void
LOCA::MultiContinuation::ExtendedGroup::projectToDraw(
const LOCA::MultiContinuation::ExtendedVector& x,
double *px) const
{
// first numParams components are the parameters
for (int i=0; i<numParams; i++)
px[i] = x.getScalar(i);
// fill remaining solution components
grpPtr->projectToDraw(*x.getXVec(), px+numParams);
}
double
LOCA::MultiContinuation::ConstrainedGroup::getNormNewtonSolveResidual() const
{
std::string callingFunction =
"LOCA::MultiContinuation::ConstrainedGroup::getNormNewtonSolveResidual()";
NOX::Abstract::Group::ReturnType finalStatus;
LOCA::MultiContinuation::ExtendedVector residual = *fVec;
finalStatus = applyJacobian(*newtonVec, residual);
globalData->locaErrorCheck->checkReturnType(finalStatus, callingFunction);
residual = residual.update(1.0, *fVec, 1.0);
return residual.norm();
}
void
LOCA::MultiPredictor::AbstractStrategy::setPredictorOrientation(
bool baseOnSecant,
const vector<double>& stepSize,
const LOCA::MultiContinuation::ExtendedGroup& grp,
const LOCA::MultiContinuation::ExtendedVector& prevXVec,
const LOCA::MultiContinuation::ExtendedVector& xVec,
LOCA::MultiContinuation::ExtendedVector& secant,
LOCA::MultiContinuation::ExtendedMultiVector& tangent)
{
// If orientation is not based on a secant vector (i.e., first or last
// steps in a continuation run) make parameter component of predictor
// positive
if (!baseOnSecant) {
for (int i=0; i<tangent.numVectors(); i++)
if (tangent.getScalar(i,i) < 0.0)
tangent[i].scale(-1.0);
return;
}
// compute secant
secant.update(1.0, xVec, -1.0, prevXVec, 0.0);
for (int i=0; i<tangent.numVectors(); i++)
if (grp.computeScaledDotProduct(secant, tangent[i])*stepSize[i] < 0.0)
tangent[i].scale(-1.0);
}
NOX::Abstract::Group::ReturnType
LOCA::MultiPredictor::Constant::compute(
bool baseOnSecant, const std::vector<double>& stepSize,
LOCA::MultiContinuation::ExtendedGroup& grp,
const LOCA::MultiContinuation::ExtendedVector& prevXVec,
const LOCA::MultiContinuation::ExtendedVector& xVec)
{
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperDetails))
globalData->locaUtils->out() <<
"\n\tCalling Predictor with method: Constant" << std::endl;
// Number of continuation parameters
int numParams = stepSize.size();
if (!initialized) {
// Allocate predictor vector
predictor = Teuchos::rcp_dynamic_cast<LOCA::MultiContinuation::ExtendedMultiVector>(xVec.createMultiVector(numParams, NOX::ShapeCopy));
// Allocate secant
secant = Teuchos::rcp_dynamic_cast<LOCA::MultiContinuation::ExtendedVector>(xVec.clone(NOX::ShapeCopy));
initialized = true;
}
predictor->init(0.0);
for (int i=0; i<numParams; i++)
predictor->getScalar(i,i) = 1.0;
// Set orientation based on parameter change
setPredictorOrientation(baseOnSecant, stepSize, grp, prevXVec,
xVec, *secant, *predictor);
return NOX::Abstract::Group::Ok;
}
NOX::Abstract::Group::ReturnType
LOCA::MultiPredictor::Secant::compute(
bool baseOnSecant, const std::vector<double>& stepSize,
LOCA::MultiContinuation::ExtendedGroup& grp,
const LOCA::MultiContinuation::ExtendedVector& prevXVec,
const LOCA::MultiContinuation::ExtendedVector& xVec)
{
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperDetails))
globalData->locaUtils->out() <<
"\n\tCalling Predictor with method: Secant" << std::endl;
// Number of continuation parameters
int numParams = stepSize.size();
if (!initialized) {
// Allocate predictor vector
predictor = Teuchos::rcp_dynamic_cast<LOCA::MultiContinuation::ExtendedMultiVector>(xVec.createMultiVector(numParams, NOX::ShapeCopy));
// Allocate secant
secant = Teuchos::rcp_dynamic_cast<LOCA::MultiContinuation::ExtendedVector>(xVec.clone(NOX::ShapeCopy));
initialized = true;
}
// Use first step predictor if this is the first step
if (isFirstStep && !isFirstStepComputed) {
isFirstStepComputed = true;
return firstStepPredictor->compute(baseOnSecant, stepSize, grp,
prevXVec, xVec);
}
if (isFirstStep && isFirstStepComputed)
isFirstStep = false;
// Compute x - xold
(*predictor)[0].update(1.0, xVec, -1.0, prevXVec, 0.0);
for (int i=0; i<numParams; i++) {
