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ModifiedNewton.cpp
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ModifiedNewton.cpp
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#include "ModifiedNewton.h"
namespace Daetk
{
using std::cerr;
using std::cout;
using std::endl;
using std::flush;
using std::max;
using std::min;
void ModifiedNewton::doChordIteration()
{CHORD_ITERATION = true;}
void ModifiedNewton::doFullNewton()
{CHORD_ITERATION = false;}
void ModifiedNewton::testResidual(real tol)
{TEST_RESIDUAL = true;resTol=tol;}
void ModifiedNewton::testConvergenceRate(bool flag)
{TEST_RATE = flag;}
void ModifiedNewton::linearSolverIsInexact()
{INEXACT_LINEAR_SOLVER=true;}
void ModifiedNewton::useLineSearch(int nls)
{ nLineSearches=nls; USE_LINE_SEARCH = true; }
void ModifiedNewton::setLineSearchFact(real lsRedIn)
{ lsRedFact= lsRedIn; }
void ModifiedNewton::setLineSearchMethod(LineSearchType lsType)
{
lineSearchMethod = lsType;
}
void ModifiedNewton::solveSubSystem(int start,int end,int stride,
int dimLS)
{SOLVE_SUB=true; VecIndex i(start,end); index=i; str=stride;}
ModifiedNewton::ModifiedNewton():
SOLVE_SUB(false),
USE_LINE_SEARCH(false),
lineSearchMethod(poorMansLS),
nLineSearches(0),
lsRedFact(0.5),
CHORD_ITERATION(true),
INEXACT_LINEAR_SOLVER(false),
TEST_RESIDUAL(false),
TEST_RATE(true),
RECOMPUTE_RATE(true),
maxIterations(4),
convergenceFactorp(0),
s(100.0),
nonlinearTolerance(.33),
p(),
residual(),
weightedNorm(0),
data(0),
linearSolver(0),
mnout("modNewt.txt")
{
Tracer tr("ModifiedNewton::ModifiedNewton()");
}
ModifiedNewton::ModifiedNewton(LinearSolver& linearSolverIn,
VectorNorm& normIn,
DataCollector& dataIn, int neq,
real linearTol,
real nonlinearTol, int maxit):
SOLVE_SUB(false),
USE_LINE_SEARCH(false),
lineSearchMethod(poorMansLS),
nLineSearches(0),
lsRedFact(0.5),
CHORD_ITERATION(true),
INEXACT_LINEAR_SOLVER(false),
TEST_RESIDUAL(false),
TEST_RATE(true),
RECOMPUTE_RATE(true),
maxIterations(maxit),
convergenceFactorp(0),
s(100.0),
nonlinearTolerance(nonlinearTol),
roundOffTolerance(linearTol),
p(neq),
residual(neq),
weightedNorm(&normIn),
data(&dataIn),
linearSolver(&linearSolverIn),
mnout("modNewt.txt")
{
Tracer tr("ModifiedNewton::ModifiedNewton(LinearSolver& linearSolverIn, WeightedVectorNorm& normIn, Data& dataIn, int neq)");
}
ModifiedNewton::~ModifiedNewton()
{
Tracer tr("ModifiedNewton::~ModifiedNewton()");
}
void ModifiedNewton::setConvergenceFactor(const real& cf)
{
convergenceFactorp = &cf;
}
void ModifiedNewton::computeRate()
{
rate = pow(normOfCorrection/normOfFirstCorrection,1.0/real(iterations-1));
if (rate < 1.0)
s=rate/(1.0-rate);
else
{
rate = 1.0;
s=100;
}
RECOMPUTE_RATE = false;
}
void ModifiedNewton::recomputeConvergenceRate()
{
RECOMPUTE_RATE=true;
}
bool ModifiedNewton::converged(VectorFunction& F)
{
bool evalError=false, conv=false;
if (TEST_RESIDUAL)
{
// cout<<nrm2(F.value(evalError))<<'\t'<<r0<<endl;
conv = (nrm2(F.value(evalError)) <= r0*resTol + resTol);
if (evalError) //should do a line search or something here
return false;
else
return conv;
}
else if (RECOMPUTE_RATE)
return false;
else
return s*normOfCorrection <= nonlinearTolerance;
}
bool ModifiedNewton::solve(Vec& correction,VectorFunction& F)
{
//cout<<"in newton solve"<<endl<<flush;
//This function will not exit in one iteration if RECOMPUTE_RATE is true, unlike DASPK's nonlinear
//solver which forces the rate to be recomputed by setting it (the inverse rate) to a large value (100).
