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;
}
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;
}
double NOX::StatusTest::NormF::computeNorm(const NOX::Abstract::Group& grp)
{
if (!grp.isF())
return -1.0;
double norm;
int n = grp.getX().length();
switch (normType)
{
case NOX::Abstract::Vector::TwoNorm:
norm = grp.getNormF();
if (scaleType == Scaled)
norm /= sqrt(1.0 * n);
break;
default:
norm = grp.getF().norm(normType);
if (scaleType == Scaled)
norm /= n;
break;
}
return norm;
}
bool NOX::Direction::ModifiedNewton::
compute(NOX::Abstract::Vector& dir,
NOX::Abstract::Group& soln,
const NOX::Solver::Generic& solver)
{
NOX::Abstract::Group::ReturnType status;
// Compute F at current solution
status = soln.computeF();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute F");
maxAgeOfJacobian = paramsPtr->sublist("Modified-Newton").get("Max Age of Jacobian", 10);
if (Teuchos::is_null(oldJacobianGrpPtr)) {
oldJacobianGrpPtr = soln.clone(DeepCopy);
}
NOX::Abstract::Group& oldJacobianGrp = *oldJacobianGrpPtr;
status = NOX::Abstract::Group::Failed;
while (status != NOX::Abstract::Group::Ok) {
// Conditionally compute Jacobian at current solution.
if ( (ageOfJacobian == -1) || (ageOfJacobian == maxAgeOfJacobian) ) {
if (ageOfJacobian > 0)
oldJacobianGrp = soln;
status = oldJacobianGrp.computeJacobian();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute Jacobian");
ageOfJacobian = 1;
}
else
ageOfJacobian++;
// Compute the Modified Newton direction
status = oldJacobianGrp.applyJacobianInverse(paramsPtr->sublist("Modified-Newton").sublist("Linear Solver"), soln.getF(), dir);
dir.scale(-1.0);
// It didn't converge, but maybe we can recover.
if ((status != NOX::Abstract::Group::Ok) &&
(doRescue == false)) {
throwError("compute", "Unable to solve Newton system");
}
else if ((status != NOX::Abstract::Group::Ok) &&
(doRescue == true)) {
if (utils->isPrintType(NOX::Utils::Warning))
utils->out() << "WARNING: NOX::Direction::ModifiedNewton::compute() - "
<< "Linear solve failed to achieve convergence - "
<< "using the step anyway since \"Rescue Bad Newton Solve\" "
<< "is true. Also, flagging recompute of Jacobian." << std::endl;
ageOfJacobian = maxAgeOfJacobian;
status = NOX::Abstract::Group::Ok;
}
}
return true;
}
double NOX::Solver::TensorBased::getDirectionalDerivative(
const NOX::Abstract::Vector& dir,
const NOX::Abstract::Group& soln) const
{
Teuchos::RCP<NOX::Abstract::Vector> tmpPtr =
soln.getF().clone(ShapeCopy);
soln.applyJacobian(dir, *tmpPtr);
numJvMults++;
double fprime = tmpPtr->innerProduct(soln.getF());
return fprime;
}
bool NOX::Direction::Newton::compute(NOX::Abstract::Vector& dir,
NOX::Abstract::Group& soln,
const NOX::Solver::Generic& solver)
{
NOX::Abstract::Group::ReturnType status;
// Compute F at current solution.
status = soln.computeF();
if (status != NOX::Abstract::Group::Ok)
NOX::Direction::Newton::throwError("compute", "Unable to compute F");
// Reset the linear solver tolerance.
if (useAdjustableForcingTerm) {
resetForcingTerm(soln, solver.getPreviousSolutionGroup(),
solver.getNumIterations(), solver);
}
else {
if (utils->isPrintType(Utils::Details)) {
utils->out() << " CALCULATING FORCING TERM" << endl;
utils->out() << " Method: Constant" << endl;
utils->out() << " Forcing Term: " << eta_k << endl;
}
}
// Compute Jacobian at current solution.
status = soln.computeJacobian();
if (status != NOX::Abstract::Group::Ok)
NOX::Direction::Newton::throwError("compute", "Unable to compute Jacobian");
// Compute the Newton direction
status = soln.computeNewton(paramsPtr->sublist("Newton").sublist("Linear Solver"));
// It didn't converge, but maybe we can recover.
if ((status != NOX::Abstract::Group::Ok) &&
(doRescue == false)) {
NOX::Direction::Newton::throwError("compute",
"Unable to solve Newton system");
}
else if ((status != NOX::Abstract::Group::Ok) &&
(doRescue == true)) {
if (utils->isPrintType(NOX::Utils::Warning))
utils->out() << "WARNING: NOX::Direction::Newton::compute() - Linear solve "
<< "failed to achieve convergence - using the step anyway "
<< "since \"Rescue Bad Newton Solve\" is true " << endl;
}
// Set search direction.
dir = soln.getNewton();
return true;
}
double NOX::MeritFunction::SumOfSquares::
computef(const NOX::Abstract::Group& grp) const
{
if ( !(grp.isF()) ) {
utils->err()
<< "ERROR: NOX::MeritFunction::SumOfSquares::computef() - "
<< "F has not been computed yet!. Please call "
<< "computeF() on the group passed into this function."
