void run( Vector<Real> &s, Real &snorm, Real &del, int &iflag, int &iter, const Vector<Real> &x,
const Vector<Real> &grad, const Real &gnorm, ProjectedObjective<Real> &pObj ) {
Real tol = std::sqrt(ROL_EPSILON);
const Real gtol = std::min(tol1_,tol2_*gnorm);
// Old and New Step Vectors
s.zero();
snorm = 0.0;
Real snorm2 = 0.0;
s_->zero();
Real s1norm2 = 0.0;
// Gradient Vector
g_->set(grad);
Real normg = gnorm;
if ( pObj.isConActivated() ) {
pObj.pruneActive(*g_,grad,x);
normg = g_->norm();
}
// Preconditioned Gradient Vector
//pObj.precond(*v,*g,x,tol);
pObj.reducedPrecond(*v_,*g_,x,grad,x,tol);
// Basis Vector
p_->set(*v_);
p_->scale(-1.0);
Real pnorm2 = v_->dot(g_->dual());
iter = 0;
iflag = 0;
Real kappa = 0.0;
Real beta = 0.0;
Real sigma = 0.0;
Real alpha = 0.0;
Real tmp = 0.0;
Real gv = v_->dot(g_->dual());
Real sMp = 0.0;
pRed_ = 0.0;
for (iter = 0; iter < maxit_; iter++) {
//pObj.hessVec(*Hp,*p,x,tol);
pObj.reducedHessVec(*Hp_,*p_,x,grad,x,tol);
kappa = p_->dot(Hp_->dual());
if (kappa <= 0.0) {
sigma = (-sMp+sqrt(sMp*sMp+pnorm2*(del*del-snorm2)))/pnorm2;
s.axpy(sigma,*p_);
iflag = 2;
break;
}
alpha = gv/kappa;
s_->set(s);
s_->axpy(alpha,*p_);
s1norm2 = snorm2 + 2.0*alpha*sMp + alpha*alpha*pnorm2;
if (s1norm2 >= del*del) {
sigma = (-sMp+sqrt(sMp*sMp+pnorm2*(del*del-snorm2)))/pnorm2;
s.axpy(sigma,*p_);
iflag = 3;
break;
}
pRed_ += 0.5*alpha*gv;
s.set(*s_);
snorm2 = s1norm2;
g_->axpy(alpha,*Hp_);
normg = g_->norm();
if (normg < gtol) {
break;
}
//pObj.precond(*v,*g,x,tol);
pObj.reducedPrecond(*v_,*g_,x,grad,x,tol);
tmp = gv;
gv = v_->dot(g_->dual());
beta = gv/tmp;
p_->scale(beta);
p_->axpy(-1.0,*v_);
sMp = beta*(sMp+alpha*pnorm2);
pnorm2 = gv + beta*beta*pnorm2;
}
if (iflag > 0) {
pRed_ += sigma*(gv-0.5*sigma*kappa);
}
if (iter == maxit_) {
iflag = 1;
}
if (iflag != 1) {
iter++;
}
snorm = s.norm();
TrustRegion<Real>::setPredictedReduction(pRed_);
}
