/usr/include/trilinos/ROL_PrimalDualActiveSetStep.hpp is in libtrilinos-rol-dev 12.10.1-3.
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#ifndef ROL_PRIMALDUALACTIVESETSTEP_H
#define ROL_PRIMALDUALACTIVESETSTEP_H
#include "ROL_Step.hpp"
#include "ROL_Vector.hpp"
#include "ROL_Krylov.hpp"
#include "ROL_Objective.hpp"
#include "ROL_BoundConstraint.hpp"
#include "ROL_Types.hpp"
#include "ROL_Secant.hpp"
#include "Teuchos_ParameterList.hpp"
/** @ingroup step_group
\class ROL::PrimalDualActiveSetStep
\brief Implements the computation of optimization steps
with the Newton primal-dual active set method.
To describe primal-dual active set (PDAS), we consider the following
abstract setting. Suppose \f$\mathcal{X}\f$ is a Hilbert space of
functions mapping \f$\Xi\f$ to \f$\mathbb{R}\f$. For example,
\f$\Xi\subset\mathbb{R}^n\f$ and \f$\mathcal{X}=L^2(\Xi)\f$ or
\f$\Xi = \{1,\ldots,n\}\f$ and \f$\mathcal{X}=\mathbb{R}^n\f$. We
assume \f$ f:\mathcal{X}\to\mathbb{R}\f$ is twice-continuously Fréchet
differentiable and \f$a,\,b\in\mathcal{X}\f$ with \f$a\le b\f$ almost
everywhere in \f$\Xi\f$. Note that the PDAS algorithm will also work
with secant approximations of the Hessian.
Traditionally, PDAS is an algorithm for the minimizing quadratic objective
functions subject to bound constraints. ROL implements a Newton PDAS which
extends PDAS to general bound-constrained nonlinear programs, i.e.,
\f[
\min_x \quad f(x) \quad \text{s.t.} \quad a \le x \le b.
\f]
Given the \f$k\f$-th iterate \f$x_k\f$, the Newton PDAS algorithm computes
steps by applying PDAS to the quadratic subproblem
\f[
\min_s \quad \langle \nabla^2 f(x_k)s + \nabla f(x_k),s \rangle_{\mathcal{X}}
\quad \text{s.t.} \quad a \le x_k + s \le b.
\f]
For the \f$k\f$-th quadratic subproblem, PDAS builds an approximation of the
active set \f$\mathcal{A}_k\f$ using the dual variable \f$\lambda_k\f$ as
\f[
\mathcal{A}^+_k = \{\,\xi\in\Xi\,:\,(\lambda_k + c(x_k-b))(\xi) > 0\,\}, \quad
\mathcal{A}^-_k = \{\,\xi\in\Xi\,:\,(\lambda_k + c(x_k-a))(\xi) < 0\,\}, \quad\text{and}\quad
\mathcal{A}_k = \mathcal{A}^-_k\cup\mathcal{A}^+_k.
\f]
We define the inactive set \f$\mathcal{I}_k=\Xi\setminus\mathcal{A}_k\f$.
The solution to the quadratic subproblem is then computed iteratively by solving
\f[
\nabla^2 f(x_k) s_k + \lambda_{k+1} = -\nabla f(x_k), \quad
x_k+s_k = a \;\text{on}\;\mathcal{A}^-_k,\quad x_k+s_k = b\;\text{on}\;\mathcal{A}^+_k,
\quad\text{and}\quad
\lambda_{k+1} = 0\;\text{on}\;\mathcal{I}_k
\f]
and updating the active and inactive sets.
One can rewrite this system by consolidating active and inactive parts, i.e.,
\f[
\begin{pmatrix}
\nabla^2 f(x_k)_{\mathcal{A}_k,\mathcal{A}_k} & \nabla^2 f(x_k)_{\mathcal{A}_k,\mathcal{I}_k} \\
\nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{A}_k} & \nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{I}_k}
\end{pmatrix}
\begin{pmatrix}
(s_k)_{\mathcal{A}_k} \\
(s_k)_{\mathcal{I}_k}
\end{pmatrix}
+
\begin{pmatrix}
(\lambda_{k+1})_{\mathcal{A}_k} \\
0
\end{pmatrix}
= -
\begin{pmatrix}
\nabla f(x_k)_{\mathcal{A}_k}\\
\nabla f(x_k)_{\mathcal{I}_k}
\end{pmatrix}.