<< std::endl;
throw "NOX Error";
}
return (0.5 * grp.getNormF() * grp.getNormF());
}
bool NOX::LineSearch::Polynomial::
updateGrp(NOX::Abstract::Group& newGrp,
const NOX::Abstract::Group& oldGrp,
const NOX::Abstract::Vector& dir,
double step) const
{
newGrp.computeX(oldGrp, dir, step);
NOX::Abstract::Group::ReturnType status = newGrp.computeF();
if (status != NOX::Abstract::Group::Ok)
return false;
return true;
}
bool NOX::Direction::ModifiedNewton::rescueBadNewtonSolve(const NOX::Abstract::Group& grp) const
{
//! Check if the "rescue" option has been selected
if (!doRescue)
return false;
//! See if the group has compute the accuracy
double accuracy;
NOX::Abstract::Group::ReturnType status = oldJacobianGrpPtr->getNormLastLinearSolveResidual(accuracy);
// If this functionality is not supported in the group, return false
/* NOTE FROM TAMMY: We could later modify this to acutally caluclate
the error itself if it's just a matter of the status being
NotDefined. */
if (status != NOX::Abstract::Group::Ok)
return false;
// Check if there is any improvement in the relative residual
double normF = grp.getNormF();
// If we can't reduce the relative norm at all, we're not happy
if (accuracy >= normF)
return false;
// Otherwise, we just print a warning and keep going
if (utils->isPrintType(NOX::Utils::Warning))
utils->out() << "WARNING: NOX::Direction::ModifiedNewton::compute - Unable to achieve desired linear solve accuracy." << std::endl;
return true;
}
void
NOX::Solver::TensorBased::printDirectionInfo(std::string dirName,
const NOX::Abstract::Vector& dir,
const NOX::Abstract::Group& soln,
bool isTensorModel) const
{
double dirNorm = dir.norm();
double residual = getNormModelResidual(dir, soln, isTensorModel);
double residualRel = residual / soln.getNormF();
double fprime = getDirectionalDerivative(dir, soln);
double fprimeRel = fprime / dirNorm;
if (utilsPtr->isPrintType(NOX::Utils::Details))
{
utilsPtr->out() << " " << dirName << " norm of model residual = "
<< utilsPtr->sciformat(residual, 6) << " (abs) "
<< utilsPtr->sciformat(residualRel, 6) << " (rel)" << std::endl;
utilsPtr->out() << " " << dirName << " directional derivative = "
<< utilsPtr->sciformat(fprime, 6) << " (abs) "
<< utilsPtr->sciformat(fprimeRel, 6) << " (rel)" << std::endl;
utilsPtr->out() << " " << dirName << " norm = "
<< utilsPtr->sciformat(dirNorm, 6) << std::endl;
}
}
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::
computeQuadraticModel(const NOX::Abstract::Vector& dir,
const NOX::Abstract::Group& grp) const
{
if (Teuchos::is_null(tmpVecPtr))
tmpVecPtr = grp.getF().clone();
double m = 0.0;
m = this->computef(grp);
m += this->computeSlope(dir, grp);
grp.applyJacobian(dir, *(tmpVecPtr.get()));
m += 0.5 * tmpVecPtr->innerProduct(*(tmpVecPtr.get()));
return m;
}
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()));
}
double NOX::LineSearch::Polynomial::
computeValue(const NOX::Abstract::Group& grp, double phi)
{
double value = phi;
if (suffDecrCond == AredPred)
{
value = grp.getNormF();
}
return value;
}
bool NOX::Direction::QuasiNewton::compute(NOX::Abstract::Vector& dir,
NOX::Abstract::Group& soln,
const Solver::Generic& solver)
{
NOX::Abstract::Group::ReturnType status;
// Compute F at current solution
status = soln.computeF();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute F");
// Compute Jacobian at current solution.