//It is sometimes the case that even with s=100 the first correction is small enough to give convergence
//while still not being below roundoff. This causes a slight difference in the way the two codes run.
bool lsFailure,evalError=false;
residual=F.value(evalError);
if (evalError)
{
cerr<<"Predictor caused evaluation error; nonlinear solver returning failure"<<endl
<<"The calling routine should have caught this"<<endl;
return evalError;
}
if (TEST_RESIDUAL)
r0 = nrm2(residual);
iterations=0;
correction=0.0;
normOfPredictor=(*weightedNorm)(F.argument());
if (!INEXACT_LINEAR_SOLVER)
roundOffTolerance = 100.0 * normOfPredictor * MACHINE_EPSILON;
//roundOffTolerance = linearTolerance otherwise
//take one Newton Step and check to see if correction is extremely small
data->nonlinearSolverIteration();
++iterations;
if (*convergenceFactorp !=1.0 && CHORD_ITERATION)
{
//don't apply convergence factor to the entire vector
//if we're solving a subsystem of the equations
if(!SOLVE_SUB)
residual*=(*convergenceFactorp);
else
{
attache.attachToVecMulti(Vec::REF,residual,index);attache.setStrideMulti(str);
attache*=(*convergenceFactorp);
}
}
lsFailure=linearSolver->solve(residual,p);
if (lsFailure)
{
// cerr<<"Linear Solver solve failure in Newton Iteration"<<endl;
data->linearSolverFailure();
return lsFailure;
}
F.correctArgument(p);
normOfCorrection=(*weightedNorm)(p);
//uncomment for an alternavie subsystem solver...only check the norm of the subsystem correction
// if(!SOLVE_SUB)
// normOfCorrection=(*weightedNorm)(p);
// else
// {
// real sum=0.0;
// const Vec& scaling(weightedNorm->getScaling());
// for (int i=0;i<scaling.ldim()/2;i++)
// sum+=scaling[str*i]*p[str*i]*scaling[str*i]*p[str*i];
// normOfCorrection=sqrt(sum);
// }
#ifndef USE_BLAS
correction-=p;
#else
axpy(-1.0,p,correction);
#endif
if (normOfCorrection <= roundOffTolerance )
{
return false;
}
else
{
normOfFirstCorrection = normOfCorrection;
if (!CHORD_ITERATION) // if this is full or inexact newton we can't use a rate from the past
recomputeConvergenceRate();
if ( converged(F) )
return false;
}
real normOfOldCorrection=0.0, FnOld=r0; //force line search check on first iteration
while ( iterations < maxIterations )
{
residual = F.value(evalError);
//mnout<<residual<<F.argument();
if (evalError)
{
cerr<<"feval in ModifiedNewton Iteration, exiting with failure"<<endl;
return true;
}
// if ((normOfCorrection >= normOfOldCorrection && USE_LINE_SEARCH) || evalError)
// {
// real FnNew=FnOld;
// if (evalError)
// cerr<<"p or S out of range in Newton Iteration"<<endl;
// else
// FnNew = nrm2(F.value(evalError));
// if (((FnNew > FnOld) && USE_LINE_SEARCH) || evalError)
// {
// int lin_it=0;
// #ifndef USE_BLAS
// correction+=p;
// #else
// axpy(1.0,p,correction);
// #endif
// while (( ((FnNew > FnOld) && USE_LINE_SEARCH) || evalError) && lin_it < 20)
// {
// std::cerr<<"In Line Search--not safe for Mass formulations yet"<<std::endl;
// F.unCorrect();
// data->lineSearch();
// //only apply linesearch to subsystem correction
// if(!SOLVE_SUB)
// p*=0.01;
// else
// {
// attache.attachToVecMulti(Vec::REF,p,index);attache.setStrideMulti(str);
// attache*=0.01;
// }
// lin_it++;
// F.correctArgument(p);
// residual = F.value(evalError);
