/usr/include/trilinos/ROL_OptimizationProblem.hpp is in libtrilinos-rol-dev 12.10.1-3.
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// ************************************************************************
//
// Rapid Optimization Library (ROL) Package
// Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
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//
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//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
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// Drew Kouri (dpkouri@sandia.gov) and
// Denis Ridzal (dridzal@sandia.gov)
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// @HEADER
#ifndef ROL_OPTIMIZATIONPROBLEM_HPP
#define ROL_OPTIMIZATIONPROBLEM_HPP
#include "Teuchos_ParameterList.hpp"
#include "ROL_Objective.hpp"
#include "ROL_Vector.hpp"
#include "ROL_BoundConstraint.hpp"
#include "ROL_InteriorPoint.hpp"
#include "ROL_LogBarrierObjective.hpp"
#include "ROL_InequalityConstraint.hpp"
#include "ROL_BoundInequalityConstraint.hpp"
#include "ROL_ObjectiveFromBoundConstraint.hpp"
#include "ROL_RandomVector.hpp"
namespace ROL {
/*
* Note: We may wish to consider making the get functions private and make Algorithm
* a friend of OptimizationProblem as Algorithm is the only class which should
* need these functions and they may return something other than what the user
* expects (penalized instead of raw objective, solution and slack instead of
* solution, etc).
*/
template<class Real>
class OptimizationProblem {
typedef PartitionedVector<Real> PV;
typedef typename PV::size_type size_type;
private:
Teuchos::RCP<Objective<Real> > obj_;
Teuchos::RCP<Vector<Real> > sol_;
Teuchos::RCP<BoundConstraint<Real> > bnd_;
Teuchos::RCP<EqualityConstraint<Real> > con_;
Teuchos::RCP<InequalityConstraint<Real> > incon_;
Teuchos::RCP<Vector<Real> > mul_;
Teuchos::RCP<Teuchos::ParameterList> parlist_;
bool hasSlack_;
const static size_type OPT = 0;
const static size_type SLACK = 1;
public:
virtual ~OptimizationProblem(void) {}
OptimizationProblem(void)
: obj_(Teuchos::null), sol_(Teuchos::null), bnd_(Teuchos::null),
con_(Teuchos::null), mul_(Teuchos::null),
parlist_(Teuchos::null), hasSlack_(false) {}
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<BoundConstraint<Real> > &bnd = Teuchos::null,
const Teuchos::RCP<Teuchos::ParameterList> &parlist = Teuchos::null)
: obj_(obj), sol_(sol), bnd_(Teuchos::null), con_(Teuchos::null), mul_(Teuchos::null),
parlist_(parlist), hasSlack_(false) {
if ( parlist != Teuchos::null ) {
if ( bnd != Teuchos::null ) {
Teuchos::ParameterList &stepList = parlist->sublist("Step");
std::string step = stepList.get("Type","Trust Region");
if( step == "Interior Point" ) {
if ( bnd->isActivated() ) {
Teuchos::ParameterList &iplist = stepList.sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
// Build composite constraint and multipliers
incon_ = Teuchos::rcp(new BoundInequalityConstraint<Real>(*bnd,*sol));
con_ = Teuchos::rcp(new InteriorPoint::CompositeConstraint<Real>(incon_));
Teuchos::RCP<Vector<Real> > lmult1 = sol->dual().clone();
Teuchos::RCP<Vector<Real> > lmult2 = sol->dual().clone();
Teuchos::RCP<Vector<Real> > inmul = CreatePartitionedVector(lmult1,lmult2);
// Create slack variables - fill with parlist value
Elementwise::Fill<Real> fill(slack_ival);
Teuchos::RCP<Vector<Real> > slack1 = sol->clone();
slack1->applyUnary(fill);
Teuchos::RCP<Vector<Real> > slack2 = sol->clone();
slack2->applyUnary(fill);
Teuchos::RCP<Vector<Real> > slack = CreatePartitionedVector(slack1,slack2);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul);
// Create penalty
Teuchos::RCP<Objective<Real> > barrier
= Teuchos::rcp( new LogBarrierObjective<Real> );
obj_ = Teuchos::rcp( new InteriorPoint::PenalizedObjective<Real>(obj,barrier,*sol_,mu) );
}
else {
// Exception
}
}
else { // Not an Interior Point, but have parameters
bnd_ = bnd;
}
}
}
else {
bnd_ = bnd;
}
}
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<EqualityConstraint<Real> > &con,
const Teuchos::RCP<Vector<Real> > &mul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist = Teuchos::null)
: obj_(obj), sol_(sol), bnd_(Teuchos::null), con_(con), mul_(mul),
parlist_(parlist), hasSlack_(false) {}
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<BoundConstraint<Real> > &bnd,