status = soln.computeJacobian();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute Jacobian");
// Compute the gradient at the current solution
status = soln.computeGradient();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute gradient");
// Push the old information onto the memory, but only after at least one previous iteration
if (solver.getNumIterations() > 0)
{
const NOX::Abstract::Group& oldSoln = solver.getPreviousSolutionGroup();
if (oldSoln.isGradient())
memory.add(soln.getX(), oldSoln.getX(), soln.getGradient(), oldSoln.getGradient());
}
// *** Calculate the QN direction ***
// d = -g
dir = soln.getGradient();
dir.scale(-1.0);
if (!memory.empty())
{
int m = memory.size();
vector<double> alpha(m);
double beta;
for (int i = m-1; i >= 0; i --)
{
alpha[i] = memory[i].rho() * dir.innerProduct( memory[i].s() );
dir.update(-1.0 * alpha[i], memory[i].y(), 1.0);
}
dir.scale( memory[m-1].sdoty() / memory[m-1].ydoty() );
for (int i = 0; i < m; i ++)
{
beta = memory[i].rho() * dir.innerProduct( memory[i].y() );
dir.update(alpha[i] - beta, memory[i].s(), 1.0);
}
}
return true;
}
void NOX::StatusTest::NormF::relativeSetup(NOX::Abstract::Group& initialGuess)
{
NOX::Abstract::Group::ReturnType rtype;
rtype = initialGuess.computeF();
if (rtype != NOX::Abstract::Group::Ok)
{
utils.err() << "NOX::StatusTest::NormF::NormF - Unable to compute F"
<< endl;
throw "NOX Error";
}
initialTolerance = computeNorm(initialGuess);
trueTolerance = specifiedTolerance * initialTolerance;
}
bool
NOX::Solver::TensorBased::implementGlobalStrategy(NOX::Abstract::Group& newGrp,
double& in_stepSize,
const NOX::Solver::Generic& s)
{
bool ok;
counter.incrementNumLineSearches();
isNewtonDirection = false;
NOX::Abstract::Vector& searchDirection = *tensorVecPtr;
if ((counter.getNumLineSearches() == 1) || (lsType == Newton))
{
isNewtonDirection = true;
searchDirection = *newtonVecPtr;
}
// Do line search and compute new soln.
if ((lsType != Dual) || (isNewtonDirection))
ok = performLinesearch(newGrp, in_stepSize, searchDirection, s);
else if (lsType == Dual)
{
double fTensor = 0.0;
double fNew = 0.0;
double tensorStep = 1.0;
bool isTensorDescent = false;
const Abstract::Group& oldGrp = s.getPreviousSolutionGroup();
double fprime = slopeObj.computeSlope(searchDirection, oldGrp);
// Backtrack along tensor direction if it is descent direction.
if (fprime < 0)
{
ok = performLinesearch(newGrp, in_stepSize, searchDirection, s);
assert(ok);
fTensor = 0.5 * newGrp.getNormF() * newGrp.getNormF();
tensorStep = in_stepSize;
isTensorDescent = true;
}
// Backtrack along the Newton direction.
ok = performLinesearch(newGrp, in_stepSize, *newtonVecPtr, s);
fNew = 0.5 * newGrp.getNormF() * newGrp.getNormF();
// If backtracking on the tensor step produced a better step, then use it.
if (isTensorDescent && (fTensor <= fNew))
{
newGrp.computeX(oldGrp, *tensorVecPtr, tensorStep);
newGrp.computeF();
}
}
return ok;
}
bool NOX::Direction::Broyden::doRestart(NOX::Abstract::Group& soln,
const NOX::Solver::LineSearchBased& solver)
{
// Test 1 - First iteration!
if (solver.getNumIterations() == 0)
return true;
// Test 2 - Frequency
if (cnt >= cntMax)
return true;
// Test 3 - Last step was zero!
if (solver.getStepSize() == 0.0)
return true;
// Test 4 - Check for convergence rate
convRate = soln.getNormF() / solver.getPreviousSolutionGroup().getNormF();
if (convRate > maxConvRate)
return true;
return false;
}
double NOX::MeritFunction::SumOfSquares::
computeSlopeWithoutJacobian(const NOX::Abstract::Vector& dir,
const NOX::Abstract::Group& grp) const
{
if (Teuchos::is_null(tmpVecPtr))
tmpVecPtr = grp.getF().clone(NOX::ShapeCopy);
if (Teuchos::is_null(tmpGrpPtr))
tmpGrpPtr = grp.clone(NOX::ShapeCopy);
// Compute the perturbation parameter
double lambda = 1.0e-6;
double denominator = dir.norm();
// Don't divide by zero
if (denominator == 0.0)
denominator = 1.0;
double eta = lambda * (lambda + grp.getX().norm() / denominator);
// Don't divide by zero
if (eta == 0.0)
eta = 1.0e-6;
// Perturb the solution vector
tmpVecPtr->update(eta, dir, 1.0, grp.getX(), 0.0);
// Compute the new F --> F(x + eta * dir)
tmpGrpPtr->setX(*(tmpVecPtr.get()));
tmpGrpPtr->computeF();
// Compute Js = (F(x + eta * dir) - F(x))/eta
tmpVecPtr->update(-1.0/eta, grp.getF(), 1.0/eta, tmpGrpPtr->getF(), 0.0);
return(tmpVecPtr->innerProduct(grp.getF()));
}
bool NOX::LineSearch::Backtrack::