// if (evalError)
// cerr<<"p or S out of range in line search"<<endl;
// else
// FnNew = nrm2(residual);
// //cout<<FnNew<<endl;
// }
// FnOld = FnNew;
// #ifndef USE_BLAS
// correction-=p;
// #else
// axpy(-1.0,p,correction);
// #endif
// //cerr<<lin_it<<" line searches"<<endl;
// if (lin_it == 20)
// {
// F.unCorrect();
// cerr<<"Max # of line searches exceeded"<<endl;
// return true;
// }
// }
// }
data->nonlinearSolverIteration();
++iterations;
if (*convergenceFactorp !=1.0 && CHORD_ITERATION)
{
if(!SOLVE_SUB)
residual*=(*convergenceFactorp);
else
{
attache.attachToVecMulti(Vec::REF,residual,index);attache.setStrideMulti(str);
attache*=(*convergenceFactorp);
}
}
lsFailure=linearSolver->solve(residual,p);
if (lsFailure)
{
// cerr<<"Linear Solver solve failure in Newton iteration"<<endl;
data->linearSolverFailure();
return lsFailure;
}
F.correctArgument(p);
normOfOldCorrection=normOfCorrection;
normOfCorrection=(*weightedNorm)(p);
// //testing different method ---- REPLACE with normOfCorrection=(*weightedNorm)(p);
// //hack
// if(!SOLVE_SUB)
// normOfCorrection=(*weightedNorm)(p);
// else
// {
// real sum=0.0;
// const Vec& scaling(weightedNorm->getScaling());
// for (int i=0;i<scaling.ldim()/2;i++)
// sum+=scaling[str*i]*p[str*i]*scaling[str*i]*p[str*i];
// normOfCorrection=sqrt(sum);
// }
#ifndef USE_BLAS
correction-=p;
#else
axpy(-1.0,p,correction);
#endif
//decide whether to exit solver
if (normOfCorrection <= roundOffTolerance )
{
return false;
}
computeRate();
if ( TEST_RATE && rate > 0.9 )
{
// cerr<<"nonlinear solver failed due to poor convergence rate "<<rate<<endl;
return true;//convergence is too slow return failure
}
else if ( converged(F) )
{
return false;
}
}
// cerr<<"nonlinear solver exceeded max iterations "<<iterations<<endl;
return true;
}
//mwf===============================================
//probably don't really need this, but had some changes for ECDM
//==================================================
// cek stuff I cut out of the public archive's version of ModifiedNewton
///////////////////////////// start line search stuff
//mwf now put these in base class?
bool ModifiedNewtonMM::lineSearch(Vec& yp,Vec& pp,Vec& Fp,Vec& Fnew,
bool& evalFailed,
Vec& corr,
VectorFunction& F)
{
bool LSFailed(false);
switch (lineSearchMethod)
{
case cubicLS:
{
LSFailed = cubicLineSearch(yp,pp,Fp,Fnew,evalFailed,corr,F);
break;
}
case armijoLS:
{
LSFailed = armijoLineSearch(yp,pp,Fp,Fnew,evalFailed,corr,F);
break;
}
case simpleLS:
{
LSFailed = simpleLineSearch(yp,pp,Fp,Fnew,evalFailed,corr,F);
break;
}
default:
{
LSFailed = poorMansLineSearch(yp,pp,Fp,Fnew,evalFailed,corr,F);
break;
}
}
return LSFailed;
}
//mwf try to use num rec. line search
bool ModifiedNewtonMM::cubicLineSearch(Vec& yp,Vec& pp,Vec& Fp,Vec& Fnew,
bool& evalFailed,
Vec& corr,
VectorFunction& F)
{
#ifndef DEBUG
#define DEBUG
#define DEBUG_LOCAL
#endif
/////line search stuff
//already comes in with full Newton step attempted
#ifdef DEBUG
cout<<"entering cubic line search, evalFailed = "<<evalFailed<<endl;
cerr<<"entering cubic line search, evalFailed = "<<evalFailed<<endl;
#endif
if (USE_LINE_SEARCH)
{
//mwf try and force first step to take line search for now?
//Fp is F(yp)
//go ahead and try to scale step?
