void NOX::MeritFunction::SumOfSquares::
computeGradient(const NOX::Abstract::Group& grp,
NOX::Abstract::Vector& result) const
{
if ( !(grp.isF()) ) {
utils->err()
<< "ERROR: NOX::MeritFunction::SumOfSquares::computeGradient() - "
<< "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::computeGradient() - "
<< "Jacobian has not been computed yet!. Please call "
<< "computeJacobian() on the group passed into this function."
<< std::endl;
throw "NOX Error";
}
NOX::Abstract::Group::ReturnType status =
grp.applyJacobianTranspose(grp.getF(), result);
if (status != NOX::Abstract::Group::Ok) {
utils->err() << "ERROR: NOX::MeritFunction::SumOfSquares::compute"
<< "Gradient - applyJacobianTranspose failed!" << std::endl;
throw "NOX Error";
}
return;
}
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) );
}
double NOX::MeritFunction::SumOfSquares::
computeSlope(const NOX::Abstract::Vector& dir,
const NOX::Abstract::Group& grp) const
{
if (Teuchos::is_null(tmpVecPtr))
tmpVecPtr = grp.getF().clone();
// If the Jacobian is not computed, approximate it with
// directional derivatives. dir^T J^T F = F^T Jd
if (!(grp.isJacobian()))
return this->computeSlopeWithoutJacobian(dir, grp);
// If the Jacobian is computed but doesn't support a gradient,
// employ a different form for the inner product, eg
// return <v, F> = F' * J * dir = <J'F, dir> = <g, dir>
else if(!(grp.isGradient()))
return this->computeSlopeWithoutJacobianTranspose(dir, grp);
this->computeGradient(grp, *(tmpVecPtr.get()));
return dir.innerProduct(*(tmpVecPtr.get()));
}
// **************************************************************************
// *** 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;
}
// Compute forcing term
eta_k = fabs(normf - normpredf) / normoldf;
// Some output
if (printing->isPrintType(NOX::Utils::Details)) {
printing->out() << indent << "Residual Norm k-1 = "
<< normoldf << "\n";
printing->out() << indent << "Residual Norm Linear Model k = "
<< normpredf << "\n";
printing->out() << indent << "Residual Norm k = " << normf << "\n";
printing->out() << indent << "Calculated eta_k (pre-bounds) = " << eta_k << std::endl;
}
// Impose safeguard and constraints ...
const double tmp = (1.0 + sqrt(5.0)) / 2.0;
const double eta_km1_alpha = pow(eta_km1, tmp);
if (eta_km1_alpha > 0.1)
eta_k = NOX_MAX(eta_k, eta_km1_alpha);
eta_k = NOX_MAX(eta_k, eta_min);
eta_k = NOX_MIN(eta_max, eta_k);
}
}
else if (forcingTermMethod == Type2) {
if (niter == 0) {
eta_k = eta_initial;
}
else {
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;
}
const double residual_ratio = normf / normoldf;
eta_k = gamma * pow(residual_ratio, alpha);
// Some output
if (printing->isPrintType(NOX::Utils::Details)) {
printing->out() << indent << "Residual Norm k-1 = "
<< normoldf << "\n";
printing->out() << indent << "Residual Norm k = "
<< normf << "\n";
printing->out() << indent << "Calculated eta_k (pre-bounds) = "
<< eta_k << std::endl;
}
// Impose safeguard and constraints ...
const double eta_k_alpha = gamma * pow(eta_km1, alpha);
if (eta_k_alpha > 0.1)
eta_k = NOX_MAX(eta_k, eta_k_alpha);
eta_k = NOX_MAX(eta_k, eta_min);
eta_k = NOX_MIN(eta_max, eta_k);
}
}
// Set the new linear solver tolerance
if (setTolerance)
paramsPtr->sublist(directionMethod).sublist("Linear Solver").
set("Tolerance", eta_k);
if (printing->isPrintType(NOX::Utils::Details))
printing->out() << indent << "Forcing Term: " << eta_k << std::endl;
return eta_k;
}