compute(NOX::Abstract::Group& grp, double& step,
const NOX::Abstract::Vector& dir,
const NOX::Solver::Generic& s)
{
const Abstract::Group& oldGrp = s.getPreviousSolutionGroup();
double oldF = meritFunctionPtr->computef(oldGrp);
double newF;
bool isFailed = false;
step = defaultStep;
grp.computeX(oldGrp, dir, step);
NOX::Abstract::Group::ReturnType rtype;
rtype = grp.computeF();
if (rtype != NOX::Abstract::Group::Ok)
{
utils->err() << "NOX::LineSearch::BackTrack::compute - Unable to compute F"
<< std::endl;
throw "NOX Error";
}
newF = meritFunctionPtr->computef(grp);
int nIters = 1;
if (utils->isPrintType(Utils::InnerIteration))
{
utils->out() << "\n" << Utils::fill(72) << "\n"
<< "-- Backtrack Line Search -- \n";
}
NOX::StatusTest::FiniteValue checkNAN;
while ( ((newF >= oldF) || (checkNAN.finiteNumberTest(newF) !=0))
&& (!isFailed))
{
if (utils->isPrintType(Utils::InnerIteration))
{
utils->out() << std::setw(3) << nIters << ":";
utils->out() << " step = " << utils->sciformat(step);
utils->out() << " old f = " << utils->sciformat(oldF);
utils->out() << " new f = " << utils->sciformat(newF);
utils->out() << std::endl;
}
nIters ++;
step = step * reductionFactor;
if ((step < minStep) || (nIters > maxIters))
{
isFailed = true;
step = recoveryStep;
}
grp.computeX(oldGrp, dir, step);
rtype = grp.computeF();
if (rtype != NOX::Abstract::Group::Ok)
{
utils->err() << "NOX::LineSearch::BackTrack::compute - Unable to compute F" << std::endl;
throw "NOX Error";
}
newF = meritFunctionPtr->computef(grp);
}
if (utils->isPrintType(Utils::InnerIteration))
{
utils->out() << std::setw(3) << nIters << ":";
utils->out() << " step = " << utils->sciformat(step);
utils->out() << " old f = " << utils->sciformat(oldF);
utils->out() << " new f = " << utils->sciformat(newF);
if (isFailed)
utils->out() << " (USING RECOVERY STEP!)" << std::endl;
else
utils->out() << " (STEP ACCEPTED!)" << std::endl;
utils->out() << Utils::fill(72) << "\n" << std::endl;
}
return (!isFailed);
}
// **************************************************************************
// *** 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;
}
void MatrixFree::setGroupForComputeF(const NOX::Abstract::Group& group)
{
useGroupForComputeF = true;
groupPtr = group.clone();
return;
}
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.
if (dir_status != NOX::Abstract::Group::Ok)
{
if (doRescue == false)
throwError("computeTensorDirection", "Unable to apply Jacobian inverse");
else if ((doRescue == true) &&
(utilsPtr->isPrintType(NOX::Utils::Warning)))
utilsPtr->out() << "WARNING: NOX::Solver::TensorBased::computeTensorDirection() - "
<< "Linear solve failed to achieve convergence - "
<< "using the step anyway "
<< "since \"Rescue Bad Newton Solve\" is true." << std::endl;
}
// Continue processing
#ifdef USE_INITIAL_GUESS_LOGIC
if (isInitialGuessGood)
{
tmpVecPtr->update(1.0, *tensorVecPtr, 1.0);
linearParams.set("Tolerance", tol);
}
#endif
// Save iteration count for comparison later
if (linearParams.sublist("Output").
isParameter("Number of Linear Iterations"))
tempVal1 = linearParams.sublist("Output").
get("Number of Linear Iterations",0);
#if DEBUG_LEVEL > 0
// Compute residual of linear system with initial guess...
soln.applyJacobian(*tmpVecPtr, *residualVecPtr);
numJvMults++;
residualVec.update(-1.0, solver.getPreviousSolutionGroup().getF(),1.0);
double residualNorm2 = residualVec.norm();
double residualNorm2Rel = residualNorm2 /
solver.getPreviousSolutionGroup().getNormF();
if (utilsPtr->isPrintType(NOX::Utils::Details))
utilsPtr->out() << " jifp norm of model residual = "
<< utilsPtr->sciformat(residualNorm2, 6) << " (abs) "
<< utilsPtr->sciformat(residualNorm2Rel, 6) << " (rel)" << std::endl;
#endif
}
// Compute the Newton direction
dir_status = soln.computeNewton(linearParams);
// If it didn't converge, maybe we can recover.
if (dir_status != NOX::Abstract::Group::Ok)
{
if (doRescue == false)
throwError("computeTensorDirection", "Unable to apply Jacobian inverse");
else if ((doRescue == true) &&
(utilsPtr->isPrintType(NOX::Utils::Warning)))
utilsPtr->out() << "WARNING: NOX::Solver::TensorBased::computeTensorDirection() - "
<< "Linear solve failed to achieve convergence - "
<< "using the step anyway "
<< "since \"Rescue Bad Newton Solve\" is true." << std::endl;
}
// Set Newton direction
*newtonVecPtr = soln.getNewton();
// Update counter
int tempVal2 = 0;
if (linearParams.sublist("Output").
