscLogEventBegin(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscStackPush("Tao user Jacobian function");
  ierr = (*tao->ops->computejacobian)(tao,X,J,Jpre,tao->user_jacP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeJacobianState"
/*@C
   TaoComputeJacobianState - Computes the Jacobian matrix that has been
   set with TaoSetJacobianStateRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
+  H - Jacobian matrix
-  Hpre - Preconditioning matrix

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   TaoComputeJacobianState() is typically used within minimization
   implementations, so most users would not generally call this routine
   themselves.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetJacobianStateRoutine(), TaoComputeJacobianDesign(), TaoSetStateDesignIS()

@*/
PetscErrorCode TaoComputeJacobianState(Tao tao, Vec X, Mat J, Mat Jpre, Mat Jinv)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computejacobianstate) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetJacobianState() first");
  ++tao->njac_state;
  ierr = PetscLogEventBegin(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscStackPush("Tao user Jacobian(state) function");
  ierr = (*tao->ops->computejacobianstate)(tao,X,J,Jpre,Jinv,tao->user_jac_stateP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeJacobianDesign"
/*@C
   TaoComputeJacobianDesign - Computes the Jacobian matrix that has been
   set with TaoSetJacobianDesignRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
.  H - Jacobian matrix

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   TaoComputeJacobianDesign() is typically used within minimization
   implementations, so most users would not generally call this routine
   themselves.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetJacobianDesignRoutine(), TaoComputeJacobianDesign(), TaoSetStateDesignIS()

@*/
PetscErrorCode TaoComputeJacobianDesign(Tao tao, Vec X, Mat J)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computejacobiandesign) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetJacobianDesign() first");
  ++tao->njac_design;
  ierr = PetscLogEventBegin(Tao_JacobianEval,tao,X,J,NULL);CHKERRQ(ierr);
  PetscStackPush("Tao user Jacobian(design) function");
  ierr = (*tao->ops->computejacobiandesign)(tao,X,J,tao->user_jac_designP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_JacobianEval,tao,X,J,NULL);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetJacobianRoutine"
/*@C
   TaoSetJacobianRoutine - Sets the function to compute the Jacobian as well as the location to store the matrix.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  J - Matrix used for the jacobian
.  Jpre - Matrix that will be used operated on by preconditioner, can be same as J
.  jac - Jacobian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Jacobian evaluation routine (may be NULL)

   Calling sequence of jac:
$    jac (Tao tao,Vec x,Mat *J,Mat *Jpre,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  J - Jacobian matrix
.  Jpre - preconditioner matrix, usually the same as J
.  flag - flag indicating information about the preconditioner matrix
   structure (see below)
-  ctx - [optional] user-defined Jacobian context

   Notes:

   The function jac() takes Mat * as the matrix arguments rather than Mat.
   This allows the Jacobian evaluation routine to replace A and/or B with a
   completely new new matrix structure (not just different matrix elements)
   when appropriate, for instance, if the nonzero structure is changing
   throughout the global iterations.

   The flag can be used to eliminate unnecessary work in the preconditioner
   during the repeated solution of linear systems of the same size.  The
   available options are
$    SAME_PRECONDITIONER -
$      Jpre is identical during successive linear solves.
$      This option is intended for folks who are using
$      different Amat and Pmat matrices and want to reuse the
$      same preconditioner matrix.  For example, this option
$      saves work by not recomputing incomplete factorization
$      for ILU/ICC preconditioners.
$    SAME_NONZERO_PATTERN -
$      Jpre has the same nonzero structure during
$      successive linear solves.
$    DIFFERENT_NONZERO_PATTERN -
$      Jpre does not have the same nonzero structure.

   Caution:
   If you specify SAME_NONZERO_PATTERN, the software believes your assertion
   and does not check the structure of the matrix.  If you erroneously
   claim that the structure is the same when it actually is not, the new
   preconditioner will not function correctly.  Thus, use this optimization
   feature carefully!

   If in doubt about whether your preconditioner matrix has changed
   structure or not, use the flag DIFFERENT_NONZERO_PATTERN.