\f]
Here the subscripts \f$\mathcal{A}_k\f$ and \f$\mathcal{I}_k\f$ denote the active and inactive
components, respectively. Moreover, the active components of \f$s_k\f$ are
\f$s_k(\xi) = a(\xi)-x_k(\xi)\f$ if \f$\xi\in\mathcal{A}^-_k\f$ and \f$s_k(\xi) = b(\xi)-x_k(\xi)\f$
if \f$\xi\in\mathcal{A}^+_k\f$. Since \f$(s_k)_{\mathcal{A}_k}\f$ is fixed, we only need to solve
for the inactive components of \f$s_k\f$ which we can do this using conjugate residuals (CR) (i.e., the
Hessian operator corresponding to the inactive indices may not be positive definite). Once
\f$(s_k)_{\mathcal{I}_k}\f$ is computed, it is straight forward to update the dual variables.
*/
namespace ROL {
template <class Real>
class PrimalDualActiveSetStep : public Step<Real> {
private:
Teuchos::RCP<Krylov<Real> > krylov_;
// Krylov Parameters
int iterCR_; ///< CR iteration counter
int flagCR_; ///< CR termination flag
Real itol_; ///< Inexact CR tolerance
// PDAS Parameters
int maxit_; ///< Maximum number of PDAS iterations
int iter_; ///< PDAS iteration counter
int flag_; ///< PDAS termination flag
Real stol_; ///< PDAS minimum step size stopping tolerance
Real gtol_; ///< PDAS gradient stopping tolerance
Real scale_; ///< Scale for dual variables in the active set, \f$c\f$
Real neps_; ///< \f$\epsilon\f$-active set parameter
bool feasible_; ///< Flag whether the current iterate is feasible or not
// Dual Variable
Teuchos::RCP<Vector<Real> > lambda_; ///< Container for dual variables
Teuchos::RCP<Vector<Real> > xlam_; ///< Container for primal plus dual variables
Teuchos::RCP<Vector<Real> > x0_; ///< Container for initial priaml variables
Teuchos::RCP<Vector<Real> > xbnd_; ///< Container for primal variable bounds
Teuchos::RCP<Vector<Real> > As_; ///< Container for step projected onto active set
Teuchos::RCP<Vector<Real> > xtmp_; ///< Container for temporary primal storage
Teuchos::RCP<Vector<Real> > res_; ///< Container for optimality system residual for quadratic model
Teuchos::RCP<Vector<Real> > Ag_; ///< Container for gradient projected onto active set
Teuchos::RCP<Vector<Real> > rtmp_; ///< Container for temporary right hand side storage
Teuchos::RCP<Vector<Real> > gtmp_; ///< Container for temporary gradient storage
// Secant Information
ESecant esec_; ///< Enum for secant type
Teuchos::RCP<Secant<Real> > secant_; ///< Secant object
bool useSecantPrecond_;
bool useSecantHessVec_;
class HessianPD : public LinearOperator<Real> {
private:
const Teuchos::RCP<Objective<Real> > obj_;
const Teuchos::RCP<BoundConstraint<Real> > bnd_;
const Teuchos::RCP<Vector<Real> > x_;
const Teuchos::RCP<Vector<Real> > xlam_;
Teuchos::RCP<Vector<Real> > v_;
Real eps_;
const Teuchos::RCP<Secant<Real> > secant_;
bool useSecant_;
public:
HessianPD(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<BoundConstraint<Real> > &bnd,
const Teuchos::RCP<Vector<Real> > &x,
const Teuchos::RCP<Vector<Real> > &xlam,
const Real eps = 0,
const Teuchos::RCP<Secant<Real> > &secant = Teuchos::null,
const bool useSecant = false )
: obj_(obj), bnd_(bnd), x_(x), xlam_(xlam),
eps_(eps), secant_(secant), useSecant_(useSecant) {
v_ = x_->clone();
if ( !useSecant || secant == Teuchos::null ) {
useSecant_ = false;
}
}
void apply( Vector<Real> &Hv, const Vector<Real> &v, Real &tol ) const {
v_->set(v);
bnd_->pruneActive(*v_,*xlam_,eps_);
if ( useSecant_ ) {
secant_->applyB(Hv,*v_);
}
else {
obj_->hessVec(Hv,*v_,*x_,tol);
}
bnd_->pruneActive(Hv,*xlam_,eps_);
}
};
class PrecondPD : public LinearOperator<Real> {
private:
const Teuchos::RCP<Objective<Real> > obj_;
const Teuchos::RCP<BoundConstraint<Real> > bnd_;
