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FLCBDF.cpp
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#include "FLCBDF.h"
namespace Daetk
{
using std::min;
using std::max;
using std::cerr;
using std::endl;
using std::cout;
void FLCBDF::useFixedStep(const real step)
{
USE_FIXED_STEP=true;
hFixed=step;
}
void FLCBDF::useFixedOrder(const int order)
{
USE_FIXED_ORDER=true;
kFixed=order;
}
void FLCBDF::updateJacobians(){UPDATE_JACOBIANS=true;}
void FLCBDF::useInterpolant(){STEP_EXACT=false;}
FLCBDF::FLCBDF():
prepareJacobian(true),
predictorAlreadyUpdated(false),
firstCallToSolver(true),
jacobianIsOld(true),
correctorStepFailed(false),
USE_FIXED_STEP(false),
USE_FIXED_ORDER(false),
UPDATE_JACOBIANS(false),
STEP_EXACT(true),
neq(0),
k(1),
kLast(0),
constStepsTaken(0),
stepsFailed(0),
MAX_FAILURES(11),
shortCutFactor(1),
kFixed(1),
h(0.0),
hLast(-1.0),
tn(0.0),
tnPlusH(0.0),
errorEstimate(1.0),
norm_yCminusyP(1.0),
convergenceFactor(1.0),
hFixed(0.0),
alphaS(0.0),
alphaoNp1(0.0),
alphaOld(0.0),
alphaLast(0.0),
startupType(0),
stepIncreaseCeilingStartup(2.0),
stepIncreaseFloorStartup(2.0),
stepDecreaseCeilingStartup(0.9),
stepDecreaseFloorStartup(0.5),
forceRichardsonExtrapolationOrder(2),
chooseStepSizeFlag(0),
stepIncreaseCeiling(2.0),
stepIncreaseFloor(2.0),
stepDecreaseCeiling(0.9),
stepDecreaseFloor(0.5),
dae(0),
data(0),
linearSolver(0),
nonlinearSolver(0),
jacobian(0),
weightedNorm(0),
stepRunTime(0)
{
//default constructor is called for all vectors
Tracer tr("FLCBDF::FLCBDF()");
int i;
for (i=0;i<7;i++)
{
sigmaNp1[i] = 1.0;
psiNp1[i]=1.0;
}
for (i=0;i<6;i++)
{
alphaNp1[i]=0.0;
betaNp1[i]=1.0;
gammaNp1[i]=0.0;
}
alphaNp1[0]=1.0;
}
FLCBDF::FLCBDF(DaeDefinition& daeIn,LinearSolver& linearSolverIn,
NonlinearSolver& nonlinearSolverIn,
Jacobian& jacobianIn,VectorNorm& norm,
DataCollector& dataIn):
prepareJacobian(true),
predictorAlreadyUpdated(false),
firstCallToSolver(true),
jacobianIsOld(true),
correctorStepFailed(false),
USE_FIXED_STEP(false),
USE_FIXED_ORDER(false),
UPDATE_JACOBIANS(false),
STEP_EXACT(true),
neq(daeIn.getY0().dim()),
k(1),
kLast(0),
constStepsTaken(0),
stepsFailed(0),
MAX_FAILURES(11),
shortCutFactor(1),
kFixed(1),
h(0.0),
hLast(-1.0),
tn(0.0),
tnPlusH(0.0),
errorEstimate(1.0),
norm_yCminusyP(1.0),
convergenceFactor(1.0),
hFixed(0.0),
yn(0),
ynprime(0),
yCminusyP(daeIn.getY0().dim(),0.0),
tempvec(daeIn.getY0().dim(),0.0),
alphaS(0.0),
alphaoNp1(0.0),
alphaOld(0.0),
alphaLast(0.0),
startupType(0),
stepIncreaseCeilingStartup(2.0),
stepIncreaseFloorStartup(2.0),
stepDecreaseCeilingStartup(0.9),
stepDecreaseFloorStartup(0.5),
forceRichardsonExtrapolationOrder(2),
chooseStepSizeFlag(0),
stepIncreaseCeiling(2.0),
stepIncreaseFloor(2.0),
stepDecreaseCeiling(0.9),
stepDecreaseFloor(0.5),
norm_yByS(1.0),
tmp(daeIn.getY0().dim(),0.0),
yBigStep(daeIn.getY0().dim(),0.0),