   Level: intermediate

@*/
PetscErrorCode TaoSetJacobianRoutine(Tao tao, Mat J, Mat Jpre, PetscErrorCode (*func)(Tao, Vec, Mat, Mat, void*), void *ctx)
{
  PetscErrorCode ierr;
  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (J) {
    PetscValidHeaderSpecific(J,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,J,2);
  }
  if (Jpre) {
    PetscValidHeaderSpecific(Jpre,MAT_CLASSID,3);
    PetscCheckSameComm(tao,1,Jpre,3);
  }
  if (ctx) {
    tao->user_jacP = ctx;
  }
  if (func) {
    tao->ops->computejacobian = func;
  }
  if (J) {
    ierr = PetscObjectReference((PetscObject)J);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian);CHKERRQ(ierr);
    tao->jacobian = J;
  }
  if (Jpre) {
    ierr = PetscObjectReference((PetscObject)Jpre);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_pre);CHKERRQ(ierr);
    tao->jacobian_pre=Jpre;
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetJacobianStateRoutine"
/*@C
   TaoSetJacobianStateRoutine - Sets the function to compute the Jacobian
   (and its inverse) of the constraint function with respect to the state variables.
   Used only for pde-constrained optimization.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  J - Matrix used for the jacobian
.  Jpre - Matrix that will be used operated on by PETSc preconditioner, can be same as J.  Only used if Jinv is NULL
.  Jinv - [optional] Matrix used to apply the inverse of the state jacobian. Use NULL to default to PETSc KSP solvers to apply the inverse.
.  jac - Jacobian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Jacobian evaluation routine (may be NULL)

   Calling sequence of jac:
$    jac (Tao tao,Vec x,Mat *J,Mat *Jpre,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  J - Jacobian matrix
.  Jpre - preconditioner matrix, usually the same as J
.  Jinv - inverse of J
.  flag - flag indicating information about the preconditioner matrix
   structure (see below)
-  ctx - [optional] user-defined Jacobian context


   Notes:
   Because of the structure of the jacobian matrix,
   It may be more efficient for a pde-constrained application to provide
   its own Jinv matrix.

   The function jac() takes Mat * as the matrix arguments rather than Mat.
   This allows the Jacobian evaluation routine to replace A and/or B with a
   completely new new maitrix structure (not just different matrix elements)
   when appropriate, for instance, if the nonzero structure is changing
   throughout the global iterations.

   The flag can be used to eliminate unnecessary work in the preconditioner
   during the repeated solution of linear systems of the same size.  The
   available options are
$    SAME_PRECONDITIONER -
$      Jpre is identical during successive linear solves.
$      This option is intended for folks who are using
$      different Amat and Pmat matrices and want to reuse the
$      same preconditioner matrix.  For example, this option
$      saves work by not recomputing incomplete factorization
$      for ILU/ICC preconditioners.
$    SAME_NONZERO_PATTERN -
$      Jpre has the same nonzero structure during
$      successive linear solves.
$    DIFFERENT_NONZERO_PATTERN -
$      Jpre does not have the same nonzero structure.

   Caution:
   If you specify SAME_NONZERO_PATTERN, the software believes your assertion
   and does not check the structure of the matrix.  If you erroneously
   claim that the structure is the same when it actually is not, the new
   preconditioner will not function correctly.  Thus, use this optimization
   feature carefully!

   If in doubt about whether your preconditioner matrix has changed
   structure or not, use the flag DIFFERENT_NONZERO_PATTERN.

   Level: intermediate
.seealse: TaoComputeJacobianState(), TaoSetJacobianDesignRoutine(), TaoSetStateDesignIS()
@*/
PetscErrorCode TaoSetJacobianStateRoutine(Tao tao, Mat J, Mat Jpre, Mat Jinv, PetscErrorCode (*func)(Tao, Vec, Mat, Mat, Mat,void*), void *ctx)
{
  PetscErrorCode ierr;
  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (J) {
    PetscValidHeaderSpecific(J,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,J,2);
  }
  if (Jpre) {
    PetscValidHeaderSpecific(Jpre,MAT_CLASSID,3);
    PetscCheckSameComm(tao,1,Jpre,3);
  }
  if (Jinv) {
    PetscValidHeaderSpecific(Jinv,MAT_CLASSID,4);
    PetscCheckSameComm(tao,1,Jinv,4);
  }
  if (ctx) {
    tao->user_jac_stateP = ctx;
  }
  if (func) {
    tao->ops->computejacobianstate = func;
  }
  if (J) {
    ierr = PetscObjectReference((PetscObject)J);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_state);CHKERRQ(ierr);
    tao->jacobian_state = J;
  }
  if (Jpre) {
    ierr = PetscObjectReference((PetscObject)Jpre);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_state_pre);CHKERRQ(ierr);
    tao->jacobian_state_pre=Jpre;
  }
  if (Jinv) {
    ierr = PetscObjectReference((PetscObject)Jinv);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_state_inv);CHKERRQ(ierr);
    tao->jacobian_state_inv=Jinv;
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetJacobianDesignRoutine"
/*@C
   TaoSetJacobianDesignRoutine - Sets the function to compute the Jacobian of
   the constraint function with respect to the design variables.  Used only for
   pde-constrained optimization.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  J - Matrix used for the jacobian
.  jac - Jacobian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Jacobian evaluation routine (may be NULL)