const Teuchos::RCP<Vector<Real> > x_;
const Teuchos::RCP<Vector<Real> > xlam_;
Teuchos::RCP<Vector<Real> > v_;
Real eps_;
const Teuchos::RCP<Secant<Real> > secant_;
bool useSecant_;
public:
PrecondPD(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<BoundConstraint<Real> > &bnd,
const Teuchos::RCP<Vector<Real> > &x,
const Teuchos::RCP<Vector<Real> > &xlam,
const Real eps = 0,
const Teuchos::RCP<Secant<Real> > &secant = Teuchos::null,
const bool useSecant = false )
: obj_(obj), bnd_(bnd), x_(x), xlam_(xlam),
eps_(eps), secant_(secant), useSecant_(useSecant) {
v_ = x_->clone();
if ( !useSecant || secant == Teuchos::null ) {
useSecant_ = false;
}
}
void apply( Vector<Real> &Hv, const Vector<Real> &v, Real &tol ) const {
Hv.set(v.dual());
}
void applyInverse( Vector<Real> &Hv, const Vector<Real> &v, Real &tol ) const {
v_->set(v);
bnd_->pruneActive(*v_,*xlam_,eps_);
if ( useSecant_ ) {
secant_->applyH(Hv,*v_);
}
else {
obj_->precond(Hv,*v_,*x_,tol);
}
bnd_->pruneActive(Hv,*xlam_,eps_);
}
};
/** \brief Compute the gradient-based criticality measure.
The criticality measure is
\f$\|x_k - P_{[a,b]}(x_k-\nabla f(x_k))\|_{\mathcal{X}}\f$.
Here, \f$P_{[a,b]}\f$ denotes the projection onto the
bound constraints.
@param[in] x is the current iteration
@param[in] obj is the objective function
@param[in] con are the bound constraints
@param[in] tol is a tolerance for inexact evaluations of the objective function
*/
Real computeCriticalityMeasure(Vector<Real> &x, Objective<Real> &obj, BoundConstraint<Real> &con, Real tol) {
Real one(1);
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
obj.gradient(*(step_state->gradientVec),x,tol);
xtmp_->set(x);
xtmp_->axpy(-one,(step_state->gradientVec)->dual());
con.project(*xtmp_);
xtmp_->axpy(-one,x);
return xtmp_->norm();
}
public:
/** \brief Constructor.
@param[in] parlist is a parameter list containing relevent algorithmic information
@param[in] useSecant is a bool which determines whether or not the algorithm uses
a secant approximation of the Hessian
*/
PrimalDualActiveSetStep( Teuchos::ParameterList &parlist )
: Step<Real>::Step(), krylov_(Teuchos::null),
iterCR_(0), flagCR_(0), itol_(0),
maxit_(0), iter_(0), flag_(0), stol_(0), gtol_(0), scale_(0),
neps_(-ROL_EPSILON<Real>()), feasible_(false),
lambda_(Teuchos::null), xlam_(Teuchos::null), x0_(Teuchos::null),
xbnd_(Teuchos::null), As_(Teuchos::null), xtmp_(Teuchos::null),
res_(Teuchos::null), Ag_(Teuchos::null), rtmp_(Teuchos::null),
gtmp_(Teuchos::null),
esec_(SECANT_LBFGS), secant_(Teuchos::null), useSecantPrecond_(false),
useSecantHessVec_(false) {
Real one(1), oem6(1.e-6), oem8(1.e-8);
// Algorithmic parameters
maxit_ = parlist.sublist("Step").sublist("Primal Dual Active Set").get("Iteration Limit",10);
stol_ = parlist.sublist("Step").sublist("Primal Dual Active Set").get("Relative Step Tolerance",oem8);
gtol_ = parlist.sublist("Step").sublist("Primal Dual Active Set").get("Relative Gradient Tolerance",oem6);
scale_ = parlist.sublist("Step").sublist("Primal Dual Active Set").get("Dual Scaling", one);
// Build secant object
esec_ = StringToESecant(parlist.sublist("General").sublist("Secant").get("Type","Limited-Memory BFGS"));
useSecantHessVec_ = parlist.sublist("General").sublist("Secant").get("Use as Hessian", false);
useSecantPrecond_ = parlist.sublist("General").sublist("Secant").get("Use as Preconditioner", false);
if ( useSecantHessVec_ || useSecantPrecond_ ) {
secant_ = SecantFactory<Real>(parlist);
}
// Build Krylov object
krylov_ = KrylovFactory<Real>(parlist);
}
/** \brief Initialize step.
