/usr/include/trilinos/ROL_LineSearch.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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// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
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// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
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// Questions? Contact lead developers:
// Drew Kouri (dpkouri@sandia.gov) and
// Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER
#ifndef ROL_LINESEARCH_H
#define ROL_LINESEARCH_H
/** \class ROL::LineSearch
\brief Provides interface for and implements line searches.
*/
#include "Teuchos_RCP.hpp"
#include "Teuchos_ParameterList.hpp"
#include "ROL_Types.hpp"
#include "ROL_Vector.hpp"
#include "ROL_Objective.hpp"
#include "ROL_BoundConstraint.hpp"
#include "ROL_ScalarFunction.hpp"
namespace ROL {
template<class Real>
class LineSearch {
private:
ECurvatureCondition econd_;
EDescent edesc_;
bool useralpha_;
bool usePrevAlpha_; // Use the previous step's accepted alpha as an initial guess
Real alpha0_;
int maxit_;
Real c1_;
Real c2_;
Real c3_;
Real eps_;
Real fmin_; // smallest fval encountered
Real alphaMin_; // Alpha that yields the smallest fval encountered
bool acceptMin_; // Use smallest fval if sufficient decrease not satisfied
bool itcond_; // true if maximum function evaluations reached
Teuchos::RCP<Vector<Real> > xtst_;
Teuchos::RCP<Vector<Real> > d_;
Teuchos::RCP<Vector<Real> > g_;
Teuchos::RCP<Vector<Real> > grad_;
// Teuchos::RCP<const Vector<Real> > grad_;
public:
virtual ~LineSearch() {}
// Constructor
LineSearch( Teuchos::ParameterList &parlist ) : eps_(0) {
Real one(1), p9(0.9), p6(0.6), p4(0.4), oem4(1.e-4), zero(0);
// Enumerations
edesc_ = StringToEDescent(parlist.sublist("Step").sublist("Line Search").sublist("Descent Method").get("Type","Quasi-Newton Method"));
econd_ = StringToECurvatureCondition(parlist.sublist("Step").sublist("Line Search").sublist("Curvature Condition").get("Type","Strong Wolfe Conditions"));
// Linesearch Parameters
alpha0_ = parlist.sublist("Step").sublist("Line Search").get("Initial Step Size",one);
useralpha_ = parlist.sublist("Step").sublist("Line Search").get("User Defined Initial Step Size",false);
usePrevAlpha_ = parlist.sublist("Step").sublist("Line Search").get("Use Previous Step Length as Initial Guess",false);
acceptMin_ = parlist.sublist("Step").sublist("Line Search").get("Accept Linesearch Minimizer",false);
maxit_ = parlist.sublist("Step").sublist("Line Search").get("Function Evaluation Limit",20);
c1_ = parlist.sublist("Step").sublist("Line Search").get("Sufficient Decrease Tolerance",oem4);
c2_ = parlist.sublist("Step").sublist("Line Search").sublist("Curvature Condition").get("General Parameter",p9);
c3_ = parlist.sublist("Step").sublist("Line Search").sublist("Curvature Condition").get("Generalized Wolfe Parameter",p6);
fmin_ = std::numeric_limits<Real>::max();
alphaMin_ = 0;
itcond_ = false;
c1_ = ((c1_ < zero) ? oem4 : c1_);
c2_ = ((c2_ < zero) ? p9 : c2_);
c3_ = ((c3_ < zero) ? p9 : c3_);
if ( c2_ <= c1_ ) {
c1_ = oem4;
c2_ = p9;
}
if ( edesc_ == DESCENT_NONLINEARCG ) {
c2_ = p4;
c3_ = std::min(one-c2_,c3_);
}
}
virtual void initialize( const Vector<Real> &x, const Vector<Real> &s, const Vector<Real> &g,
Objective<Real> &obj, BoundConstraint<Real> &con ) {
grad_ = g.clone();
xtst_ = x.clone();
d_ = s.clone();
g_ = g.clone();
}
virtual 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 ) = 0;
// Set epsilon for epsilon active sets
void setData(Real &eps, const Vector<Real> &g) {
eps_ = eps;
grad_->set(g);
}
// use this function to modify alpha and fval if the maximum number of iterations
// are reached
void setMaxitUpdate(Real &alpha, Real &fnew, const Real &fold) {
// Use local minimizer