ySmallStep(daeIn.getY0().dim(),0.0),
yExt(daeIn.getY0().dim(),0.0),
ySminusyB(daeIn.getY0().dim(),0.0),
dae(&daeIn),
data(&dataIn),
linearSolver(&linearSolverIn),
nonlinearSolver(&nonlinearSolverIn),
jacobian(&jacobianIn),
weightedNorm(&norm),
stepRunTime(0.0)
{
Tracer tr("FLCBDF::FLCBDF(DaeDefinition& daeIn,NonlinearSolver& nonlinearSolverIn,BDFJacobian& jacobianIn,VectorNorm& norm,Data& dataIn)");
//finish allocating vectors and initialize history
int i;
for (i=0;i<7;i++)
{
// divdifN[i].newsize(daeIn.getY0().dim());
// divdifN[i] = 0.0;
// divdifNm1[i].newsize(daeIn.getY0().dim());
// divdifNm1[i] = 0.0;
phiN[i].newsize(daeIn.getY0().dim());
phiN[i] = 0.0;
psiNp1[i]=1.0;
sigmaNp1[i]=1.0;
}
for (i=0;i<6;i++)
{
alphaNp1[i]=0.0;
betaNp1[i]=1.0;
gammaNp1[i]=0.0;
}
alphaNp1[0]=1.0;
}
FLCBDF::~FLCBDF()
{
Tracer tr("FLCBDF::~FLCBDF()");
}
bool FLCBDF::takeInitialSteps(const real& tout,real& tStep,Vec&
solutionAtTStep, Vec& sp)
{
//now this is a wrapper routine for calling different alternatives
switch (startupType)
{
case 1:
return takeInitialStepsWithModifiedCoefs(tout,tStep,solutionAtTStep,sp);
case 2:
return takeInitialStepsWithRichExtrap(tout,tStep,solutionAtTStep,sp);
case 0:
return takeInitialStepsOriginal(tout,tStep,solutionAtTStep,sp);
default:
return takeInitialStepsOriginal(tout,tStep,solutionAtTStep,sp);
}
}
bool FLCBDF::takeInitialStepsOriginal(const real& tout,real& tStep,Vec&
solutionAtTStep, Vec& sp)
{
clock.reset();
clock.start();
yn = &solutionAtTStep;
ynprime = &sp;
tn=dae->getT0();
#ifndef USE_BLAS
*yn=dae->getY0();
#else
copy(dae->getY0(),*yn);
#endif
#ifndef USE_BLAS
*ynprime=dae->getY0prime();
#else
copy(dae->getY0prime(),*ynprime);
#endif
weightedNorm->setWeight(*yn);
//conservative estimate of first step
if ((*weightedNorm)(*ynprime) > 0)
h=min((1e-3)*fabs(tout-tn),0.5/(*weightedNorm)(*ynprime));
else
h=(1e-3)*fabs(tout-tn);
h=copysign(h,tout-tn);
alphaOld = 1e-20; //forces jacobian evaluation on first step
if (USE_FIXED_STEP && hFixed < h)
h=hFixed;
/* Try to run the order up to 5 and double the step size at each
step. Exit the function and start using normal error control if the
error estimates look particularly bad.
*/
int i;
int maxOrd=5;
if (USE_FIXED_ORDER)
maxOrd=kFixed;
for (i=1;i<maxOrd+1;i++)
{
errorFailures=0;
while ( stepsFailed <= MAX_FAILURES )
{
if (i==1)
{
constStepsTaken = 0;
/* Alter the history directly in order to take the first
step. Form a history that reflects a step of h*y0prime from a
previous value of yn - h*ynprime to get to the current value,
yn=y0; In this way the predictor corrector framework is used to take
a backward Euler step.
*/
//update scalar coefficients with stepsize h for the "pseudo-step"
updateCoefficients();
hLast=0;
kLast=1;
k=1;
updateCoefficients();
//alter the history array directly on the first step.
