/usr/include/trilinos/ROL_ScalarMinimizationLineSearch.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
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// @HEADER
#ifndef ROL_ScalarMinimizationLineSearch_H
#define ROL_ScalarMinimizationLineSearch_H
/** \class ROL::ScalarMinimizationLineSearch
\brief Implements line search methods that attempt to minimize the
scalar function \f$\phi(t) := f(x+ts)\f$.
*/
#include "ROL_LineSearch.hpp"
#include "ROL_BrentsScalarMinimization.hpp"
#include "ROL_BisectionScalarMinimization.hpp"
#include "ROL_GoldenSectionScalarMinimization.hpp"
#include "ROL_ScalarFunction.hpp"
#include "ROL_Bracketing.hpp"
namespace ROL {
template<class Real>
class ScalarMinimizationLineSearch : public LineSearch<Real> {
private:
Teuchos::RCP<Vector<Real> > xnew_;
Teuchos::RCP<Vector<Real> > g_;
Teuchos::RCP<ScalarMinimization<Real> > sm_;
Teuchos::RCP<Bracketing<Real> > br_;
Teuchos::RCP<ScalarFunction<Real> > sf_;
ECurvatureCondition econd_;
Real c1_;
Real c2_;
Real c3_;
int max_nfval_;
class Phi : public ScalarFunction<Real> {
private:
const Teuchos::RCP<Vector<Real> > xnew_;
const Teuchos::RCP<Vector<Real> > g_;
const Teuchos::RCP<const Vector<Real> > x_;
const Teuchos::RCP<const Vector<Real> > s_;
const Teuchos::RCP<Objective<Real> > obj_;
const Teuchos::RCP<BoundConstraint<Real> > con_;
Real ftol_;
void updateIterate(Real alpha) {
xnew_->set(*x_);
xnew_->axpy(alpha,*s_);
if ( con_->isActivated() ) {
con_->project(*xnew_);
}
}
public:
Phi(const Teuchos::RCP<Vector<Real> > &xnew,
const Teuchos::RCP<Vector<Real> > &g,
const Teuchos::RCP<const Vector<Real> > &x,
const Teuchos::RCP<const Vector<Real> > &s,
const Teuchos::RCP<Objective<Real> > &obj,
const Teuchos::RCP<BoundConstraint<Real> > &con)
: xnew_(xnew), g_(g), x_(x), s_(s), obj_(obj), con_(con),
ftol_(std::sqrt(ROL_EPSILON<Real>())) {}
Real value(const Real alpha) {
updateIterate(alpha);
obj_->update(*xnew_);
return obj_->value(*xnew_,ftol_);
}
Real deriv(const Real alpha) {
updateIterate(alpha);
obj_->update(*xnew_);
obj_->gradient(*g_,*xnew_,ftol_);
return s_->dot(g_->dual());
}
};
class LineSearchStatusTest : public ScalarMinimizationStatusTest<Real> {
private:
Teuchos::RCP<ScalarFunction<Real> > phi_;
const Real f0_;
const Real g0_;
const Real c1_;
const Real c2_;
const Real c3_;
const int max_nfval_;
const ECurvatureCondition econd_;
public:
LineSearchStatusTest(const Real f0, const Real g0,
const Real c1, const Real c2, const Real c3,
const int max_nfval, ECurvatureCondition econd,
const Teuchos::RCP<ScalarFunction<Real> > &phi)
: phi_(phi), f0_(f0), g0_(g0), c1_(c1), c2_(c2), c3_(c3),
max_nfval_(max_nfval), econd_(econd) {}
bool check(Real &x, Real &fx, Real &gx,
int &nfval, int &ngval, const bool deriv = false) {
Real one(1), two(2);
bool armijo = (fx <= f0_ + c1_*x*g0_);
// bool itcond = (nfval >= max_nfval_);
bool curvcond = false;
// if (armijo && !itcond) {
if (armijo) {
if (econd_ == CURVATURECONDITION_GOLDSTEIN) {
curvcond = (fx >= f0_ + (one-c1_)*x*g0_);
}
else if (econd_ == CURVATURECONDITION_NULL) {
curvcond = true;
}
else {
if (!deriv) {
gx = phi_->deriv(x); ngval++;
}
if (econd_ == CURVATURECONDITION_WOLFE) {
curvcond = (gx >= c2_*g0_);
}
else if (econd_ == CURVATURECONDITION_STRONGWOLFE) {
curvcond = (std::abs(gx) <= c2_*std::abs(g0_));
}
else if (econd_ == CURVATURECONDITION_GENERALIZEDWOLFE) {
curvcond = (c2_*g0_ <= gx && gx <= -c3_*g0_);
}
else if (econd_ == CURVATURECONDITION_APPROXIMATEWOLFE) {
curvcond = (c2_*g0_ <= gx && gx <= (two*c1_ - one)*g0_);
}
}
}
//return (armijo && curvcond) || itcond;
return (armijo && curvcond);
}
};
public:
// Constructor
ScalarMinimizationLineSearch( Teuchos::ParameterList &parlist,
const Teuchos::RCP<ScalarMinimization<Real> > &sm = Teuchos::null,
const Teuchos::RCP<Bracketing<Real> > &br = Teuchos::null,
const Teuchos::RCP<ScalarFunction<Real> > &sf = Teuchos::null )
