void updateObj( Vector<Real> &x, int iter, ProjectedObjective<Real> &pObj ) {
if ( !softUp_ ) {
pObj.update(x,true,iter);
}
else {
pObj.update(x);
}
}
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<Real>());
// Compute quasi-Newton step
pObj.reducedInvHessVec(*s_,grad,x,grad,x,tol);
s_->scale(-1.0);
Real sNnorm = s_->norm();
Real gsN = s_->dot(grad.dual());
bool negCurv = false;
if ( gsN >= 0.0 ) {
negCurv = true;
}
if ( negCurv ) {
cpt_->run(s,snorm,del,iflag,iter,x,grad,gnorm,pObj);
pRed_ = cpt_->getPredictedReduction();
iflag = 2;
}
else {
// Approximately solve trust region subproblem using double dogleg curve
if (sNnorm <= del) { // Use the quasi-Newton step
s.set(*s_);
snorm = sNnorm;
pRed_ = -0.5*gsN;
iflag = 0;
}
else { // quasi-Newton step is outside of trust region
pObj.reducedHessVec(*Hp_,grad.dual(),x,grad,x,tol);
Real alpha = 0.0;
Real beta = 0.0;
Real gnorm2 = gnorm*gnorm;
Real gBg = grad.dot(*Hp_);
Real gamma = gnorm2/gBg;
if ( gamma*gnorm >= del || gBg <= 0.0 ) {
alpha = 0.0;
beta = del/gnorm;
s.set(grad.dual());
s.scale(-beta);
snorm = del;
iflag = 2;
}
else {
Real a = sNnorm*sNnorm + 2.0*gamma*gsN + gamma*gamma*gnorm2;
Real b = -gamma*gsN - gamma*gamma*gnorm2;
Real c = gamma*gamma*gnorm2 - del*del;
alpha = (-b + sqrt(b*b - a*c))/a;
beta = gamma*(1.0-alpha);
s.set(grad.dual());
s.scale(-beta);
s.axpy(alpha,*s_);
snorm = del;
iflag = 1;
}
pRed_ = (alpha*(0.5*alpha-1)*gsN - 0.5*beta*beta*gBg + beta*(1-alpha)*gnorm2);
}
}
TrustRegion<Real>::setPredictedReduction(pRed_);
}
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_);
}
void truncatedCG_proj( 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);
// Compute Cauchy Point
Real scnorm = 0.0;
Teuchos::RCP<Vector<Real> > sc = x.clone();
cauchypoint(*sc,scnorm,del,iflag,iter,x,grad,gnorm,pObj);
Teuchos::RCP<Vector<Real> > xc = x.clone();
xc->set(x);
xc->plus(*sc);
// Old and New Step Vectors
s.set(*sc);
snorm = s.norm();
Real snorm2 = snorm*snorm;
Teuchos::RCP<Vector<Real> > s1 = x.clone();
s1->zero();
Real s1norm2 = 0.0;
// Gradient Vector
Teuchos::RCP<Vector<Real> > g = x.clone();
g->set(grad);
Teuchos::RCP<Vector<Real> > Hs = x.clone();
pObj.reducedHessVec(*Hs,s,*xc,x,tol);
g->plus(*Hs);
Real normg = g->norm();
// Preconditioned Gradient Vector
Teuchos::RCP<Vector<Real> > v = x.clone();
pObj.reducedPrecond(*v,*g,*xc,x,tol);
// Basis Vector
Teuchos::RCP<Vector<Real> > p = x.clone();
p->set(*v);
p->scale(-1.0);
Real pnorm2 = v->dot(*g);
// Hessian Times Basis Vector
Teuchos::RCP<Vector<Real> > Hp = x.clone();
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);
Real sMp = 0.0;
pRed_ = 0.0;
for (iter = 0; iter < maxit_; iter++) {
pObj.reducedHessVec(*Hp,*p,*xc,x,tol);
kappa = p->dot(*Hp);
if (kappa <= 0) {
sigma = (-sMp+sqrt(sMp*sMp+pnorm2*(del*del-snorm2)))/pnorm2;
s.axpy(sigma,*p);
iflag = 2;
break;
}
alpha = gv/kappa;
s1->set(s);
s1->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(*s1);
snorm2 = s1norm2;
g->axpy(alpha,*Hp);
normg = g->norm();
if (normg < gtol) {
break;
}
pObj.reducedPrecond(*v,*g,*xc,x,tol);
tmp = gv;
gv = v->dot(*g);
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();
}