isParameter("Number of Linear Iterations"))
tempVal2 = linearParams.sublist("Output").
get("Number of Linear Iterations",0);
numJ2vMults += (tempVal1 > tempVal2) ? tempVal1 : tempVal2;
#ifdef CHECK_RESIDUALS
printDirectionInfo("newtonVec", *newtonVecPtr, soln, false);
#endif // CHECK_RESIDUALS
// Continue processing the tensor step, if necessary
if ((nIter > 0) && (requestedBaseStep == TensorStep))
{
// Form the term inv(J)*a... (note that a is not multiplied by 2)
// The next line does not work in some implementations for some reason
//tmpVec.update(1.0, newtonVec, -1.0, sVec, 1.0);
tmpVecPtr->update(1.0, *newtonVecPtr, 1.0);
tmpVecPtr->update(-1.0, *sVecPtr, 1.0);
if (sDotS != 0.0)
tmpVecPtr->scale( 1.0 / (sDotS * sDotS));
// Calculate value of beta
sTinvJF = -sVecPtr->innerProduct(*newtonVecPtr);
sTinvJa = sVecPtr->innerProduct(*tmpVecPtr);
double qval = 0;
double lambdaBar = 1;
beta = calculateBeta(sTinvJa, 1.0, sTinvJF, qval, lambdaBar);
double sVecNorm = sVecPtr->norm();
double aVecNorm = aVecPtr->norm();
if (utilsPtr->isPrintType(NOX::Utils::Details))
{
utilsPtr->out() << " sTinvJF = " << utilsPtr->sciformat(sTinvJF, 6)
<< " sTinvJa = " << utilsPtr->sciformat(sTinvJa, 6) << std::endl;
utilsPtr->out() << " norm(s) = " << utilsPtr->sciformat(sVecNorm, 6)
<< " norm(a) = " << utilsPtr->sciformat(aVecNorm, 6) << std::endl;
}
if (useModifiedMethod)
{
double alpha2 = lambdaBar;
if (utilsPtr->isPrintType(NOX::Utils::Details))
utilsPtr->out() << " Beta = " << utilsPtr->sciformat(beta, 6)
<< " Alpha2 = " << utilsPtr->sciformat(alpha2, 6) << std::endl;
if (alpha2 != 1.0)
{
if (utilsPtr->isPrintType(NOX::Utils::Details))
utilsPtr->out() << " *** Scaling tensor term a ***" << std::endl;
aVecPtr->scale(alpha2);
tmpVecPtr->scale(alpha2);
sTinvJa *= alpha2;
beta /= alpha2;
lambdaBar = 1.0;
qval = 0;
}
}
// Form the tensor step
tensorVecPtr->update(1.0, *newtonVecPtr, -beta*beta, *tmpVecPtr, 0.0);
#ifdef CHECK_RESIDUALS
printDirectionInfo("tensorVec", *tensorVecPtr, soln, true);
#endif // CHECK_RESIDUALS
#if DEBUG_LEVEL > 0
double sDotT = tensorVecPtr->innerProduct(sVec);
if (utilsPtr->isPrintType(NOX::Utils::Details))
utilsPtr->out() << " Beta = " << utilsPtr->sciformat(beta, 6)
<< " std = " << utilsPtr->sciformat(sDotT, 6)
<< " qval = " << utilsPtr->sciformat(qval, 2)
<< " lambdaBar = " << lambdaBar << std::endl;
#endif
}
else
*tensorVecPtr = *newtonVecPtr;
return true;
}
bool
NOX::Solver::TensorBased::performLinesearch(NOX::Abstract::Group& newSoln,
double& in_stepSize,
const NOX::Abstract::Vector& lsDir,
const NOX::Solver::Generic& s)
{
if (print.isPrintType(NOX::Utils::InnerIteration))
{
utilsPtr->out() << "\n" << NOX::Utils::fill(72) << "\n";
utilsPtr->out() << "-- Tensor Line Search (";
if (lsType == Curvilinear)
utilsPtr->out() << "Curvilinear";
else if (lsType == Standard)
utilsPtr->out() << "Standard";
else if (lsType == FullStep)
utilsPtr->out() << "Full Step";
else if (lsType == Dual)
utilsPtr->out() << "Dual";
utilsPtr->out() << ") -- " << std::endl;
}
// Local variables
bool isFailed = false;
bool isAcceptable = false;
bool isFirstPass = true;
std::string message = "(STEP ACCEPTED!)";
// Set counters
int lsIterations = 1;
// Get Old f
const Abstract::Group& oldSoln = s.getPreviousSolutionGroup();
double fOld = 0.5 * oldSoln.getNormF() * oldSoln.getNormF();
// Compute first trial point and its function value
in_stepSize = defaultStep;
newSoln.computeX(oldSoln, lsDir, in_stepSize);
newSoln.computeF();
double fNew = 0.5 * newSoln.getNormF() * newSoln.getNormF();
// Stop here if only using the full step
if (lsType == FullStep)
{
print.printStep(lsIterations, in_stepSize, fOld, fNew, message);
return (!isFailed);
}
// Compute directional derivative
double fprime;
if ((lsType == Curvilinear) && !(isNewtonDirection))
fprime = slopeObj.computeSlope(*newtonVecPtr, oldSoln);
else
fprime = slopeObj.computeSlope(lsDir, oldSoln);
numJvMults++; // computeSlope() has J*v inside of it
// Compute the convergence criteria for the line search
double threshold = fOld + alpha*in_stepSize*fprime;
isAcceptable = (fNew < threshold);
// Update counter and temporarily hold direction if a linesearch is needed
if (!isAcceptable)
{
counter.incrementNumNonTrivialLineSearches();
*tmpVecPtr = lsDir;
}
// Iterate until the trial point is accepted....
