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::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;
}
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()));
}
// **************************************************************************
// *** 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;
}
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::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;
}