This includes projecting the initial guess onto the constraints,
computing the initial objective function value and gradient,
and initializing the dual variables.
@param[in,out] x is the initial guess
@param[in] obj is the objective function
@param[in] con are the bound constraints
@param[in] algo_state is the current state of the algorithm
*/
void initialize( Vector<Real> &x, const Vector<Real> &s, const Vector<Real> &g,
Objective<Real> &obj, BoundConstraint<Real> &con,
AlgorithmState<Real> &algo_state ) {
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
Real zero(0), one(1);
// Initialize state descent direction and gradient storage
step_state->descentVec = s.clone();
step_state->gradientVec = g.clone();
step_state->searchSize = zero;
// Initialize additional storage
xlam_ = x.clone();
x0_ = x.clone();
xbnd_ = x.clone();
As_ = s.clone();
xtmp_ = x.clone();
res_ = g.clone();
Ag_ = g.clone();
rtmp_ = g.clone();
gtmp_ = g.clone();
// Project x onto constraint set
con.project(x);
// Update objective function, get value, and get gradient
Real tol = std::sqrt(ROL_EPSILON<Real>());
obj.update(x,true,algo_state.iter);
algo_state.value = obj.value(x,tol);
algo_state.nfval++;
algo_state.gnorm = computeCriticalityMeasure(x,obj,con,tol);
algo_state.ngrad++;
// Initialize dual variable
lambda_ = s.clone();
lambda_->set((step_state->gradientVec)->dual());
lambda_->scale(-one);
//con.setVectorToLowerBound(*lambda_);
}
/** \brief Compute step.
Given \f$x_k\f$, this function first builds the
primal-dual active sets
\f$\mathcal{A}_k^-\f$ and \f$\mathcal{A}_k^+\f$.
Next, it uses CR to compute the inactive
components of the step by solving
\f[
\nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{I}_k}(s_k)_{\mathcal{I}_k} =
-\nabla f(x_k)_{\mathcal{I}_k}
-\nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{A}_k} (s_k)_{\mathcal{A}_k}.
\f]
Finally, it updates the active components of the
dual variables as
\f[
\lambda_{k+1} = -\nabla f(x_k)_{\mathcal{A}_k}
-(\nabla^2 f(x_k) s_k)_{\mathcal{A}_k}.