while (!isAcceptable)
{
// Check for linesearch failure
if (lsIterations > maxIters)
{
isFailed = true;
message = "(FAILED - Max Iters)";
break;
}
print.printStep(lsIterations, in_stepSize, fOld, fNew);
// Is the full tensor step a descent direction? If not, switch to Newton
if (isFirstPass &&
(!isNewtonDirection) &&
(fprime >= 0) &&
(lsType != Curvilinear) )
{
*tmpVecPtr = *newtonVecPtr;
fprime = slopeObj.computeSlope(*tmpVecPtr, oldSoln);
numJvMults++;
if (utilsPtr->isPrintType(NOX::Utils::Details))
utilsPtr->out() << " Switching to Newton step. New fprime = "
<< utilsPtr->sciformat(fprime, 6) << std::endl;
}
else
{
in_stepSize = selectLambda(fNew, fOld, fprime, in_stepSize);
}
isFirstPass = false;
// Check for linesearch failure
if (in_stepSize < minStep)
{
isFailed = true;
message = "(FAILED - Min Step)";
break;
}
// Update the number of linesearch iterations
counter.incrementNumIterations();
lsIterations ++;
// Compute new trial point and its function value
if ((lsType == Curvilinear) && !(isNewtonDirection))
{
computeCurvilinearStep(*tmpVecPtr, oldSoln, s, in_stepSize);
// Note: oldSoln is needed above to get correct preconditioner
newSoln.computeX(oldSoln, *tmpVecPtr, 1.0);
}
else
{
newSoln.computeX(oldSoln, *tmpVecPtr, in_stepSize);
}
newSoln.computeF();
fNew = 0.5 * newSoln.getNormF() * newSoln.getNormF();
// Recompute convergence criteria based on new step
threshold = fOld + alpha*in_stepSize*fprime;
isAcceptable = (fNew < threshold);
}
if (isFailed)
{
counter.incrementNumFailedLineSearches();
if (recoveryStepType == Constant)
{
in_stepSize = recoveryStep;
if (in_stepSize == 0.0)
{
newSoln = oldSoln;
newSoln.computeF();
fNew = fOld;
}
else
{
// Update the group using recovery step
if ((lsType == Curvilinear) && !(isNewtonDirection))
{
computeCurvilinearStep(*tmpVecPtr, oldSoln, s, in_stepSize);
// Note: oldSoln is needed above to get correct preconditioner
newSoln.computeX(oldSoln, *tmpVecPtr, 1.0);
}
else
{
newSoln.computeX(oldSoln, *tmpVecPtr, in_stepSize);
}
//newSoln.computeX(oldSoln, lsDir, in_stepSize);
newSoln.computeF();
fNew = 0.5 * newSoln.getNormF() * newSoln.getNormF();
message = "(USING RECOVERY STEP!)";
}
}
else
message = "(USING LAST STEP!)";
}
print.printStep(lsIterations, in_stepSize, fOld, fNew, message);
counter.setValues(paramsPtr->sublist("Line Search"));
return (!isFailed);
}
NOX::Abstract::Group::ReturnType
LOCA::Eigensolver::DGGEVStrategy::computeEigenvalues(
NOX::Abstract::Group& group,
Teuchos::RCP< std::vector<double> >& evals_r,
Teuchos::RCP< std::vector<double> >& evals_i,
Teuchos::RCP< NOX::Abstract::MultiVector >& evecs_r,
Teuchos::RCP< NOX::Abstract::MultiVector >& evecs_i)
{
// Get LAPACK group
LOCA::LAPACK::Group* grp =
dynamic_cast<LOCA::LAPACK::Group*>(&group);
bool hasMassMatrix = true;
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperIteration)) {
globalData->locaUtils->out()
<< std::endl << globalData->locaUtils->fill(64,'=') << std::endl
<< "LAPACK ";
if (hasMassMatrix)
globalData->locaUtils->out() << "DGGEV ";
else
globalData->locaUtils->out() << "DGEEV ";
globalData->locaUtils->out() << "Eigensolver starting."