#ifndef USE_BLAS
phiN[0] = dae->getY0();
#else
copy(dae->getY0(),phiN[0]);
#endif
#ifndef USE_BLAS
phiN[1] = h*(dae->getY0prime());
#else
copy(dae->getY0prime(),phiN[1]);
scal(h,phiN[1]);
#endif
phiN[2] = 0.0;
}
else
updateCoefficients();
predictor();
//cout<<"in first call to corrector"<<endl<<flush;
correctorStepFailed=corrector();
if (correctorStepFailed)
{
cerr<<"corrector Failed"<<endl;
stepsFailed+=1;
data->nonlinearSolverFailure();
h*=.25;
if (stepsFailed > 2)
k=1;
checkStepSize();
resetCoefficients();
}
else if (errorForStepTooLarge())
{
stepsFailed+=1;
data->errorFailure();
chooseOrderForStep();
if (errorFailures==1)
{
h*=max( min(0.9,0.9*errorEstimate), 0.25);
}
else if (errorFailures==2)
{
h*=.25;
}
else
{
k=1;
h*=.25;
}
checkStepSize();
resetCoefficients();
}
else
{
tn += h;
tStep = tn;
hLast = h;
kLast = k;
weightedNorm->setWeight(*yn);
//mwf from seg?
stepRunTime=clock.elapsed();
data->stepTaken(k,h,tn,error,stepRunTime,0.0);
dae->stepTaken();
predictorAlreadyUpdated=false;
chooseOrderForStep();
if ( k < i )
{
//exit initial phase succesfully but k < maxord
chooseStepSizeOriginal(tn,tout);
return 0;
}
else if (tStep >= tout)//relies on direction--fix
return 0;
else
{
//take another intial phase step
stepsFailed = 0;
if (k!=5)
{
if (USE_FIXED_STEP)
chooseStepSizeOriginal(tn,tout);
else
h*=2.0;
checkStepSize();
if (USE_FIXED_ORDER)
{
if (k < kFixed )
k++;
}
else
k++;
}
break;
}
}
if (stepsFailed >= MAX_FAILURES)
{
data->stepTaken(k,h,tn,error);
return 1;
}
}
}
/* Exited the loop successfully with k=5. Reset h to it's previous
value and choose the stepsize according to the error estimates. */
chooseStepSizeOriginal(tn,tout);
return 0;
}
bool FLCBDF::takeInitialStepsWithModifiedCoefs(const real& tout,real& tStep,Vec&
solutionAtTStep, Vec& sp)
{
clock.reset();
clock.start();
yn = &solutionAtTStep;
ynprime = &sp;
tn=dae->getT0();
#ifndef USE_BLAS
*yn=dae->getY0();
#else
copy(dae->getY0(),*yn);
#endif
#ifndef USE_BLAS
*ynprime=dae->getY0prime();
#else
copy(dae->getY0prime(),*ynprime);
#endif
weightedNorm->setWeight(*yn);
//conservative estimate of first step
if ((*weightedNorm)(*ynprime) > 0)
h=min((1e-3)*fabs(tout-tn),0.5/(*weightedNorm)(*ynprime));
else
h=(1e-3)*fabs(tout-tn);
h=copysign(h,tout-tn);
alphaOld = 1e-20; //forces jacobian evaluation on first step
if (USE_FIXED_STEP && hFixed < h)
h=hFixed;
/* Try to run the order up to 5 and double the step size at each
step. Exit the function and start using normal error control if the
error estimates look particularly bad.
*/
int i;
int maxOrd=5;
if (USE_FIXED_ORDER)
maxOrd=kFixed;
for (i=1;i<maxOrd+1;i++)
{
errorFailures=0;
while ( stepsFailed <= MAX_FAILURES )
{
if (i==1)
{
constStepsTaken = 0;
/* Alter the history directly in order to take the first
step. Form a history that reflects a step of h*y0prime from a
previous value of yn - h*ynprime to get to the current value,
yn=y0; In this way the predictor corrector framework is used to take
a backward Euler step.
*/
//update scalar coefficients with stepsize h for the "pseudo-step"
updateCoefficients();
hLast=0;
kLast=1;
k=1;
updateCoefficients();
//alter the history array directly on the first step.