(*predictor)[i] = (*predictor)[0];
// Rescale so parameter component = 1
(*predictor)[i].scale(1.0/fabs(predictor->getScalar(i,i)));
// Set off-diagonal elements to 0
for (int j=0; j<numParams; j++)
if (i != j)
predictor->getScalar(i,j) = 0.0;
}
// Set orientation based on parameter change
setPredictorOrientation(baseOnSecant, stepSize, grp, prevXVec,
xVec, *secant, *predictor);
return NOX::Abstract::Group::Ok;
}
NOX::Abstract::Group::ReturnType
LOCA::StepSize::Adaptive::computeStepSize(
LOCA::MultiContinuation::AbstractStrategy& curGroup,
const LOCA::MultiContinuation::ExtendedVector& predictor,
const NOX::Solver::Generic& solver,
const LOCA::Abstract::Iterator::StepStatus& stepStatus,
const LOCA::Stepper& stepper,
double& stepSize)
{
// If this is the first step, set step size to initial value
if (isFirstStep) {
double dpds = predictor.getScalar(0);
if (dpds != 0.0) {
LOCA::StepSize::Constant::startStepSize /= dpds;
LOCA::StepSize::Constant::maxStepSize /= dpds;
LOCA::StepSize::Constant::minStepSize /= dpds;
}
LOCA::StepSize::Constant::isFirstStep = false;
stepSize = LOCA::StepSize::Constant::startStepSize;
prevStepSize = 0.0;
}
else {
// A failed nonlinear solve cuts the step size in half
if (stepStatus == LOCA::Abstract::Iterator::Unsuccessful) {
stepSize *= LOCA::StepSize::Constant::failedFactor;
}
else {
double ds_ratio = curGroup.getStepSizeScaleFactor();
LOCA::StepSize::Constant::startStepSize *= ds_ratio;
LOCA::StepSize::Constant::maxStepSize *= ds_ratio;
LOCA::StepSize::Constant::minStepSize *= ds_ratio;
// Get number of nonlinear iterations in last step
double numNonlinearSteps =
static_cast<double>(solver.getNumIterations());
// Save successful stepsize as previous
prevStepSize = stepSize;
// adapive step size control
double factor = (maxNonlinearSteps - numNonlinearSteps)
/ (maxNonlinearSteps);
stepSize *= (1.0 + agrValue * factor * factor);
stepSize *= ds_ratio;
}
}
// Clip step size to be within prescribed bounds
NOX::Abstract::Group::ReturnType res = clipStepSize(stepSize);
return res;
}
void
LOCA::MultiContinuation::ExtendedGroup::scaleTangent()
{
LOCA::MultiContinuation::ExtendedVector *v;
scaledTangentMultiVec = tangentMultiVec;
// Only scale the tangent if it is scalable
if (predictor->isTangentScalable()) {
for (int i=0; i<numParams; i++) {
v =
dynamic_cast<LOCA::MultiContinuation::ExtendedVector*>(&scaledTangentMultiVec[i]);
grpPtr->scaleVector(*(v->getXVec()));
grpPtr->scaleVector(*(v->getXVec()));
}
}
}
NOX::Abstract::Group::ReturnType
LOCA::StepSize::Constant::computeStepSize(
LOCA::MultiContinuation::AbstractStrategy& curGroup,
const LOCA::MultiContinuation::ExtendedVector& predictor,
const NOX::Solver::Generic& solver,
const LOCA::Abstract::Iterator::StepStatus& stepStatus,
// const LOCA::Stepper& stepper,
const LOCA::Abstract::Iterator& stepper,
double& stepSize)
{
// If this is the first step, set step size to initial value adjusted
// to predicted change in parameter
if (isFirstStep) {
double dpds = predictor.getScalar(0);
if (dpds != 0.0) {
startStepSize /= dpds;
maxStepSize /= dpds;
minStepSize /= dpds;
}
stepSize = startStepSize;
isFirstStep = false;
prevStepSize = 0.0;
}
else {
// Step size remains constant, unless...
// A failed nonlinear solve cuts the step size by failedFactor
if (stepStatus == LOCA::Abstract::Iterator::Unsuccessful) {
stepSize *= failedFactor;
}
else {
double ds_ratio = curGroup.getStepSizeScaleFactor();
startStepSize *= ds_ratio;
maxStepSize *= ds_ratio;
minStepSize *= ds_ratio;
prevStepSize = stepSize;
stepSize *= ds_ratio;
// For constant step size, the step size may still have been
// reduced by a solver failure. We then increase the step size
// by a factor of cube-root-2 until back to the original step size
if (stepSize != startStepSize) {
stepSize *= successFactor;
if (startStepSize > 0.0)
stepSize = NOX_MIN(stepSize, startStepSize);
else
stepSize = NOX_MAX(stepSize, startStepSize);
}
}
}
// Clip step size to be within prescribed bounds
NOX::Abstract::Group::ReturnType res = clipStepSize(stepSize);
return res;
}