   Calling sequence of jac:
$    jac (Tao tao,Vec x,Mat *J,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  J - Jacobian matrix
-  ctx - [optional] user-defined Jacobian context


   Notes:

   The function jac() takes Mat * as the matrix arguments rather than Mat.
   This allows the Jacobian evaluation routine to replace A and/or B with a
   completely new new matrix structure (not just different matrix elements)
   when appropriate, for instance, if the nonzero structure is changing
   throughout the global iterations.

   Level: intermediate
.seealso: TaoComputeJacobianDesign(), TaoSetJacobianStateRoutine(), TaoSetStateDesignIS()
@*/
PetscErrorCode TaoSetJacobianDesignRoutine(Tao tao, Mat J, PetscErrorCode (*func)(Tao, Vec, Mat, void*), void *ctx)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (J) {
    PetscValidHeaderSpecific(J,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,J,2);
  }
  if (ctx) {
    tao->user_jac_designP = ctx;
  }
  if (func) {
    tao->ops->computejacobiandesign = func;
  }
  if (J) {
    ierr = PetscObjectReference((PetscObject)J);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_design);CHKERRQ(ierr);
    tao->jacobian_design = J;
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetStateDesignIS"
/*@
   TaoSetStateDesignIS - Indicate to the Tao which variables in the
   solution vector are state variables and which are design.  Only applies to
   pde-constrained optimization.

   Logically Collective on Tao

   Input Parameters:
+  tao - The Tao context
.  s_is - the index set corresponding to the state variables
-  d_is - the index set corresponding to the design variables

   Level: intermediate

.seealso: TaoSetJacobianStateRoutine(), TaoSetJacobianDesignRoutine()
@*/
PetscErrorCode TaoSetStateDesignIS(Tao tao, IS s_is, IS d_is)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  ierr = PetscObjectReference((PetscObject)s_is);CHKERRQ(ierr);
  ierr = ISDestroy(&tao->state_is);CHKERRQ(ierr);
  tao->state_is = s_is;
  ierr = PetscObjectReference((PetscObject)(d_is));CHKERRQ(ierr);
  ierr = ISDestroy(&tao->design_is);CHKERRQ(ierr);
  tao->design_is = d_is;
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeJacobianEquality"
/*@C
   TaoComputeJacobianEquality - Computes the Jacobian matrix that has been
   set with TaoSetJacobianEqualityRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
+  H - Jacobian matrix
.  Hpre - Preconditioning matrix
-  flag - flag indicating matrix structure (SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN, or SAME_PRECONDITIONER)

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetJacobianStateRoutine(), TaoComputeJacobianDesign(), TaoSetStateDesignIS()

@*/
PetscErrorCode TaoComputeJacobianEquality(Tao tao, Vec X, Mat J, Mat Jpre)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computejacobianequality) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetJacobianEquality() first");
  ++tao->njac_equality;
  ierr = PetscLogEventBegin(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscStackPush("Tao user Jacobian(equality) function");
  ierr = (*tao->ops->computejacobianequality)(tao,X,J,Jpre,tao->user_jac_equalityP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoComputeJacobianInequality"
/*@C
   TaoComputeJacobianInequality - Computes the Jacobian matrix that has been
   set with TaoSetJacobianInequalityRoutine().

   Collective on Tao

   Input Parameters:
+  solver - the Tao solver context
-  xx - input vector

   Output Parameters:
+  H - Jacobian matrix
.  Hpre - Preconditioning matrix
-  flag - flag indicating matrix structure (SAME_NONZERO_PATTERN, DIFFERENT_NONZERO_PATTERN, or SAME_PRECONDITIONER)

   Notes:
   Most users should not need to explicitly call this routine, as it
   is used internally within the minimization solvers.