#ifndef USE_BLAS
phiN[0] = dae->getY0();
#else
copy(dae->getY0(),phiN[0]);
#endif
#ifndef USE_BLAS
phiN[1] = h*(dae->getY0prime());
#else
copy(dae->getY0prime(),phiN[1]);
scal(h,phiN[1]);
#endif
phiN[2] = 0.0;
}
else
updateCoefficients();
predictor();
//cout<<"in first call to corrector"<<endl<<flush;
correctorStepFailed=corrector();
if (correctorStepFailed)
{
cerr<<"corrector Failed"<<endl;
stepsFailed+=1;
data->nonlinearSolverFailure();
h*=.25;
if (stepsFailed > 2)
k=1;
checkStepSize();
resetCoefficients();
}
else if (errorForStepTooLarge())
{
stepsFailed+=1;
data->errorFailure();
chooseOrderForStep();
if (errorFailures==1)
{
h*=max( min(0.9,0.9*errorEstimate), 0.25);
}
else if (errorFailures==2)
{
h*=.25;
}
else
{
k=1;
h*=.25;
}
checkStepSize();
resetCoefficients();
}
else
{
tn += h;
tStep = tn;
hLast = h;
kLast = k;
weightedNorm->setWeight(*yn);
//mwf from seg?
stepRunTime=clock.elapsed();
data->stepTaken(k,h,tn,error,stepRunTime,0.0);
//data->stepTaken(k,h,tn,error);
dae->stepTaken();
predictorAlreadyUpdated=false;
chooseOrderForStep();
if ( k < i )
{
//exit initial phase succesfully but k < maxord
chooseStepSizeStartup(tn,tout);
return 0;
}
else if (tStep >= tout)//relies on direction--fix
return 0;
else
{
//take another intial phase step
stepsFailed = 0;
if (k!=5)
{
chooseStepSizeStartup(tn,tout);
checkStepSize();
if (USE_FIXED_ORDER)
{
if (k < kFixed )
k++;
}
else
k++;
}
break;
}
}
if (stepsFailed >= MAX_FAILURES)
{
data->stepTaken(k,h,tn,error);
return 1;
}
}
}
/* Exited the loop successfully with k=5. Reset h to it's previous
value and choose the stepsize according to the error estimates. */
chooseStepSizeStartup(tn,tout);
return 0;
}
bool FLCBDF::takeInitialStepsWithRichExtrap(const real& tout,real& tStep,Vec&
solutionAtTStep, Vec& sp)
{
clock.reset();
clock.start();
yn = &solutionAtTStep;
ynprime = &sp;
tn=dae->getT0();
#ifndef USE_BLAS
*yn=dae->getY0();
#else
copy(dae->getY0(),*yn);
#endif
#ifndef USE_BLAS
*ynprime=dae->getY0prime();
#else
copy(dae->getY0prime(),*ynprime);
#endif
weightedNorm->setWeight(*yn);
//conservative estimate of first step
if ((*weightedNorm)(*ynprime) > 0)
h=min((1e-3)*fabs(tout-tn),0.5/(*weightedNorm)(*ynprime));
else
h=(1e-3)*fabs(tout-tn);
h=copysign(h,tout-tn);
alphaOld = 1e-20; //forces jacobian evaluation on first step
if (USE_FIXED_STEP && hFixed < h)
h=hFixed;
/* Try to run the order up to 5 and double the step size at each
step. Exit the function and start using normal error control if the
error estimates look particularly bad.