// real stepMax = 1.0;
real normCorrection = nrm2(pp);
pp *=1.0/normCorrection;
real yTol=1.0e-7;
real alpha=1.0e-4;
//slope is grad(F*F/2)*p but should be -Fp*Fp
real FnOld = 0.5*dot(Fp,Fp);
real slope = -2.0*FnOld;
//compute minimum step factor
real test(0.0),temp(0.0);
int nn = pp.size();
for (int i=0; i < nn; i++)
{
temp = fabs(pp(i))/std::max(fabs(yp(i)),1.0);
if (temp > test)
test = temp;
}
real lamin = yTol/test;
real lam = 1.0;
#ifdef DEBUG
// cout <<"in line search, lamin="<<lamin<<endl;
// cerr <<"in line search, lamin="<<lamin<<endl;
#endif
//temporaries for line search
int lin_it=0;
real tmplam(0.0),a(0.0),b(0.0),rhs1(0.0),rhs2(0.0);
real FnOld2(0.0),FnNew2(0.0),lam2(0.0),disc(0.0);
//mwf wastes an evaluation but lets me know if
real FnNew= 0.5*dot(Fnew,Fnew);
#ifdef DEBUG
cout<<"entering cubic line search, FnOld="<<FnOld<<endl;
cerr<<"entering cubic line search, FnOld="<<FnOld<<endl;
cout<<"entering cubic line search, FnNew="<<FnNew<<endl;
cerr<<"entering cubic line search, FnNew="<<FnNew<<endl;
#endif
while (lin_it < nLineSearches && lam >= lamin &&
(FnNew > FnOld + alpha*lam*slope || evalFailed ))
{
//should I uncorrect argument before I start
//new line search?
F.unCorrect();
if (lam >= 1.0)
{
//first step
tmplam= -slope/(2.0*(FnNew-FnOld-slope));
}
else
{
rhs1 = FnNew-FnOld-lam*slope;
rhs2 = FnNew2-FnOld2-lam2*slope;
a=(rhs1/(lam*lam)-rhs2/(lam2*lam2))/(lam-lam2);
b=(-lam2*rhs1/(lam*lam)+lam*rhs2/(lam2*lam2))/(lam-lam2);
if (a==0.0)
{
tmplam=-slope/(2.0*b);
}
else
{
disc=b*b-3.0*a*slope;
if (disc < 0.0)
{
cerr<<"roundoff problem in num rec. line search"<<endl;
return 1;
}
else
{
tmplam=(-b+sqrt(disc))/(3.0*a);
}
}
if (tmplam > 0.5*lam)
tmplam = 0.5*lam;
}//end portion for steps after first
lam2 =lam;
FnNew2=FnNew;
//should I recalculate FnOld?
//is FnOld2 to be set to FnNew or something?
FnOld2=FnOld;
#ifdef DEBUG
cout <<"in cubic line search, tmplam="<<tmplam<<endl;
cerr <<"in cubic line search, tmplam="<<tmplam<<endl;
#endif
//mwf try something more aggressive?
//mwf changed to 1.0e-3 from 1.0e-1
lam=max(tmplam,1.0e-1*lam);
#ifdef DEBUG
cout <<"in cubic line search, lam="<<lam<<endl;
cerr <<"in cubic line search, lam="<<lam<<endl;
#endif
pp*=lam;
F.correctArgument(pp);
#ifndef USE_BLAS
corr-=pp;
#else
axpy(-1.0,pp,corr);
#endif
Fnew = F.value(evalFailed);
if (!evalFailed)
{
FnNew = 0.5*dot(Fnew,Fnew);
#ifdef DEBUG
cout<<"in cubic line search, FnNew="<<FnNew<<endl;
cerr<<"in cubic line search, FnNew="<<FnNew<<endl;
#endif
cerr<<"+"<<endl;
}
else
{
//mwf what to do if evaluation fails?