\f]
@param[out] s is the step computed via PDAS
@param[in] x is the current iterate
@param[in] obj is the objective function
@param[in] con are the bound constraints
@param[in] algo_state is the current state of the algorithm
*/
void compute( Vector<Real> &s, const Vector<Real> &x, Objective<Real> &obj, BoundConstraint<Real> &con,
AlgorithmState<Real> &algo_state ) {
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
Real zero(0), one(1);
s.zero();
x0_->set(x);
res_->set(*(step_state->gradientVec));
for ( iter_ = 0; iter_ < maxit_; iter_++ ) {
/********************************************************************/
// MODIFY ITERATE VECTOR TO CHECK ACTIVE SET
/********************************************************************/
xlam_->set(*x0_); // xlam = x0
xlam_->axpy(scale_,*(lambda_)); // xlam = x0 + c*lambda
/********************************************************************/
// PROJECT x ONTO PRIMAL DUAL FEASIBLE SET
/********************************************************************/
As_->zero(); // As = 0
con.setVectorToUpperBound(*xbnd_); // xbnd = u
xbnd_->axpy(-one,x); // xbnd = u - x
xtmp_->set(*xbnd_); // tmp = u - x
con.pruneUpperActive(*xtmp_,*xlam_,neps_); // tmp = I(u - x)
xbnd_->axpy(-one,*xtmp_); // xbnd = A(u - x)
As_->plus(*xbnd_); // As += A(u - x)
con.setVectorToLowerBound(*xbnd_); // xbnd = l
xbnd_->axpy(-one,x); // xbnd = l - x
xtmp_->set(*xbnd_); // tmp = l - x
con.pruneLowerActive(*xtmp_,*xlam_,neps_); // tmp = I(l - x)
xbnd_->axpy(-one,*xtmp_); // xbnd = A(l - x)
As_->plus(*xbnd_); // As += A(l - x)
/********************************************************************/
// APPLY HESSIAN TO ACTIVE COMPONENTS OF s AND REMOVE INACTIVE
/********************************************************************/
itol_ = std::sqrt(ROL_EPSILON<Real>());
if ( useSecantHessVec_ && secant_ != Teuchos::null ) { // IHAs = H*As
secant_->applyB(*gtmp_,*As_);
}
else {
obj.hessVec(*gtmp_,*As_,x,itol_);
}
con.pruneActive(*gtmp_,*xlam_,neps_); // IHAs = I(H*As)
/********************************************************************/
// SEPARATE ACTIVE AND INACTIVE COMPONENTS OF THE GRADIENT
/********************************************************************/
rtmp_->set(*(step_state->gradientVec)); // Inactive components
con.pruneActive(*rtmp_,*xlam_,neps_);
Ag_->set(*(step_state->gradientVec)); // Active components
Ag_->axpy(-one,*rtmp_);
/********************************************************************/
// SOLVE REDUCED NEWTON SYSTEM
/********************************************************************/
rtmp_->plus(*gtmp_);
rtmp_->scale(-one); // rhs = -Ig - I(H*As)
s.zero();
if ( rtmp_->norm() > zero ) {
// Initialize Hessian and preconditioner
Teuchos::RCP<Objective<Real> > obj_ptr = Teuchos::rcpFromRef(obj);
Teuchos::RCP<BoundConstraint<Real> > con_ptr = Teuchos::rcpFromRef(con);
Teuchos::RCP<LinearOperator<Real> > hessian
= Teuchos::rcp(new HessianPD(obj_ptr,con_ptr,
algo_state.iterateVec,xlam_,neps_,secant_,useSecantHessVec_));
Teuchos::RCP<LinearOperator<Real> > precond
= Teuchos::rcp(new PrecondPD(obj_ptr,con_ptr,
algo_state.iterateVec,xlam_,neps_,secant_,useSecantPrecond_));
//solve(s,*rtmp_,*xlam_,x,obj,con); // Call conjugate residuals
krylov_->run(s,*hessian,*rtmp_,*precond,iterCR_,flagCR_);
con.pruneActive(s,*xlam_,neps_); // s <- Is
}
s.plus(*As_); // s = Is + As
/********************************************************************/
// UPDATE MULTIPLIER
/********************************************************************/
if ( useSecantHessVec_ && secant_ != Teuchos::null ) {
secant_->applyB(*rtmp_,s);
}
else {
obj.hessVec(*rtmp_,s,x,itol_);
}
gtmp_->set(*rtmp_);
con.pruneActive(*gtmp_,*xlam_,neps_);
lambda_->set(*rtmp_);
lambda_->axpy(-one,*gtmp_);
lambda_->plus(*Ag_);
lambda_->scale(-one);
/********************************************************************/
// UPDATE STEP
/********************************************************************/
x0_->set(x);
x0_->plus(s);
res_->set(*(step_state->gradientVec));
res_->plus(*rtmp_);
// Compute criticality measure
xtmp_->set(*x0_);
xtmp_->axpy(-one,res_->dual());
con.project(*xtmp_);
xtmp_->axpy(-one,*x0_);
// std::cout << s.norm() << " "
// << tmp->norm() << " "
// << res_->norm() << " "
// << lambda_->norm() << " "
// << flagCR_ << " "
// << iterCR_ << "\n";
if ( xtmp_->norm() < gtol_*algo_state.gnorm ) {
flag_ = 0;
break;
}
if ( s.norm() < stol_*x.norm() ) {
flag_ = 2;
break;
}
}
if ( iter_ == maxit_ ) {
flag_ = 1;
}
else {
iter_++;
}
}
/** \brief Update step, if successful.