const Teuchos::RCP<EqualityConstraint<Real> > &con,
const Teuchos::RCP<Vector<Real> > &mul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist = Teuchos::null)
: obj_(obj), sol_(sol), bnd_(Teuchos::null), con_(con), mul_(mul),
parlist_(parlist), hasSlack_(true) {
if ( parlist != Teuchos::null ) {
Teuchos::ParameterList &stepList = parlist->sublist("Step");
std::string step = stepList.get("Type","Trust Region");
if ( bnd->isActivated() && step == "Interior Point" ) {
Teuchos::ParameterList &iplist = stepList.sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
// Build composite constraint and multipliers
incon_ = Teuchos::rcp(new BoundInequalityConstraint<Real>(*bnd,*sol));
con_ = Teuchos::rcp(new InteriorPoint::CompositeConstraint<Real>(incon_,con));
Teuchos::RCP<Vector<Real> > lmult1 = sol->clone();
Teuchos::RCP<Vector<Real> > lmult2 = sol->clone();
Teuchos::RCP<Vector<Real> > inmul = CreatePartitionedVector(lmult1,lmult2);
// Create slack variables - fill with parlist value
Elementwise::Fill<Real> fill(slack_ival);
Teuchos::RCP<Vector<Real> > slack1 = sol->clone();
slack1->applyUnary(fill);
Teuchos::RCP<Vector<Real> > slack2 = sol->clone();
slack2->applyUnary(fill);
Teuchos::RCP<Vector<Real> > slack = CreatePartitionedVector(slack1,slack2);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul,mul);
// Create penalty
Teuchos::RCP<Objective<Real> > barrier
= Teuchos::rcp( new LogBarrierObjective<Real> );
obj_ = Teuchos::rcp( new InteriorPoint::PenalizedObjective<Real>(obj,barrier,*sol_,mu) );
}
else {
bnd_ = bnd;
}
}
else {
bnd_ = bnd;
}
}
// For interior points without equality constraint
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<InequalityConstraint<Real> > &incon,
const Teuchos::RCP<Vector<Real> > &inmul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist )
: obj_(Teuchos::null), sol_(Teuchos::null),
con_(Teuchos::null), mul_(Teuchos::null),
parlist_(Teuchos::null), hasSlack_(true) {
using InteriorPoint::PenalizedObjective;
using InteriorPoint::CompositeConstraint;
using Elementwise::Fill;
using Teuchos::RCP; using Teuchos::rcp;
Teuchos::ParameterList &iplist = parlist->sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
con_ = rcp( new CompositeConstraint<Real>(incon) );
// Create slack variables - fill with parlist value
RCP<Vector<Real> > slack = inmul->dual().clone();
Fill<Real> fill(slack_ival);
slack->applyUnary(fill);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul);
// Create penalty
RCP<Objective<Real> > barrier = rcp( new LogBarrierObjective<Real> );
obj_ = rcp( new PenalizedObjective<Real>(obj,barrier,*sol_,mu) );
}
// Bound but no equality
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<BoundConstraint<Real> > &bnd,
const Teuchos::RCP<InequalityConstraint<Real> > &incon,
const Teuchos::RCP<Vector<Real> > &inmul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist )
: obj_(Teuchos::null), sol_(Teuchos::null), bnd_(bnd),
con_(Teuchos::null), mul_(Teuchos::null),
parlist_(Teuchos::null), hasSlack_(true) {
using InteriorPoint::PenalizedObjective;
using InteriorPoint::CompositeConstraint;
using Elementwise::Fill;
using Teuchos::RCP; using Teuchos::rcp;
Teuchos::ParameterList &iplist = parlist->sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
con_ = rcp( new CompositeConstraint<Real>(incon) );
// Create slack variables - fill with parlist value
RCP<Vector<Real> > slack = inmul->dual().clone();
Fill<Real> fill(slack_ival);
slack->applyUnary(fill);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul);
// Create penalties
RCP<Objective<Real> > slack_barrier = rcp( new LogBarrierObjective<Real> );
RCP<Objective<Real> > bc_barrier = rcp( new ObjectiveFromBoundConstraint<Real>(*bnd,*parlist) );
obj_ = rcp( new PenalizedObjective<Real>(obj,slack_barrier,bc_barrier,*sol_,mu) );
}
// For interior points with equality constraint
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<EqualityConstraint<Real> > &eqcon,
const Teuchos::RCP<Vector<Real> > &eqmul,
const Teuchos::RCP<InequalityConstraint<Real> > &incon,
const Teuchos::RCP<Vector<Real> > &inmul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist )
: obj_(Teuchos::null), sol_(Teuchos::null),
con_(Teuchos::null), mul_(Teuchos::null),
parlist_(parlist), hasSlack_(true) {
using InteriorPoint::PenalizedObjective;