//mwf want to reject new iterate
cerr<<"evalFailed in line search"<<endl;
cerr<<"-"<<endl;
}
data->lineSearch();
lin_it++;
} // end while
//Fp is residual in solve
Fp = Fnew;
if (lin_it == nLineSearches)
{
cerr <<"line search reached max"<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
//still try another newton iteration?
return true;
}
if (lam <= lamin)
{
cerr <<"line search lambda = "<<lam<<" below min= "
<<lamin<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
return true;
}
}//end if for line search
////// end line search stuff
#ifdef DEBUG
cout<<"successfully completed cubic line search?"<<endl;
cerr<<"successfully completed cubic line search?"<<endl;
#endif
#ifdef DEBUG_LOCAL
#undef DEBUG
#endif
return false;
}
//mwf try to use poor man's line search again
bool ModifiedNewtonMM::simpleLineSearch(Vec& yp,Vec& pp,Vec& Fp,Vec& Fnew,
bool& evalFailed,
Vec& corr,
VectorFunction& F)
{
if (USE_LINE_SEARCH)
{
real FnOld = nrm2(Fp);
real FnNew = FnOld;
if (evalFailed)
cerr<<"p or S out of range in Newton Iteration simpLS"<<endl;
else
FnNew = nrm2(Fnew);
std::cout<<" in simpLS "
<<" FnNew = "<<FnNew<<" FnOld= "<<FnOld<<std::endl;
int lin_it=0;
if ((FnNew > FnOld) || evalFailed)
{
#ifndef USE_BLAS
corr+=pp;
#else
axpy(1.0,p,corr);
#endif
while (((FnNew > FnOld) || evalFailed) && lin_it < nLineSearches)
{
F.unCorrect();
data->lineSearch();
//only apply linesearch to subsystem correction
if(!SOLVE_SUB)
pp*=lsRedFact;
else
{
attache.attachToVecMulti(Vec::REF,pp,index);attache.setStrideMulti(str);
attache*=lsRedFact;
}
lin_it++;
F.correctArgument(pp);
residual = F.value(evalFailed);
if (evalFailed)
cerr<<"p or S out of range in line search"<<endl;
else
FnNew = nrm2(residual);
//cout<<FnNew<<endl;
}//end while (FnNew > FnOld)
FnOld = FnNew;
#ifndef USE_BLAS
corr-=pp;
#else
axpy(-1.0,pp,corr);
#endif
std::cout<<lin_it<<" line searches"<<std::endl;
if (lin_it == nLineSearches)
{
F.unCorrect();
//mwf reset boundary conditions based on FnOld
residual = F.value(evalFailed);
cerr<<"Max # of line searches exceeded"
<<" last FnNew= "<<FnNew<<endl;
return true;
}
} //end original if (FnNew > FnOld)
}// end USE_LINE_SEARCH
return false;
}
//mwf try to use poor man's line search again
bool ModifiedNewtonMM::poorMansLineSearch(Vec& yp,Vec& pp,Vec& Fp,Vec& Fnew,
bool& evalFailed,
Vec& corr,
VectorFunction& F)
{
bool localEvalError(false);
/////line search stuff
//mwf turn on debugging?
#ifndef DEBUG
#define DEBUG
#define DEBUG_LOCAL
#endif
//already comes in with full Newton step attempted
#ifdef DEBUG
cout<<"entering poor man's line search, evalFailed = "<<evalFailed<<endl;
cerr<<"entering poor man's line search, evalFailed = "<<evalFailed<<endl;
#endif
if (USE_LINE_SEARCH)
{
real alpha=1.0e-4;
real lamin=1.0e-12;
//real lamax=0.5;
real FnOld = norm(Fp);
real lam = 1.0;
//temporaries for line search
int lin_it=0;
//mwf wastes an evaluation but lets me know
Fnew = F.value(localEvalError);
real FnNew(100.0*FnOld);
if (!evalFailed)
FnNew= norm(Fnew);
#ifdef DEBUG
cout<<"entering po line search, localEvalError = "<<localEvalError<<endl;
cout<<"entering po line search, evalFailed = "<<evalFailed<<endl;
cerr<<"entering po line search, localEvalError = "<<localEvalError<<endl;
cerr<<"entering po line search, evalFailed = "<<evalFailed<<endl;
cout<<"entering po line search, FnOld="<<FnOld<<endl;
cerr<<"entering po line search, FnOld="<<FnOld<<endl;
cout<<"entering po line search, FnNew="<<FnNew<<endl;
cerr<<"entering po line search, FnNew="<<FnNew<<endl;
#endif
while (lin_it < nLineSearches && lam >= lamin &&
(FnNew >= (1.0-alpha*lam)*FnOld || evalFailed) )
{
//uncorrect argument before I start
//new line search
F.unCorrect();
//mwf should I check that FnOld is same as value now
#ifdef DEBUG
//cout<<"after uncorrecting norm(F.value())= "
// <<norm(F.value(localEvalError))<<endl;
#endif
lam = lsRedFact*lam;
#ifdef DEBUG
//cout <<"in line search, lam="<<lam<<endl;
//cerr <<"in line search, lam="<<lam<<endl;
#endif
pp*=lam;
F.correctArgument(pp);
//mwf do I still need this?