<< std::endl << std::endl;;
}
// Make sure Jacobian & mass matrices are fresh
grp->computeJacobian();
if (hasMassMatrix)
grp->computeShiftedMatrix(0.0, 1.0);
// Get data out of group
NOX::LAPACK::Matrix<double>& jacobianMatrix = grp->getJacobianMatrix();
NOX::LAPACK::Matrix<double>& massMatrix = grp->getShiftedMatrix();
// Size of matrix
int n = jacobianMatrix.numRows();
int lda = jacobianMatrix.numRows();
int ldb = massMatrix.numRows();
// Space to hold right eigenvectors
double *vr = new double[n*n];
// Space to hold real and imaginary eigenvalues
double *alphar = new double[n];
double *alphai = new double[n];
double *beta = new double[n];
// Size of work array, set to -1 to do a workspace query
int lwork = -1;
// Initial work "array"
double work0;
// Actual work array
double *work;
// Return code
int info;
// Copy Jacobian matrix since lapack routines overwrite it
NOX::LAPACK::Matrix<double> J(jacobianMatrix);
NOX::LAPACK::Matrix<double> M;
// First do a workspace query
if (hasMassMatrix) {
// Copy mass matrix since lapack routines overwrite it
M = massMatrix;
DGGEV_F77("N", "V", &n, &J(0,0), &lda, &M(0,0), &ldb, alphar, alphai, beta,
vr, &n, vr, &n, &work0, &lwork, &info);
}
else {
DGEEV_F77("N", "V", &n, &J(0,0), &lda, alphar, alphai,
vr, &n, vr, &n, &work0, &lwork, &info);
}
// Allocate work array
lwork = (int) work0;
work = new double[lwork];
// Calculate eigenvalues, eigenvectors
if (hasMassMatrix) {
DGGEV_F77("N", "V", &n, &J(0,0), &lda, &M(0,0), &ldb, alphar, alphai, beta,
vr, &n, vr, &n, work, &lwork, &info);
}
else {
DGEEV_F77("N", "V", &n, &J(0,0), &lda, alphar, alphai,
vr, &n, vr, &n, work, &lwork, &info);
}
// Check for success
if (info != 0)
return NOX::Abstract::Group::Failed;
// Compute all of the eigenvalues and eigenvectors before sorting
std::vector<double> evals_r_tmp(n);
std::vector<double> evals_i_tmp(n);
Teuchos::RCP<NOX::Abstract::MultiVector> evecs_r_tmp =
group.getX().createMultiVector(n, NOX::ShapeCopy);
Teuchos::RCP<NOX::Abstract::MultiVector>evecs_i_tmp =
group.getX().createMultiVector(n, NOX::ShapeCopy);
NOX::LAPACK::Vector* tmpr;
NOX::LAPACK::Vector* tmpi;
double rnext;
double inext;
bool isComplexEval = false;
bool isPrevComplexEval = false;
for (int j=0; j<n; j++) {
// Compute eigenvalues
if (hasMassMatrix) {
evals_r_tmp[j] = alphar[j]/beta[j];
evals_i_tmp[j] = alphai[j]/beta[j];
}
else {
evals_r_tmp[j] = alphar[j];
evals_i_tmp[j] = alphai[j];
}
// Compute next eigenvalue
if (!isPrevComplexEval && j < n-1) {
if (hasMassMatrix) {
rnext = alphar[j+1]/beta[j+1];
inext = alphai[j+1]/beta[j+1];
}
else {
rnext = alphar[j+1];
inext = alphai[j+1];
}
// Determine if this eigenvalue is a complex conjugate pair
if (fabs(evals_r_tmp[j] - rnext) < 1.0e-14*fabs(1.0+evals_r_tmp[j]) &&
fabs(evals_i_tmp[j] + inext) < 1.0e-14*fabs(1.0+evals_i_tmp[j]))
isComplexEval = true;
else
isComplexEval = false;
}
else if (!isPrevComplexEval && j == n-1)
isComplexEval = false;
tmpr = dynamic_cast<NOX::LAPACK::Vector*>(&((*evecs_r_tmp)[j]));
tmpi = dynamic_cast<NOX::LAPACK::Vector*>(&((*evecs_i_tmp)[j]));
if (isComplexEval)
for (int i=0; i<n; i++) {
(*tmpr)(i) = vr[i+j*n];
(*tmpi)(i) = vr[i+(j+1)*n];
}
else if (isPrevComplexEval)
for (int i=0; i<n; i++) {
(*tmpr)(i) = vr[i+(j-1)*n];
(*tmpi)(i) = -vr[i+j*n];
}
else
for (int i=0; i<n; i++) {
(*tmpr)(i) = vr[i+j*n];
(*tmpi)(i) = 0.0;;
}
if (isPrevComplexEval) {
isPrevComplexEval = false;
isComplexEval = false;
}
if (isComplexEval) {
isPrevComplexEval = true;
isComplexEval = false;
}
}
// Instantiate a sorting strategy
Teuchos::RCP<LOCA::EigenvalueSort::AbstractStrategy> evalSort =
globalData->locaFactory->createEigenvalueSortStrategy(topParams,
eigenParams);
// Create permutation array
std::vector<int> perm(n);
// Sort eigenvalues