This function returns \f$x_{k+1} = x_k + s_k\f$.
It also updates secant information if being used.
@param[in] x is the new iterate
@param[out] s is the step computed via PDAS
@param[in] obj is the objective function
@param[in] con are the bound constraints
@param[in] algo_state is the current state of the algorithm
*/
void update( Vector<Real> &x, const Vector<Real> &s, Objective<Real> &obj, BoundConstraint<Real> &con,
AlgorithmState<Real> &algo_state ) {
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
x.plus(s);
feasible_ = con.isFeasible(x);
algo_state.snorm = s.norm();
algo_state.iter++;
Real tol = std::sqrt(ROL_EPSILON<Real>());
obj.update(x,true,algo_state.iter);
algo_state.value = obj.value(x,tol);
algo_state.nfval++;
if ( secant_ != Teuchos::null ) {
gtmp_->set(*(step_state->gradientVec));
}
algo_state.gnorm = computeCriticalityMeasure(x,obj,con,tol);
algo_state.ngrad++;
if ( secant_ != Teuchos::null ) {
secant_->updateStorage(x,*(step_state->gradientVec),*gtmp_,s,algo_state.snorm,algo_state.iter+1);
}
(algo_state.iterateVec)->set(x);
}
/** \brief Print iterate header.
This function produces a string containing
header information.
*/
std::string printHeader( void ) const {
std::stringstream hist;
hist << " ";
hist << std::setw(6) << std::left << "iter";
hist << std::setw(15) << std::left << "value";
hist << std::setw(15) << std::left << "gnorm";
hist << std::setw(15) << std::left << "snorm";
hist << std::setw(10) << std::left << "#fval";
hist << std::setw(10) << std::left << "#grad";
if ( maxit_ > 1 ) {
hist << std::setw(10) << std::left << "iterPDAS";
hist << std::setw(10) << std::left << "flagPDAS";
}
else {
hist << std::setw(10) << std::left << "iterCR";
hist << std::setw(10) << std::left << "flagCR";
}
hist << std::setw(10) << std::left << "feasible";
hist << "\n";
return hist.str();
}
/** \brief Print step name.
This function produces a string containing
the algorithmic step information.
*/
std::string printName( void ) const {
std::stringstream hist;
hist << "\nPrimal Dual Active Set Newton's Method\n";
return hist.str();
}
/** \brief Print iterate status.
This function prints the iteration status.