*/
int i;
int maxOrd=2;//mwf check out this choice, whether or not used unless USE_FIXED_ORDER
int stepsStabilized = 0;
bool useExtrapolatedSolutionAsSolution = false;
int minStableRichExtrapSteps = 10;
real richNLfailureFactor = 0.25;
if (USE_FIXED_ORDER)
maxOrd=kFixed;
bool done = false;
int iTimeStep = 0; //number of global time steps taken so far
while (!done)
{
iTimeStep++;
errorFailures = 0;
stepsFailed = 0; //this means corrector failures for now
cout<<"RichExtrap starting step number "<<iTimeStep<<" errorFailures = "<<errorFailures<<endl;
bool richExtrapStepDone = false;
while (stepsFailed <= MAX_FAILURES && !richExtrapStepDone) //failure if any one of the
//Richardson Extrapolation
//steps fails
{
//loop through 1 whole step and 2 half steps
// starting with the whole step when ii==1
// continuing for two half steps ii==2, ii==3
for (int iiRichExtrapPhase = 1; iiRichExtrapPhase < 4; iiRichExtrapPhase++)
{
if (iiRichExtrapPhase == 2) h *= 0.5;
cout<<"\t starting phase "<<iiRichExtrapPhase<<" h= "<<h<<endl;
if (iTimeStep == 1 && iiRichExtrapPhase != 3)
{
constStepsTaken = 0;
/* Alter the history directly in order to take the first
step. Form a history that reflects a step of h*y0prime from a
previous value of yn - h*ynprime to get to the current value,
yn=y0; In this way the predictor corrector framework is used to take
a backward Euler step.
*/
//update scalar coefficients with stepsize h for the "pseudo-step"
updateCoefficients();
hLast=0;
kLast=1;
k=1;
updateCoefficients();
//alter the history array directly on the first step.
#ifndef USE_BLAS
phiN[0] = dae->getY0();
#else
copy(dae->getY0(),phiN[0]);
#endif
#ifndef USE_BLAS
phiN[1] = h*(dae->getY0prime());
#else
copy(dae->getY0prime(),phiN[1]);
scal(h,phiN[1]);
#endif
phiN[2] = 0.0;
}//starting new time step from scratch
else
updateCoefficients();
predictor();
correctorStepFailed=corrector();
if (correctorStepFailed)
{
stepsFailed+=1;
data->nonlinearSolverFailure();
//if first step try to cut back time step and redo
if (iiRichExtrapPhase == 1)
{
h *= richNLfailureFactor;
}
else //rerun with smaller whole step if failure occurs on a half step
{ //this won't go undo first half step though
h *= 2.0;
h *= richNLfailureFactor;
}
cerr<<"corrector Failed in Richardson Extrapolation phase h= "<<h<<endl;
checkStepSize();
resetCoefficients();
break;
}//corrector failed
else
{
if (iiRichExtrapPhase == 1)
{
copy(*yn,yBigStep);
resetCoefficients();
}
else if (iiRichExtrapPhase == 2)
{
//mwf put in step taken
tn += h;
tStep = tn;
hLast = h;
kLast = k;
weightedNorm->setWeight(*yn);
dae->stepTaken();
//seg put in CPU times
stepRunTime=clock.elapsed();
cout<<"time elapsed = "<<stepRunTime<<endl;
//mwf error and errorRichExt from last time step?
data->stepTaken(k,h,tn,error,stepRunTime,errorRichExt);
predictorAlreadyUpdated=false;
}
else
{
//seg put in CPU times
stepRunTime=clock.elapsed();
cout<<"time elapsed = "<<stepRunTime<<endl;
//mwf put in step taken
//weightedNorm->setWeight(*yn);
copy(*yn,ySmallStep);
int ldim=ySmallStep.ldim();
for (int jj=0;jj<ldim;jj++)
{
ySminusyB[jj]= fabs(ySmallStep[jj]-yBigStep[jj]);
}
norm_yByS=(*weightedNorm)(ySminusyB);
// extrapolate to more accurate solution for yn
if (useExtrapolatedSolutionAsSolution)
{
for (int jj=0;jj<ldim;jj++)
{