evalSort->sort(n, &evals_r_tmp[0], &evals_i_tmp[0], &perm);
// Get first nev entries of perm
std::vector<int> perm_short(perm.begin(), perm.begin()+nev);
// Get sorted eigenvalues and eigenvectors
evals_r = Teuchos::rcp(new std::vector<double>(evals_r_tmp.begin(),
evals_r_tmp.begin()+nev));
evals_i = Teuchos::rcp(new std::vector<double>(evals_i_tmp.begin(),
evals_i_tmp.begin()+nev));
evecs_r = evecs_r_tmp->subCopy(perm_short);
evecs_i = evecs_i_tmp->subCopy(perm_short);
// Print out eigenvalues
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperIteration)) {
for (int i=0; i<nev; i++)
globalData->locaUtils->out()
<< "Eigenvalue " << i << " : "
<< globalData->locaUtils->sciformat((*evals_r)[i]) << " "
<< globalData->locaUtils->sciformat((*evals_i)[i]) << " i"
<< std::endl;
}
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperIteration))
globalData->locaUtils->out()
<< "\nLAPACK Eigensolver finished.\n"
<< globalData->locaUtils->fill(64,'=') << "\n" << std::endl;
delete [] alphar;
delete [] alphai;
delete [] beta;
delete [] vr;
delete [] work;
return NOX::Abstract::Group::Ok;
}
bool NOX::Direction::Broyden::compute(NOX::Abstract::Vector& dir,
NOX::Abstract::Group& soln,
const NOX::Solver::LineSearchBased& solver)
{
// Return value for group operations (temp variable)
NOX::Abstract::Group::ReturnType status;
// Compute F at current solution
status = soln.computeF();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute F");
// Check for restart
if (doRestart(soln, solver))
{
// Reset memory
memory.reset();
// Update group
if (Teuchos::is_null(oldJacobianGrpPtr))
oldJacobianGrpPtr = soln.clone(NOX::DeepCopy);
else
// RPP - update the entire group (this grabs state vectors in xyce).
// Otherwise, xyce is forced to recalculate F at each iteration.
//oldJacobianGrpPtr->setX(soln.getX());
*oldJacobianGrpPtr = soln;
// Calcuate new Jacobian
if (utils->isPrintType(NOX::Utils::Details))
utils->out() << " Recomputing Jacobian" << endl;
status = oldJacobianGrpPtr->computeJacobian();
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to compute Jacobian");
// Reset counter
cnt = 0;
}
// If necesary, scale the s-vector from the last iteration
if (!memory.empty())
{
double step = solver.getStepSize();
memory[memory.size() - 1].setStep(step);
}
// --- Calculate the Broyden direction ---
// Compute inexact forcing term if requested.
inexactNewtonUtils.computeForcingTerm(soln,
solver.getPreviousSolutionGroup(),
solver.getNumIterations(),
solver);
// dir = - J_old^{-1} * F
cnt ++;
status = oldJacobianGrpPtr->applyJacobianInverse(*lsParamsPtr,
soln.getF(),
dir);
if (status != NOX::Abstract::Group::Ok)
throwError("compute", "Unable to apply Jacobian inverse");
dir.scale(-1.0);
// Apply the Broyden modifications to the old Jacobian (implicitly)
if (!memory.empty())
{
// Number of elements in the memory
int m = memory.size();
// Information corresponding to index i
double step;
Teuchos::RCP<const NOX::Abstract::Vector> sPtr;
// Information corresponding to index i + 1
// (initialized for i = -1)
double stepNext = memory[0].step();
Teuchos::RCP<const NOX::Abstract::Vector> sPtrNext =
memory[0].sPtr();
// Intermediate storage
double a, b, c, denom;
for (int i = 0; i < m-1; i ++)
{
step = stepNext;
sPtr = sPtrNext;
stepNext = memory[i+1].step();
sPtrNext = memory[i+1].sPtr();
a = step / stepNext;
b = step - 1;
c = sPtr->innerProduct(dir) / memory[i].sNormSqr();
dir.update(a * c, *sPtrNext, b * c, *sPtr, 1.0);
}
step = stepNext;
sPtr = sPtrNext;
a = sPtr->innerProduct(dir); // <s,z>
b = memory[m-1].sNormSqr(); // ||s||^2
c = (step - 1) * a; // (\lambda-1) <s,z>
denom = b - step * a; // ||s||^2 - \lambda <s,z>
dir.update(c / denom, *sPtr, b / denom);
}
//! Add this direction to the memory
memory.push(dir);
return true;
}