#ifndef USE_BLAS
corr-=pp;
#else
axpy(-1.0,pp,corr);
#endif
Fnew = F.value(evalFailed);
if (!evalFailed)
{
FnNew = norm(Fnew);
#ifdef DEBUG
//cout<<"in line search, FnNew="<<FnNew<<endl;
//cerr<<"in line search, FnNew="<<FnNew<<endl;
#endif
cerr<<"+"<<endl;
}
else
{
//mwf what to do if evaluation fails?
//mwf want to reject new iterate
//FnNew = 100.0*FnOld;
cerr<<"evalFailed in line search"<<endl;
cerr<<"-"<<endl;
}
#ifdef DEBUG
//cerr <<"suff decrease = "<<FnNew - (1.0-alpha*lam)*FnOld<<endl;
//cout <<"suff decrease = "<<FnNew - (1.0-alpha*lam)*FnOld<<endl;
#endif
data->lineSearch();
lin_it++;
} // end while
//Fp is residual in solve
Fp = Fnew;
if (lin_it == nLineSearches)
{
cerr <<"line search reached max"<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
return true;
}
if (lam <= lamin)
{
cerr <<"line search lambda = "<<lam<<" below min= "
<<lamin<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
return true;
}
}//end if for line search
////// end line search stuff
#ifdef DEBUG
//cerr <<"leaving poor man's line search "<<endl;
//cout <<"leaving poor man's line search "<<endl;
#endif
#ifdef DEBUG_LOCAL
#undef DEBUG
#endif
return false;
}
//mwf try to armijo line search
bool ModifiedNewtonMM::armijoLineSearch(Vec& yp,Vec& pp,Vec& Fp,Vec& Fnew,
bool& evalFailed,
Vec& corr,
VectorFunction& F)
{
// bool localEvalError(false);
/////line search stuff
//mwf turn on debugging?
#ifndef DEBUG
#define DEBUG
#define DEBUG_LOCAL
#endif
//already comes in with full Newton step attempted
#ifdef DEBUG
cout<<"entering armijo routine, evalFailed = "<<evalFailed<<endl;
cerr<<"entering armijo routine, evalFailed = "<<evalFailed<<endl;
#endif
if (USE_LINE_SEARCH)
{
//
real suffDecrParam=1.0e-4;
//
real initialStepSize=1.0;
//
real scaleFactor=lsRedFact*initialStepSize;
real scaleMin=1.0e-12;
int lin_it=0;
//original Newton Step?
const Vec origStep(pp);
//now minimize 0.5*F^{T}*F
real FnOld = 0.5*dot(Fp,Fp);
//mwf wastes an evaluation but lets me know if
real FnNew= 0.5*dot(Fnew,Fnew);
#ifdef DEBUG
//cout<<"entering line search, FnOld="<<FnOld<<endl;
cerr<<"entering line search, FnOld="<<FnOld<<endl;
//cout<<"entering line search, FnNew="<<FnNew<<endl;
cerr<<"entering line search, FnNew="<<FnNew<<endl;
#endif
while (lin_it < nLineSearches && scaleFactor >= scaleMin &&
(FnNew >= (1.0-2.0*suffDecrParam*scaleFactor)*FnOld
|| evalFailed ))
{
//uncorrect argument before I start
//new line search
F.unCorrect();
//mwf should I check that FnOld is same as value now
#ifdef DEBUG
// cout<<"after uncorrecting norm(F.value())= "
// <<norm(F.value(localEvalError))<<endl;
#endif
scaleFactor *= lsRedFact;
#ifdef DEBUG
// cout <<"in line search, scaleFactor="<<scaleFactor<<endl;
// cerr <<"in line search, scaleFactor="<<scaleFactor<<endl;
#endif
#ifdef DEBUG
// cout <<"in line search, scaleFactor="<<scaleFactor<<endl;
// cerr <<"in line search, scaleFactor="<<scaleFactor<<endl;
#endif
pp = origStep;
pp*= scaleFactor;
F.correctArgument(pp);
#ifndef USE_BLAS
corr-=pp;
#else
axpy(-1.0,pp,corr);
#endif
Fnew = F.value(evalFailed);
if (!evalFailed)
{
FnNew = 0.5*dot(Fnew,Fnew);
#ifdef DEBUG
//cout<<"in line search, FnNew="<<FnNew<<endl;
cerr<<"in line search, FnNew="<<FnNew<<endl;
#endif
cerr<<"+"<<endl;
}
else
{
//mwf what to do if evaluation fails?