   Level: developer

.seealso:  TaoComputeObjective(), TaoComputeObjectiveAndGradient(), TaoSetJacobianStateRoutine(), TaoComputeJacobianDesign(), TaoSetStateDesignIS()

@*/
PetscErrorCode TaoComputeJacobianInequality(Tao tao, Vec X, Mat J, Mat Jpre)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  PetscValidHeaderSpecific(X, VEC_CLASSID,2);
  PetscCheckSameComm(tao,1,X,2);

  if (!tao->ops->computejacobianinequality) SETERRQ(PETSC_COMM_SELF,PETSC_ERR_ARG_WRONGSTATE,"Must call TaoSetJacobianInequality() first");
  ++tao->njac_inequality;
  ierr = PetscLogEventBegin(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscStackPush("Tao user Jacobian(inequality) function");
  ierr = (*tao->ops->computejacobianinequality)(tao,X,J,Jpre,tao->user_jac_inequalityP);CHKERRQ(ierr);
  PetscStackPop;
  ierr = PetscLogEventEnd(Tao_JacobianEval,tao,X,J,Jpre);CHKERRQ(ierr);
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetJacobianEqualityRoutine"
/*@C
   TaoSetJacobianEqualityRoutine - Sets the function to compute the Jacobian
   (and its inverse) of the constraint function with respect to the equality variables.
   Used only for pde-constrained optimization.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  J - Matrix used for the jacobian
.  Jpre - Matrix that will be used operated on by PETSc preconditioner, can be same as J.
.  jac - Jacobian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Jacobian evaluation routine (may be NULL)

   Calling sequence of jac:
$    jac (Tao tao,Vec x,Mat *J,Mat *Jpre,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  J - Jacobian matrix
.  Jpre - preconditioner matrix, usually the same as J
.  flag - flag indicating information about the preconditioner matrix
   structure (see below)
-  ctx - [optional] user-defined Jacobian context

   Notes:
   Because of the structure of the jacobian matrix,
   It may be more efficient for a pde-constrained application to provide
   its own Jinv matrix.

   The function jac() takes Mat * as the matrix arguments rather than Mat.
   This allows the Jacobian evaluation routine to replace A and/or B with a
   completely new new maitrix structure (not just different matrix elements)
   when appropriate, for instance, if the nonzero structure is changing
   throughout the global iterations.

   The flag can be used to eliminate unnecessary work in the preconditioner
   during the repeated solution of linear systems of the same size.  The
   available options are
$    SAME_PRECONDITIONER -
$      Jpre is identical during successive linear solves.
$      This option is intended for folks who are using
$      different Amat and Pmat matrices and want to reuse the
$      same preconditioner matrix.  For example, this option
$      saves work by not recomputing incomplete factorization
$      for ILU/ICC preconditioners.
$    SAME_NONZERO_PATTERN -
$      Jpre has the same nonzero structure during
$      successive linear solves.
$    DIFFERENT_NONZERO_PATTERN -
$      Jpre does not have the same nonzero structure.

   Caution:
   If you specify SAME_NONZERO_PATTERN, the software believes your assertion
   and does not check the structure of the matrix.  If you erroneously
   claim that the structure is the same when it actually is not, the new
   preconditioner will not function correctly.  Thus, use this optimization
   feature carefully!

   If in doubt about whether your preconditioner matrix has changed
   structure or not, use the flag DIFFERENT_NONZERO_PATTERN.

   Level: intermediate
.seealse: TaoComputeJacobianEquality(), TaoSetJacobianDesignRoutine(), TaoSetEqualityDesignIS()
@*/
PetscErrorCode TaoSetJacobianEqualityRoutine(Tao tao, Mat J, Mat Jpre, PetscErrorCode (*func)(Tao, Vec, Mat, Mat,void*), void *ctx)
{
  PetscErrorCode ierr;

  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (J) {
    PetscValidHeaderSpecific(J,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,J,2);
  }
  if (Jpre) {
    PetscValidHeaderSpecific(Jpre,MAT_CLASSID,3);
    PetscCheckSameComm(tao,1,Jpre,3);
  }
  if (ctx) {
    tao->user_jac_equalityP = ctx;
  }
  if (func) {
    tao->ops->computejacobianequality = func;
  }
  if (J) {
    ierr = PetscObjectReference((PetscObject)J);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_equality);CHKERRQ(ierr);
    tao->jacobian_equality = J;
  }
  if (Jpre) {
    ierr = PetscObjectReference((PetscObject)Jpre);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_equality_pre);CHKERRQ(ierr);
    tao->jacobian_equality_pre=Jpre;
  }
  PetscFunctionReturn(0);
}

#undef __FUNCT__
#define __FUNCT__ "TaoSetJacobianInequalityRoutine"
/*@C
   TaoSetJacobianInequalityRoutine - Sets the function to compute the Jacobian
   (and its inverse) of the constraint function with respect to the inequality variables.
   Used only for pde-constrained optimization.