: LineSearch<Real>(parlist) {
Real zero(0), p4(0.4), p6(0.6), p9(0.9), oem4(1.e-4), oem10(1.e-10), one(1);
Teuchos::ParameterList &list0 = parlist.sublist("Step").sublist("Line Search");
Teuchos::ParameterList &list = list0.sublist("Line-Search Method");
// Get Bracketing Method
if( br == Teuchos::null ) {
br_ = Teuchos::rcp(new Bracketing<Real>());
}
else {
br_ = br;
}
// Get ScalarMinimization Method
std::string type = list.get("Type","Brent's");
Real tol = list.sublist(type).get("Tolerance",oem10);
int niter = list.sublist(type).get("Iteration Limit",1000);
Teuchos::ParameterList plist;
plist.sublist("Scalar Minimization").set("Type",type);
plist.sublist("Scalar Minimization").sublist(type).set("Tolerance",tol);
plist.sublist("Scalar Minimization").sublist(type).set("Iteration Limit",niter);
if( sm == Teuchos::null ) { // No user-provided ScalarMinimization object
if ( type == "Brent's" ) {
sm_ = Teuchos::rcp(new BrentsScalarMinimization<Real>(plist));
}
else if ( type == "Bisection" ) {
sm_ = Teuchos::rcp(new BisectionScalarMinimization<Real>(plist));
}
else if ( type == "Golden Section" ) {
sm_ = Teuchos::rcp(new GoldenSectionScalarMinimization<Real>(plist));
}
else {
TEUCHOS_TEST_FOR_EXCEPTION(true, std::invalid_argument,
">>> (ROL::ScalarMinimizationLineSearch): Undefined ScalarMinimization type!");
}
}
else {
sm_ = sm;
}
sf_ = sf;
// Status test for line search
econd_ = StringToECurvatureCondition(list0.sublist("Curvature Condition").get("Type","Strong Wolfe Conditions"));
max_nfval_ = list0.get("Function Evaluation Limit",20);
c1_ = list0.get("Sufficient Decrease Tolerance",oem4);
c2_ = list0.sublist("Curvature Condition").get("General Parameter",p9);
c3_ = list0.sublist("Curvature Condition").get("Generalized Wolfe Parameter",p6);
// Check status test inputs
c1_ = ((c1_ < zero) ? oem4 : c1_);
c2_ = ((c2_ < zero) ? p9 : c2_);
c3_ = ((c3_ < zero) ? p9 : c3_);
if ( c2_ <= c1_ ) {
c1_ = oem4;
c2_ = p9;
}
EDescent edesc = StringToEDescent(list0.sublist("Descent Method").get("Type","Quasi-Newton Method"));
if ( edesc == DESCENT_NONLINEARCG ) {
c2_ = p4;
c3_ = std::min(one-c2_,c3_);
}
}
void initialize( const Vector<Real> &x, const Vector<Real> &s, const Vector<Real> &g,
Objective<Real> &obj, BoundConstraint<Real> &con ) {
LineSearch<Real>::initialize(x,s,g,obj,con);
xnew_ = x.clone();
g_ = g.clone();
}
// Find the minimum of phi(alpha) = f(x + alpha*s) using Brent's method
void run( Real &alpha, Real &fval, int &ls_neval, int &ls_ngrad,
const Real &gs, const Vector<Real> &s, const Vector<Real> &x,
Objective<Real> &obj, BoundConstraint<Real> &con ) {
ls_neval = 0; ls_ngrad = 0;
// Get initial line search parameter
alpha = LineSearch<Real>::getInitialAlpha(ls_neval,ls_ngrad,fval,gs,x,s,obj,con);
// Build ScalarFunction and ScalarMinimizationStatusTest
Teuchos::RCP<const Vector<Real> > x_ptr = Teuchos::rcpFromRef(x);
Teuchos::RCP<const Vector<Real> > s_ptr = Teuchos::rcpFromRef(s);
Teuchos::RCP<Objective<Real> > obj_ptr = Teuchos::rcpFromRef(obj);
Teuchos::RCP<BoundConstraint<Real> > bnd_ptr = Teuchos::rcpFromRef(con);
Teuchos::RCP<ScalarFunction<Real> > phi;
if( sf_ == Teuchos::null ) {
phi = Teuchos::rcp(new Phi(xnew_,g_,x_ptr,s_ptr,obj_ptr,bnd_ptr));
}
else {
phi = sf_;
}
Teuchos::RCP<ScalarMinimizationStatusTest<Real> > test
= Teuchos::rcp(new LineSearchStatusTest(fval,gs,c1_,c2_,c3_,max_nfval_,econd_,phi));
// Run Bracketing
int nfval = 0, ngrad = 0;
Real A(0), fA = fval;
Real B = alpha, fB = phi->value(B);
br_->run(alpha,fval,A,fA,B,fB,nfval,ngrad,*phi,*test);
B = alpha;
ls_neval += nfval; ls_ngrad += ngrad;
// Run ScalarMinimization
nfval = 0, ngrad = 0;
sm_->run(fval, alpha, nfval, ngrad, *phi, A, B, *test);
ls_neval += nfval; ls_ngrad += ngrad;
LineSearch<Real>::setNextInitialAlpha(alpha);
}
};
}
#endif
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