//mwf want to reject new iterate
FnNew = 100.0*FnOld;
cerr<<"evalFailed in armijo line search"<<endl;
cerr<<"-"<<endl;
}
data->lineSearch();
lin_it++;
} // end while
//Fp is residual in solve
Fp = Fnew;
if (lin_it == nLineSearches)
{
cerr <<"line search reached max"<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
return true;
}
if (scaleFactor <= scaleMin)
{
cerr <<"line search scaleFactor = "<<scaleFactor<<" below min= "
<<scaleMin<<endl;
cerr <<"norm of correction= "<<nrm2(corr)<<endl;
return true;
}
}//end if for line search
////// end line search stuff
#ifdef DEBUG
//cerr <<"leaving armijo line search "<<endl;
//cout <<"leaving armijo line search "<<endl;
#endif
return false;
}
ModifiedNewtonMM::ModifiedNewtonMM() : ModifiedNewton()
{}
ModifiedNewtonMM::ModifiedNewtonMM(LinearSolver& linearSolverIn, VectorNorm& W,
DataCollector& dataIn, int neq,
real lTol,real nlTol, int maxit):
ModifiedNewton(linearSolverIn,W,
dataIn,neq,lTol,nlTol,maxit),
attache2(),attache3(),resForLSolve(),pForLSolve()
{}
ModifiedNewtonMM::ModifiedNewtonMM(LinearSolver& linearSolverIn, VectorNorm& W,
DataCollector& dataIn, int neq,
VectorNorm& Wlin,
real lTol,real nlTol, int maxit):
ModifiedNewton(linearSolverIn,W,
dataIn,neq,lTol,nlTol,maxit),
attache2(),attache3(),resForLSolve(),pForLSolve(),
weightedNormLinSys(&Wlin)
{}
ModifiedNewtonMM::~ModifiedNewtonMM()
{}
void ModifiedNewtonMM::solveSubSystem(int start,int end,int stride,
int dimLS)
{
SOLVE_SUB=true; VecIndex i(start,end); index=i; str=stride;
if (dimLS > 0)
{
resForLSolve.newsize(dimLS);
pForLSolve.newsize(dimLS);
}
}
//mwf try a different way to switch out right hand side for subsystems
bool ModifiedNewtonMM::solve(Vec& correction,VectorFunction& F)
{
//cout<<"in newton solve"<<endl<<flush;
//This function will not exit in one iteration if RECOMPUTE_RATE is true, unlike DASPK's nonlinear
//solver which forces the rate to be recomputed by setting it (the inverse rate) to a large value (100).
//It is sometimes the case that even with s=100 the first correction is small enough to give convergence
//while still not being below roundoff. This causes a slight difference in the way the two codes run.
//mwf try and get vectors for linear solves
//mwf index may not be set for normal system?
bool lsFailure,evalError=false;
residual=F.value(evalError);
if (evalError)
{
cerr<<"Predictor caused evaluation error; nonlinear solver returning failure"<<endl
<<"The calling routine should have caught this"<<endl;
return evalError;
}
if (TEST_RESIDUAL)
r0 = nrm2(residual);
iterations=0;
correction=0.0;
//mwf now see if I need to set weighted norm?
Vec dummy = F.argument();
weightedNorm->setWeight(dummy);
normOfPredictor=(*weightedNorm)(F.argument());
if (!INEXACT_LINEAR_SOLVER)