if( itcond_ && acceptMin_ ) {
alpha = alphaMin_;
fnew = fmin_;
}
// Take no step
else if(itcond_ && !acceptMin_) {
alpha = 0;
fnew = fold;
}
setNextInitialAlpha(alpha);
}
protected:
virtual bool status( const ELineSearch type, int &ls_neval, int &ls_ngrad, const Real alpha,
const Real fold, const Real sgold, const Real fnew,
const Vector<Real> &x, const Vector<Real> &s,
Objective<Real> &obj, BoundConstraint<Real> &con ) {
Real tol = std::sqrt(ROL_EPSILON<Real>()), one(1), two(2);
// Check Armijo Condition
bool armijo = false;
if ( con.isActivated() ) {
Real gs(0);
if ( edesc_ == DESCENT_STEEPEST ) {
updateIterate(*d_,x,s,alpha,con);
d_->scale(-one);
d_->plus(x);
gs = -s.dot(*d_);
}
else {
d_->set(s);
d_->scale(-one);
con.pruneActive(*d_,grad_->dual(),x,eps_);
gs = alpha*(grad_)->dot(d_->dual());
d_->zero();
updateIterate(*d_,x,s,alpha,con);
d_->scale(-one);
d_->plus(x);
con.pruneInactive(*d_,grad_->dual(),x,eps_);
gs += d_->dot(grad_->dual());
}
if ( fnew <= fold - c1_*gs ) {
armijo = true;
}
}
else {
if ( fnew <= fold + c1_*alpha*sgold ) {
armijo = true;
}
}
// Check Maximum Iteration
itcond_ = false;
if ( ls_neval >= maxit_ ) {
itcond_ = true;
}
// Check Curvature Condition
bool curvcond = false;
if ( armijo && ((type != LINESEARCH_BACKTRACKING && type != LINESEARCH_CUBICINTERP) ||
(edesc_ == DESCENT_NONLINEARCG)) ) {
if (econd_ == CURVATURECONDITION_GOLDSTEIN) {
if (fnew >= fold + (one-c1_)*alpha*sgold) {
curvcond = true;
}
}
else if (econd_ == CURVATURECONDITION_NULL) {
curvcond = true;
}
else {
updateIterate(*xtst_,x,s,alpha,con);
obj.update(*xtst_);
obj.gradient(*g_,*xtst_,tol);
Real sgnew(0);
if ( con.isActivated() ) {
d_->set(s);
d_->scale(-alpha);
con.pruneActive(*d_,s,x);
sgnew = -d_->dot(g_->dual());
}
else {
sgnew = s.dot(g_->dual());
}
ls_ngrad++;
if ( ((econd_ == CURVATURECONDITION_WOLFE)
&& (sgnew >= c2_*sgold))
|| ((econd_ == CURVATURECONDITION_STRONGWOLFE)
&& (std::abs(sgnew) <= c2_*std::abs(sgold)))
|| ((econd_ == CURVATURECONDITION_GENERALIZEDWOLFE)
&& (c2_*sgold <= sgnew && sgnew <= -c3_*sgold))
|| ((econd_ == CURVATURECONDITION_APPROXIMATEWOLFE)
&& (c2_*sgold <= sgnew && sgnew <= (two*c1_ - one)*sgold)) ) {
curvcond = true;
}
}
}
if(fnew<fmin_) {
fmin_ = fnew;
alphaMin_ = alpha;
}
if (type == LINESEARCH_BACKTRACKING || type == LINESEARCH_CUBICINTERP) {
if (edesc_ == DESCENT_NONLINEARCG) {
return ((armijo && curvcond) || itcond_);
}
else {
return (armijo || itcond_);
}
}
else {
return ((armijo && curvcond) || itcond_);
}
}
virtual Real getInitialAlpha(int &ls_neval, int &ls_ngrad, const Real fval, const Real gs,
const Vector<Real> &x, const Vector<Real> &s,
Objective<Real> &obj, BoundConstraint<Real> &con) {
Real val(1), one(1), half(0.5), p1(1.e-1);
if (useralpha_ || usePrevAlpha_ ) {
val = alpha0_;
}
else {
if (edesc_ == DESCENT_STEEPEST || edesc_ == DESCENT_NONLINEARCG) {
Real tol = std::sqrt(ROL_EPSILON<Real>());
// Evaluate objective at x + s
updateIterate(*d_,x,s,one,con);
obj.update(*d_);
Real fnew = obj.value(*d_,tol);
ls_neval++;
// Minimize quadratic interpolate to compute new alpha
Real denom = (fnew - fval - gs);
Real alpha = ((denom > ROL_EPSILON<Real>()) ? -half*gs/denom : one);
val = ((alpha > p1) ? alpha : one);
alpha0_ = val;
useralpha_ = true;
}
else {
val = one;
}
}
return val;
}
void setNextInitialAlpha( Real alpha ) {
if( usePrevAlpha_ ) {
alpha0_ = alpha;
}
}
void updateIterate(Vector<Real> &xnew, const Vector<Real> &x, const Vector<Real> &s, Real alpha,
BoundConstraint<Real> &con ) {
xnew.set(x);
xnew.axpy(alpha,s);
if ( con.isActivated() ) {
con.project(xnew);
}
}
bool useLocalMinimizer() {
return itcond_ && acceptMin_;
}
bool takeNoStep() {
return itcond_ && !acceptMin_;
}
};
}
#include "ROL_LineSearchFactory.hpp"
#endif
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