yExt[jj] = ySmallStep[jj] + 1.0/(pow(2.0,k)-1.0)*(ySminusyB[jj]);
}
//could make this different variable
norm_yByS *= 1.0/(pow(2.0,k)-1.0);
//if going to use extrapolated solution for evolution
copy(yExt,*yn);
}
richExtrapStepDone = true;
}//iiRichPhase
}//corrector did not fail
}//Richardson Extrapolation loop
}//step failure while
if (stepsFailed >= MAX_FAILURES)
{
//data->stepTaken(k,h,tn,error);
data->stepTaken(k,h,tn,error,stepRunTime,errorRichExt);
return 1;
}
//need to add check to see if Richardson step was ok error wise
//put in steps for FLC eror calculation here to determine its error and errork
bool flcbdfErrorTooLarge = errorForStepTooLarge();
//mwf Feb 17 2009 I believe this is error for next time step
real errork = sigmaNp1[k]*norm_yCminusyP;
//computed a full time step with Richardson Extrap
//h here is the small step
tn += h;
tStep = tn;
hLast = h;
kLast = k;
weightedNorm->setWeight(*yn);
// data->stepTaken(k,h,tn,error);
dae->stepTaken();
predictorAlreadyUpdated=false;
chooseOrderForStep();
cout<<"Richardson Extrap error norm = "<<norm_yByS<<", flcbdf errork= "<<errork<<" error= "<<error<<endl;
cout<<"RichExtrap step number "<<iTimeStep<<" took step to "<<tn<<" hLast= "<<hLast<<endl;
if (tStep >= tout - 1.0e-12)//relies on direction--fix
{
//data->stepTaken(k,h,tn,error);
done = true;
data->stepTaken(k,h,tn,error,stepRunTime,errorRichExt);
//mwf debug
std::cout<<"Leaving RichExtrap Init tStep= "<<tStep<<" tout= "<<tout<<std::endl;
return 0;
}
else //continue on, use classical error control to pick next step size
{
const real safety = 0.975;
const real richardsonExtrapCeiling = 5.0;
const real richardsonExtrapFloor = 0.2;
errorRichExt = norm_yByS/(pow(2.0,k)-1.0);
real fac = pow(1.0/errorRichExt,1.0/(k+1.0));
fac = std::min(fac,richardsonExtrapCeiling);
fac = std::max(fac,richardsonExtrapFloor);
if (fac<2.0 && fac > 0.9)
stepsStabilized++;
data->stepTaken(k,h,tn,error,stepRunTime,errorRichExt);
//put in steps for FLC eror calculation here to determine its error and errork
//mwf don't need this again? Feb 17 2009
//bool flcbdfErrorTooLarge = errorForStepTooLarge();
//mwf or this Feb 17 2009
//real errork = sigmaNp1[k]*norm_yCminusyP;
// chooseOrderForStep(); seg--don't think it's needed because calculating errork above
real errorRatio = errorRichExt/errork;
cout<<"Richardson Extrap error norm = "<<errorRichExt<<", flcbdf errork= "<<errork<<" error= "<<error<<"errorRatio = "<<errorRatio<<endl;
cout<<" fac stepStabilized= "<<fac<<", "<<stepsStabilized<<endl;
//decide if time to quit
if (stepsStabilized > minStableRichExtrapSteps && errorRatio > safety*2.0/k)
{
done = true;
}
else //not done, continue for
{
h *= 2.0*fac; //recall h is small step size coming in
if (forceRichardsonExtrapolationOrder > 0)
k = forceRichardsonExtrapolationOrder;
if (STEP_EXACT)
{
//mwf put this in to see if I can get it to just go to tout
if (tn+h > tout)
{
real htmp = fabs(tout-tn);
if (tn+htmp > tn)
h = htmp;
}
}
cout<<"Continuing Richardson Extrap step = "<<iTimeStep<<" order= "<<k<<" new h = "<<h<<std::endl;
checkStepSize();
}
}
}//startup phase end richards extrapolation phases
//seg -- take out chooseStepSize() call, want to send RE step choice to integrator first.
// otherwise, this reduces stepsize based on flcbdf error before step() is taken outside of start-up routine.