   Logically collective on Tao

   Input Parameters:
+  tao - the Tao context
.  J - Matrix used for the jacobian
.  Jpre - Matrix that will be used operated on by PETSc preconditioner, can be same as J.
.  jac - Jacobian evaluation routine
-  ctx - [optional] user-defined context for private data for the
         Jacobian evaluation routine (may be NULL)

   Calling sequence of jac:
$    jac (Tao tao,Vec x,Mat *J,Mat *Jpre,void *ctx);

+  tao - the Tao  context
.  x - input vector
.  J - Jacobian matrix
.  Jpre - preconditioner matrix, usually the same as J
.  flag - flag indicating information about the preconditioner matrix
   structure (see below)
-  ctx - [optional] user-defined Jacobian context


   Notes:
   Because of the structure of the jacobian matrix,
   It may be more efficient for a pde-constrained application to provide
   its own Jinv matrix.

   The function jac() takes Mat * as the matrix arguments rather than Mat.
   This allows the Jacobian evaluation routine to replace A and/or B with a
   completely new new maitrix structure (not just different matrix elements)
   when appropriate, for instance, if the nonzero structure is changing
   throughout the global iterations.

   The flag can be used to eliminate unnecessary work in the preconditioner
   during the repeated solution of linear systems of the same size.  The
   available options are
$    SAME_PRECONDITIONER -
$      Jpre is identical during successive linear solves.
$      This option is intended for folks who are using
$      different Amat and Pmat matrices and want to reuse the
$      same preconditioner matrix.  For example, this option
$      saves work by not recomputing incomplete factorization
$      for ILU/ICC preconditioners.
$    SAME_NONZERO_PATTERN -
$      Jpre has the same nonzero structure during
$      successive linear solves.
$    DIFFERENT_NONZERO_PATTERN -
$      Jpre does not have the same nonzero structure.

   Caution:
   If you specify SAME_NONZERO_PATTERN, the software believes your assertion
   and does not check the structure of the matrix.  If you erroneously
   claim that the structure is the same when it actually is not, the new
   preconditioner will not function correctly.  Thus, use this optimization
   feature carefully!

   If in doubt about whether your preconditioner matrix has changed
   structure or not, use the flag DIFFERENT_NONZERO_PATTERN.

   Level: intermediate
.seealse: TaoComputeJacobianInequality(), TaoSetJacobianDesignRoutine(), TaoSetInequalityDesignIS()
@*/
PetscErrorCode TaoSetJacobianInequalityRoutine(Tao tao, Mat J, Mat Jpre, PetscErrorCode (*func)(Tao, Vec, Mat, Mat,void*), void *ctx)
{
  PetscErrorCode ierr;
  PetscFunctionBegin;
  PetscValidHeaderSpecific(tao,TAO_CLASSID,1);
  if (J) {
    PetscValidHeaderSpecific(J,MAT_CLASSID,2);
    PetscCheckSameComm(tao,1,J,2);
  }
  if (Jpre) {
    PetscValidHeaderSpecific(Jpre,MAT_CLASSID,3);
    PetscCheckSameComm(tao,1,Jpre,3);
  }
  if (ctx) {
    tao->user_jac_inequalityP = ctx;
  }
  if (func) {
    tao->ops->computejacobianinequality = func;
  }
  if (J) {
    ierr = PetscObjectReference((PetscObject)J);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_inequality);CHKERRQ(ierr);
    tao->jacobian_inequality = J;
  }
  if (Jpre) {
    ierr = PetscObjectReference((PetscObject)Jpre);CHKERRQ(ierr);
    ierr = MatDestroy(&tao->jacobian_inequality_pre);CHKERRQ(ierr);
    tao->jacobian_inequality_pre=Jpre;
  }
  PetscFunctionReturn(0);
}