@param[in] algo_state is the current state of the algorithm
@param[in] printHeader if set to true will print the header at each iteration
*/
virtual std::string print( AlgorithmState<Real> &algo_state, bool print_header = false ) const {
std::stringstream hist;
hist << std::scientific << std::setprecision(6);
if ( algo_state.iter == 0 ) {
hist << printName();
}
if ( print_header ) {
hist << printHeader();
}
if ( algo_state.iter == 0 ) {
hist << " ";
hist << std::setw(6) << std::left << algo_state.iter;
hist << std::setw(15) << std::left << algo_state.value;
hist << std::setw(15) << std::left << algo_state.gnorm;
hist << "\n";
}
else {
hist << " ";
hist << std::setw(6) << std::left << algo_state.iter;
hist << std::setw(15) << std::left << algo_state.value;
hist << std::setw(15) << std::left << algo_state.gnorm;
hist << std::setw(15) << std::left << algo_state.snorm;
hist << std::setw(10) << std::left << algo_state.nfval;
hist << std::setw(10) << std::left << algo_state.ngrad;
if ( maxit_ > 1 ) {
hist << std::setw(10) << std::left << iter_;
hist << std::setw(10) << std::left << flag_;
}
else {
hist << std::setw(10) << std::left << iterCR_;
hist << std::setw(10) << std::left << flagCR_;
}
if ( feasible_ ) {
hist << std::setw(10) << std::left << "YES";
}
else {
hist << std::setw(10) << std::left << "NO";
}
hist << "\n";
}
return hist.str();
}
}; // class PrimalDualActiveSetStep
} // namespace ROL
#endif
// void solve(Vector<Real> &sol, const Vector<Real> &rhs, const Vector<Real> &xlam, const Vector<Real> &x,
// Objective<Real> &obj, BoundConstraint<Real> &con) {
// Real rnorm = rhs.norm();
// Real rtol = std::min(tol1_,tol2_*rnorm);
// itol_ = std::sqrt(ROL_EPSILON<Real>());
// sol.zero();
//
// Teuchos::RCP<Vector<Real> > res = rhs.clone();
// res->set(rhs);
//
// Teuchos::RCP<Vector<Real> > v = x.clone();
// con.pruneActive(*res,xlam,neps_);
// obj.precond(*v,*res,x,itol_);
// con.pruneActive(*v,xlam,neps_);
//
// Teuchos::RCP<Vector<Real> > p = x.clone();
// p->set(*v);
//
// Teuchos::RCP<Vector<Real> > Hp = x.clone();
//
// iterCR_ = 0;
// flagCR_ = 0;
//
// Real kappa = 0.0, beta = 0.0, alpha = 0.0, tmp = 0.0, rv = v->dot(*res);
//
// for (iterCR_ = 0; iterCR_ < maxitCR_; iterCR_++) {
// if ( false ) {
// itol_ = rtol/(maxitCR_*rnorm);
// }
// con.pruneActive(*p,xlam,neps_);
// if ( secant_ == Teuchos::null ) {
// obj.hessVec(*Hp, *p, x, itol_);
// }
// else {
// secant_->applyB( *Hp, *p, x );
// }
// con.pruneActive(*Hp,xlam,neps_);
//
// kappa = p->dot(*Hp);
// if ( kappa <= 0.0 ) { flagCR_ = 2; break; }
// alpha = rv/kappa;
// sol.axpy(alpha,*p);
//
// res->axpy(-alpha,*Hp);
// rnorm = res->norm();
// if ( rnorm < rtol ) { break; }
//
// con.pruneActive(*res,xlam,neps_);
// obj.precond(*v,*res,x,itol_);
// con.pruneActive(*v,xlam,neps_);
// tmp = rv;
// rv = v->dot(*res);
// beta = rv/tmp;
//
// p->scale(beta);
// p->axpy(1.0,*v);
// }
// if ( iterCR_ == maxitCR_ ) {
// flagCR_ = 1;
// }
// else {
// iterCR_++;
// }
// }
// /** \brief Apply the inactive components of the Hessian operator.
//
// I.e., the components corresponding to \f$\mathcal{I}_k\f$.
//
// @param[out] hv is the result of applying the Hessian at @b x to
// @b v
// @param[in] v is the direction in which we apply the Hessian
// @param[in] x is the current iteration vector \f$x_k\f$
// @param[in] xlam is the vector \f$x_k + c\lambda_k\f$
// @param[in] obj is the objective function
// @param[in] con are the bound constraints
// */
// void applyInactiveHessian(Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x,
// const Vector<Real> &xlam, Objective<Real> &obj, BoundConstraint<Real> &con) {
// Teuchos::RCP<Vector<Real> > tmp = v.clone();
// tmp->set(v);
// con.pruneActive(*tmp,xlam,neps_);
// if ( secant_ == Teuchos::null ) {
// obj.hessVec(hv,*tmp,x,itol_);
// }
// else {
// secant_->applyB(hv,*tmp,x);
// }
// con.pruneActive(hv,xlam,neps_);
// }
//
// /** \brief Apply the inactive components of the preconditioner operator.
//
// I.e., the components corresponding to \f$\mathcal{I}_k\f$.
