C++ Group::applyJacobian方法代码示例

double
NOX::Solver::TensorBased::getNormModelResidual(
                                       const NOX::Abstract::Vector& dir,
                       const NOX::Abstract::Group& soln,
                       bool isTensorModel) const
{

  // Compute residual of Newton model...
  Teuchos::RCP<NOX::Abstract::Vector> residualPtr =
    soln.getF().clone(ShapeCopy);
  soln.applyJacobian(dir, *residualPtr);
  numJvMults++;
  residualPtr->update(1.0, soln.getF(), 1.0);

  // Compute residual of Tensor model, if requested...
  if (isTensorModel)
  {
    double tmp = sVecPtr->innerProduct(dir);
    if (utilsPtr->isPrintType(NOX::Utils::Details))
      utilsPtr->out() << " sc'*dt   = " << utilsPtr->sciformat(tmp, 6) << std::endl;
    residualPtr->update(tmp*tmp, *aVecPtr, 1.0);
  }

  double modelNorm = residualPtr->norm();
  return modelNorm;
}

void NOX::MeritFunction::SumOfSquares::
computeQuadraticMinimizer(const NOX::Abstract::Group& grp,
              NOX::Abstract::Vector& result) const
{
  // Clone a temporary vector
  if (Teuchos::is_null(tmpVecPtr))
    tmpVecPtr = grp.getF().clone(NOX::ShapeCopy);

  // Make sure the function and Jacobian have been evaluated
  if ( !(grp.isF()) ) {
    utils->err()
      << "ERROR: NOX::MeritFunction::SumOfSquares::"
      << "computeQuadraticMinimizer() - "
      << "F has not been computed yet!.  Please call "
      << "computeF() on the group passed into this function."
      << std::endl;
    throw "NOX Error";
  }

  if ( !(grp.isJacobian()) ) {
    utils->err()
      << "ERROR: NOX::MeritFunction::SumOfSquares::"
      << "computeQuadraticMinimizer() - "
      << "Jacobian has not been computed yet!.  Please call "
      << "computeJacobian() on the group passed into this function."
      << std::endl;
    throw "NOX Error";
  }

  // Compute the steepest descent direction = J^T F
  this->computeGradient(grp, result);

  // Compute = J (J^T F)
  NOX::Abstract::Group::ReturnType status =
    grp.applyJacobian(result, *tmpVecPtr);
  if (status != NOX::Abstract::Group::Ok) {
    utils->err()
      << "ERROR: NOX::MeritFunction::SumOfSquares::"
      << "computeQuadraticMinimizer() - grp->applyJacobian() has failed!"
      << std::endl;
    throw "NOX Error";
  }

  result.scale( -1.0 * result.innerProduct(result) /
        tmpVecPtr->innerProduct(*tmpVecPtr) );

}

// **************************************************************************
// *** computeForcingTerm
// **************************************************************************
double NOX::Direction::Utils::InexactNewton::
computeForcingTerm(const NOX::Abstract::Group& soln,
		   const NOX::Abstract::Group& oldsoln, 
		   int niter,
		   const NOX::Solver::Generic& solver,
		   double eta_last)
{
  const std::string indent = "       ";

  if (forcingTermMethod == Constant) {
    if (printing->isPrintType(NOX::Utils::Details)) {
      printing->out() << indent << "CALCULATING FORCING TERM" << std::endl;
      printing->out() << indent << "Method: Constant" << std::endl;
      printing->out() << indent << "Forcing Term: " << eta_k << std::endl;
    }
    if (setTolerance)
      paramsPtr->sublist(directionMethod).sublist("Linear Solver").
	set("Tolerance", eta_k);

    return eta_k;
  }

  // Get linear solver current tolerance. 
  // NOTE: These values are changing at each nonlinear iteration and 
  // must either be updated from the parameter list each time a compute 
  // is called or supplied during the function call!
  double eta_km1 = 0.0;
  if (eta_last < 0.0)
    eta_km1 = paramsPtr->sublist(directionMethod).
      sublist("Linear Solver").get("Tolerance", 0.0);
  else
    eta_km1 = eta_last;

  // Tolerance may have been adjusted in a line search algorithm so we 
  // have to account for this.
  const NOX::Solver::LineSearchBased* solverPtr = 0;
  solverPtr = dynamic_cast<const NOX::Solver::LineSearchBased*>(&solver);
  if (solverPtr != 0) {
    eta_km1 = 1.0 - solverPtr->getStepSize() * (1.0 - eta_km1);
  }

  if (printing->isPrintType(NOX::Utils::Details)) {
    printing->out() << indent << "CALCULATING FORCING TERM" << std::endl;
    printing->out() << indent << "Method: " << method << std::endl;
  }


  if (forcingTermMethod == Type1) {
    
    if (niter == 0) {
      
      eta_k = eta_initial;

    }
    else {

      // Return norm of predicted F

      // do NOT use the following lines!! This does NOT account for 
      // line search step length taken.
      // const double normpredf = 0.0;
      // oldsoln.getNormLastLinearSolveResidual(normpredf);
      
      // Create a new vector to be the predicted RHS
      if (Teuchos::is_null(predRhs)) {
	predRhs = oldsoln.getF().clone(ShapeCopy);
      }
      if (Teuchos::is_null(stepDir)) {
	stepDir = oldsoln.getF().clone(ShapeCopy);
      }
      
      // stepDir = X - oldX (i.e., the step times the direction)
      stepDir->update(1.0, soln.getX(), -1.0, oldsoln.getX(), 0);
      