using InteriorPoint::CompositeConstraint;
using Elementwise::Fill;
using Teuchos::RCP; using Teuchos::rcp;
Teuchos::ParameterList &iplist = parlist->sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
con_ = rcp( new CompositeConstraint<Real>(incon,eqcon) );
// Create slack variables - fill with parlist value
RCP<Vector<Real> > slack = inmul->dual().clone();
Fill<Real> fill(slack_ival);
slack->applyUnary(fill);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul,eqmul);
// Create penalty
RCP<Objective<Real> > slack_barrier = rcp( new LogBarrierObjective<Real> );
obj_ = rcp( new PenalizedObjective<Real>(obj,slack_barrier,*sol_,mu) );
}
// Both bound and equality constraint
OptimizationProblem(const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<Vector<Real> > &sol,
const Teuchos::RCP<BoundConstraint<Real> > &bnd,
const Teuchos::RCP<EqualityConstraint<Real> > &eqcon,
const Teuchos::RCP<Vector<Real> > &eqmul,
const Teuchos::RCP<InequalityConstraint<Real> > &incon,
const Teuchos::RCP<Vector<Real> > &inmul,
const Teuchos::RCP<Teuchos::ParameterList> &parlist )
: obj_(Teuchos::null), sol_(Teuchos::null), bnd_(bnd),
con_(Teuchos::null), mul_(Teuchos::null),
parlist_(parlist), hasSlack_(true) {
using InteriorPoint::PenalizedObjective;
using InteriorPoint::CompositeConstraint;
using Elementwise::Fill;
using Teuchos::RCP; using Teuchos::rcp;
Teuchos::ParameterList &iplist = parlist->sublist("Interior Point");
Real mu = iplist.get("Initial Barrier Penalty",1.0);
Real slack_ival = iplist.get("Initial Slack Variable Value",1.0);
con_ = rcp( new CompositeConstraint<Real>(incon,eqcon) );
// Create slack variables - fill with parlist value
RCP<Vector<Real> > slack = inmul->dual().clone();
Fill<Real> fill(slack_ival);
slack->applyUnary(fill);
// Form vector of optimization and slack variables
sol_ = CreatePartitionedVector(sol,slack);
// Form partitioned Lagrange multiplier
mul_ = CreatePartitionedVector(inmul,eqmul);
// Create penalties
RCP<Objective<Real> > slack_barrier = rcp( new LogBarrierObjective<Real> );
RCP<Objective<Real> > bc_barrier = rcp( new ObjectiveFromBoundConstraint<Real>(*bnd,*parlist) );
obj_ = rcp( new PenalizedObjective<Real>(obj,slack_barrier,bc_barrier,*sol_,mu) );
}
Teuchos::RCP<Objective<Real> > getObjective(void) {
return obj_;
}
void setObjective(const Teuchos::RCP<Objective<Real> > &obj) {
obj_ = obj;
}
Teuchos::RCP<Vector<Real> > getSolutionVector(void) {
return sol_;
}
void setSolutionVector(const Teuchos::RCP<Vector<Real> > &sol) {
sol_ = sol;
}
Teuchos::RCP<BoundConstraint<Real> > getBoundConstraint(void) {
return bnd_;
}
void setBoundConstraint(const Teuchos::RCP<BoundConstraint<Real> > &bnd) {
bnd_ = bnd;
}
Teuchos::RCP<EqualityConstraint<Real> > getEqualityConstraint(void) {
return con_;
}
void setEqualityConstraint(const Teuchos::RCP<EqualityConstraint<Real> > &con) {
con_ = con;
}
Teuchos::RCP<Vector<Real> > getMultiplierVector(void) {
return mul_;
}
void setMultiplierVector(const Teuchos::RCP<Vector<Real> > &mul) {
mul_ = mul;
}
Teuchos::RCP<Teuchos::ParameterList> getParameterList(void) {
return parlist_;
}
void setParameterList( const Teuchos::RCP<Teuchos::ParameterList> &parlist ) {
parlist_ = parlist;
}
virtual std::vector<std::vector<Real> > checkObjectiveGradient( const Vector<Real> &d,
const bool printToStream = true,
std::ostream & outStream = std::cout,
const int numSteps = ROL_NUM_CHECKDERIV_STEPS,
const int order = 1 ) {
if(hasSlack_) {
Teuchos::RCP<PV> ds = Teuchos::rcp_static_cast<PV>(sol_->clone());
ds->set(OPT,d);
RandomizeVector(*(ds->get(SLACK)));
return obj_->checkGradient(*sol_,*ds,printToStream,outStream,numSteps,order);
}
else {
return obj_->checkGradient(*sol_,d,printToStream,outStream,numSteps,order);
}
}
virtual std::vector<std::vector<Real> > checkObjectiveHessVec( const Vector<Real> &v,
const bool printToStream = true,
std::ostream & outStream = std::cout,
const int numSteps = ROL_NUM_CHECKDERIV_STEPS,
const int order = 1 ) {
if(hasSlack_) {
Teuchos::RCP<PV> vs = Teuchos::rcp_static_cast<PV>(sol_->clone());
vs->set(OPT,v);
RandomizeVector(*(vs->get(SLACK)));
return obj_->checkHessVec(*sol_,*vs,printToStream,outStream,numSteps,order);
}
else {
return obj_->checkHessVec(*sol_,v,printToStream,outStream,numSteps,order);
}
}
};
}
#endif
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