//
// @param[out] hv is the result of applying the preconditioner at @b x to
// @b v
// @param[in] v is the direction in which we apply the preconditioner
// @param[in] x is the current iteration vector \f$x_k\f$
// @param[in] xlam is the vector \f$x_k + c\lambda_k\f$
// @param[in] obj is the objective function
// @param[in] con are the bound constraints
// */
// void applyInactivePrecond(Vector<Real> &pv, const Vector<Real> &v, const Vector<Real> &x,
// const Vector<Real> &xlam, Objective<Real> &obj, BoundConstraint<Real> &con) {
// Teuchos::RCP<Vector<Real> > tmp = v.clone();
// tmp->set(v);
// con.pruneActive(*tmp,xlam,neps_);
// obj.precond(pv,*tmp,x,itol_);
// con.pruneActive(pv,xlam,neps_);
// }
//
// /** \brief Solve the inactive part of the PDAS optimality system.
//
// The inactive PDAS optimality system is
// \f[
// \nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{I}_k}s =
// -\nabla f(x_k)_{\mathcal{I}_k}
// -\nabla^2 f(x_k)_{\mathcal{I}_k,\mathcal{A}_k} (s_k)_{\mathcal{A}_k}.
// \f]
// Since the inactive part of the Hessian may not be positive definite, we solve
// using CR.
//
// @param[out] sol is the vector containing the solution
// @param[in] rhs is the right-hand side vector
// @param[in] xlam is the vector \f$x_k + c\lambda_k\f$
// @param[in] x is the current iteration vector \f$x_k\f$
// @param[in] obj is the objective function
// @param[in] con are the bound constraints
// */
// // Solve the inactive part of the optimality system using conjugate residuals
// void solve(Vector<Real> &sol, const Vector<Real> &rhs, const Vector<Real> &xlam, const Vector<Real> &x,
// Objective<Real> &obj, BoundConstraint<Real> &con) {
// // Initialize Residual
// Teuchos::RCP<Vector<Real> > res = rhs.clone();
// res->set(rhs);
// Real rnorm = res->norm();
// Real rtol = std::min(tol1_,tol2_*rnorm);
// if ( false ) { itol_ = rtol/(maxitCR_*rnorm); }
// sol.zero();
//
// // Apply preconditioner to residual r = Mres
// Teuchos::RCP<Vector<Real> > r = x.clone();
// applyInactivePrecond(*r,*res,x,xlam,obj,con);
//
// // Initialize direction p = v
// Teuchos::RCP<Vector<Real> > p = x.clone();
// p->set(*r);
//
// // Apply Hessian to v
// Teuchos::RCP<Vector<Real> > Hr = x.clone();
// applyInactiveHessian(*Hr,*r,x,xlam,obj,con);
//
// // Apply Hessian to p
// Teuchos::RCP<Vector<Real> > Hp = x.clone();
// Teuchos::RCP<Vector<Real> > MHp = x.clone();
// Hp->set(*Hr);
//
// iterCR_ = 0;
// flagCR_ = 0;
//
// Real kappa = 0.0, beta = 0.0, alpha = 0.0, tmp = 0.0, rHr = Hr->dot(*r);
//
// for (iterCR_ = 0; iterCR_ < maxitCR_; iterCR_++) {
// // Precondition Hp
// applyInactivePrecond(*MHp,*Hp,x,xlam,obj,con);
//
// kappa = Hp->dot(*MHp); // p' H M H p
// alpha = rHr/kappa; // r' M H M r
// sol.axpy(alpha,*p); // update step
// res->axpy(-alpha,*Hp); // residual
// r->axpy(-alpha,*MHp); // preconditioned residual
//
// // recompute rnorm and decide whether or not to exit
// rnorm = res->norm();
// if ( rnorm < rtol ) { break; }
//
// // Apply Hessian to v
// itol_ = rtol/(maxitCR_*rnorm);
// applyInactiveHessian(*Hr,*r,x,xlam,obj,con);
//
// tmp = rHr;
// rHr = Hr->dot(*r);
// beta = rHr/tmp;
// p->scale(beta);
// p->axpy(1.0,*r);
// Hp->scale(beta);
// Hp->axpy(1.0,*Hr);
// }
// if ( iterCR_ == maxitCR_ ) {
// flagCR_ = 1;
// }
// else {
// iterCR_++;
// }
// }
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