      // Compute predRhs = Jacobian * step * dir
      if (!(oldsoln.isJacobian())) {
	if (printing->isPrintType(NOX::Utils::Details)) {
	  printing->out() << "WARNING: NOX::InexactNewtonUtils::resetForcingTerm() - "
	       << "Jacobian is out of date! Recomputing Jacobian." << std::endl;
	}
	const_cast<NOX::Abstract::Group&>(oldsoln).computeJacobian();
      }
      oldsoln.applyJacobian(*stepDir, *predRhs);

      // Compute predRhs = RHSVector + predRhs (this is the predicted RHS)
      predRhs->update(1.0, oldsoln.getF(), 1.0);
      
      // Compute the norms
      double normpredf = predRhs->norm();
      double normf = soln.getNormF();
      double normoldf = oldsoln.getNormF();

      if (printing->isPrintType(NOX::Utils::Details)) {
	printing->out() << indent << "Forcing Term Norm: Using L-2 Norm."
			<< std::endl;
      }

//.........这里部分代码省略.........

bool
NOX::Solver::TensorBased::computeTensorDirection(NOX::Abstract::Group& soln,
                     const NOX::Solver::Generic& solver)
{
  NOX::Abstract::Group::ReturnType dir_status;

  Teuchos::ParameterList& linearParams = paramsPtr->sublist("Direction").
    sublist(paramsPtr->sublist("Direction").
        get("Method","Tensor")).
    sublist("Linear Solver");

  // Compute F at current solution.
  dir_status = soln.computeF();
  if (dir_status != NOX::Abstract::Group::Ok)
    throwError("computeTensorDirection", "Unable to compute F");

  // Compute Jacobian at current solution.
  dir_status = soln.computeJacobian();
  if (dir_status != NOX::Abstract::Group::Ok)
    throwError("computeTensorDirection", "Unable to compute Jacobian");

  // Begin processing for the tensor step, if necessary.
  double sDotS = 0.0;
  int tempVal1 = 0;
  if ((nIter > 0)  &&  (requestedBaseStep == TensorStep))
  {
    // Compute the tensor term s = x_{k-1} - x_k
    *sVecPtr = soln.getX();
    sVecPtr->update(1.0, solver.getPreviousSolutionGroup().getX(), -1.0);
    double normS = sVecPtr->norm();
    sDotS = normS * normS;

    // Form the tensor term a = (F_{k-1} - F_k - J*s) / (s^T s)^2
    soln.applyJacobian(*sVecPtr, *aVecPtr);
    numJvMults++;
    aVecPtr->update(1.0, solver.getPreviousSolutionGroup().getF(), -1.0);
    aVecPtr->update(-1.0, soln.getF(), 1.0);
    if (sDotS != 0)
      aVecPtr->scale(1.0 / (sDotS * sDotS));

    // Save old Newton step as initial guess to second system
    *tmpVecPtr = *newtonVecPtr;
    tmpVecPtr->scale(-1.0);   // Rewrite to avoid this?

    // Compute residual of linear system using initial guess...
    soln.applyJacobian(*tmpVecPtr, *residualVecPtr);
    numJvMults++;
    residualVecPtr->update(1.0, solver.getPreviousSolutionGroup().getF(),-1.0);
    double residualNorm = residualVecPtr->norm();

#if DEBUG_LEVEL > 0
    double tmpVecNorm = tmpVecPtr->norm();
    double residualNormRel = residualNorm /
      solver.getPreviousSolutionGroup().getNormF();
    if (utilsPtr->isPrintType(NOX::Utils::Details))
    {
      utilsPtr->out() << "  Norm of initial guess: " << utilsPtr->sciformat(tmpVecNorm, 6)
       << std::endl;
      utilsPtr->out() << "  initg norm of model residual =   "
       << utilsPtr->sciformat(residualNorm, 6) << " (abs)     "
       << utilsPtr->sciformat(residualNormRel, 6) << " (rel)" << std::endl;
    }
#endif

    // Save some parameters and use them later...
    double tol = linearParams.get("Tolerance", 1e-4);
    double relativeResidual = residualNorm /
      solver.getPreviousSolutionGroup().getNormF();

    // Decide whether to use initial guess...
    bool isInitialGuessGood = false;
#ifdef USE_INITIAL_GUESS_LOGIC
    if (relativeResidual < 1.0)
    {
      if (utilsPtr->isPrintType(NOX::Utils::Details))
    utilsPtr->out() << "  Initial guess is good..." << std::endl;
      isInitialGuessGood = true;
      // RPP - Brett please make sure the line below is correct.
      *tensorVecPtr = *tmpVecPtr;
      double newTol = tol / relativeResidual;
      if (newTol > 0.99)
    newTol = 0.99;  // force at least one iteration
      linearParams.set("Tolerance",  newTol);
      if (utilsPtr->isPrintType(NOX::Utils::Details))
    utilsPtr->out() << "  Setting tolerance to " << utilsPtr->sciformat(newTol,6) << std::endl;
    }
    else
#endif // USE_INITIAL_GUESS_LOGIC
    {
      //utilsPtr->out() << "  Initial guess is BAD... do not use!\n";
      isInitialGuessGood = false;
      *residualVecPtr = solver.getPreviousSolutionGroup().getF();
    }

    // Compute the term inv(J)*Fp....
    tmpVecPtr->init(0.0);
    dir_status = soln.applyJacobianInverse(linearParams, *residualVecPtr,
                       *tmpVecPtr);

    // If it didn't converge, maybe we can recover.
//.........这里部分代码省略.........