//chooseStepSize(tn,tout);
return 0;
}
bool FLCBDF::step(const real& tout,real& tStep,Vec& solutionAtTStep, Vec& stp)
{
//clock.start();
yn = &solutionAtTStep;
ynprime = &stp;
errorFailures=0;
stepsFailed=0;
while ( stepsFailed <= MAX_FAILURES )
{
updateCoefficients();
predictor();
correctorStepFailed=corrector();
if (correctorStepFailed)
{
cerr<<"corrector Failed"<<endl;
stepsFailed+=1;
data->nonlinearSolverFailure();
h*=.25;
if (stepsFailed>2)
k=1;
checkStepSize();
resetCoefficients();
}
else if (errorForStepTooLarge())
{
errorFailures++;
stepsFailed+=1;
data->errorFailure();
chooseOrderForStep();
if (errorFailures==1)
{
h*=max( min(0.9,0.9*errorEstimate), 0.25);
}
else if (errorFailures==2)
{
h*=.25;
}
else
{
k=1;
h*=.25;
}
checkStepSize();
resetCoefficients();
}
//step was accepted; exit function here
else
{
tn += h;
tStep = tn;
if (k>kLast)
kRaisedOnLastStep=true;
else
kRaisedOnLastStep=false;
hLast = h;
kLast = k;
//seg put in CPU times
stepRunTime=clock.elapsed();
cout<<"time elapsed = "<<stepRunTime<<endl;
data->stepTaken(k,h,tn,error,stepRunTime,0.0);
// data->stepTaken(k,h,tn,error);
dae->stepTaken();
weightedNorm->setWeight(*yn);
predictorAlreadyUpdated=false;
chooseOrderForStep();
chooseStepSize(tn,tout);
return 0;
}
}
data->stepTaken(k,h,tn);
return 1;//step failed
}
void FLCBDF::updateCoefficients()
{
register int i;
register real temp1(0),temp2(0);
//The following coefficients are constants throughout the integration:
//betaNp1[0]=1.0, sigmaNp1[0]=1.0, gammaNp1[0]=0.0, alphaNp1[0]=1.0;
if (k==kLast && h==hLast)
constStepsTaken+=1;
else
constStepsTaken = 0;
if (!predictorAlreadyUpdated)
{
if(kLast<5)
{
#ifndef USE_BLAS
phiN[kLast+1] = yCminusyP;
#else
copy(yCminusyP,phiN[kLast+1]);
#endif
}
#ifndef USE_BLAS
phiN[kLast] += yCminusyP;
#else
axpy(1.0,yCminusyP,phiN[kLast]);
#endif
for (i=kLast-1;i>=0;i--)
{
#ifndef USE_BLAS
phiN[i] += phiN[i+1];
#else
axpy(1.0,phiN[i+1],phiN[i]);
#endif
}
}
if (predictorAlreadyUpdated)
{
int i;
for (i=constStepsTakenLastUpdate+1;i<kLastUpdate+1;i++)
{
temp1 = 1.0/betaNp1[i];
#ifndef USE_BLAS
phiN[i]*=temp1;
#else
scal(temp1,phiN[i]);
#endif
}
if (kLastUpdate >= constStepsTakenLastUpdate)
for (i=1;i<kLastUpdate+1;i++)
psiNp1[i-1] = psiNp1[i] - hLastUpdate;
}
if (k >= constStepsTaken)
{
temp1 = h;
for (i=1;i<k+1;i++)
{
temp2 = psiNp1[i-1];
psiNp1[i-1] = temp1;
betaNp1[i] = betaNp1[i-1]*psiNp1[i-1]/temp2;
temp1 = temp2+h;
alphaNp1[i] = h/temp1;
sigmaNp1[i] = real(i)*sigmaNp1[i-1]*alphaNp1[i];
gammaNp1[i] = gammaNp1[i-1]+alphaNp1[i-1]/h;
}
psiNp1[k] = temp1;
}
hLastUpdate = h;
kLastUpdate = k;
constStepsTakenLastUpdate = constStepsTaken;
predictorAlreadyUpdated=false;
}
void FLCBDF::resetCoefficients()
{
predictorAlreadyUpdated=true;
}
void FLCBDF::predictor()
{
#ifndef USE_BLAS
*yn=phiN[0];
#else
copy(phiN[0],*yn);
#endif
*ynprime=0.0;
//recall betaNp1[0] = 1.0;
int i;
for (i=constStepsTaken+1;i<k+1;i++)
{
#ifndef USE_BLAS
phiN[i]*=betaNp1[i];
#else
scal(betaNp1[i],phiN[i]);
#endif
}
for (i=1;i<k+1;i++)
{