//! Constructor
Rosenbrock() :
initialGuess(2),
solution(2)
{
initialGuess(0) = -1.2;
initialGuess(1) = 1;
solution(0) = 1;
solution(1) = 1;
};
void
ChanProblemInterface::init() {
for (int i=0; i<n; i++)
initialGuess(i) =
i*(n-1-i)*source_param(alpha, scale)/((n-1)*(n-1)) + 0.001;
}
// Generate the rational approximation x^(pnum/pden)
void AlgRemez::generateApprox()
{
char *fname = "generateApprox()";
Float time = -dclock();
iter = 0;
spread = 1.0e37;
if (approx_type == RATIONAL_APPROX_ZERO_POLE) {
n--;
neq--;
}
initialGuess();
stpini(step);
while (spread > tolerance) { //iterate until convergance
if (iter++%100==0)
VRB.Result(cname,fname,"Iteration %d, spread %e delta %e\n", iter-1,(Float)spread,(Float)delta);
equations();
if (delta < tolerance)
ERR.General(cname, fname,"Delta too small, try increasing precision\n");
search(step);
}
int sign;
Float error = (Float)getErr(mm[0],&sign);
VRB.Result(cname,fname,"Converged at %d iterations, error = %e\n",
iter,error);
//!< Once the approximation has been generated, calculate the roots
if(!root()) ERR.General(cname,fname,"Root finding failed\n");
if (approx_type == RATIONAL_APPROX_ZERO_POLE) {
roots[n] = (bigfloat)0.0;
n++;
neq++;
}
//!< Now find the partial fraction expansions
if (remez_arg->field_type == BOSON) {
getPFE(remez_arg->residue, remez_arg->pole, &(remez_arg->norm));
getIPFE(remez_arg->residue_inv, remez_arg->pole_inv, &(remez_arg->norm_inv));
} else {
getIPFE(remez_arg->residue, remez_arg->pole, &(remez_arg->norm));
getPFE(remez_arg->residue_inv, remez_arg->pole_inv, &(remez_arg->norm_inv));
}
remez_arg->error = error;
time += dclock();
print_time(cname,fname,time);
}
PitchforkProblemInterface::PitchforkProblemInterface(int N, double a,
double b, double l) :
initialGuess(N),
alpha(a),
beta(b),
lambda(l),
n(N)
{
for (int i=0; i<n; i++)
initialGuess(i) = lambda/alpha + 0.01;
}
//! Constructor
Broyden(int m, double lambdaVal=0) :
initialGuess(m),
solution(m)
{
n = m;
lambda = lambdaVal;
cout << "Broyden ill-conditioning: lambda = " << lambda << "\n";
for (int i=0; i<n; i++) {
// initialGuess(i) = -100; // Case for lambdaBar != 1.0
initialGuess(i) = 0; // General testing
// initialGuess(i) = -1; // Standard
solution(i) = 1;
}
fevals = 0;
};
void NICPAlignerGuiMainWindow::correspondences() {
if(viewer->drawableList().size() < 2) { return; }
initialGuess();
_selectProjector();
nicp::Cloud *referenceCloud = _clouds[_clouds.size() - 2];
nicp::Cloud *currentCloud = _clouds.back();
// Projective Correspondence Finder
_projector->setTransform(_sensorOffset);
_projector->project(_correspondenceFinderProjective->currentIndexImage(),
_correspondenceFinderProjective->currentDepthImage(),
currentCloud->points());
_projector->setTransform(_sensorOffset);
_projector->project(_correspondenceFinderProjective->referenceIndexImage(),
_correspondenceFinderProjective->referenceDepthImage(),
referenceCloud->points());
_correspondenceFinder->compute(*referenceCloud, *currentCloud, (_poses[_poses.size() - 2].inverse() * _poses.back()).inverse());
// NN Correspondence Finder
// _correspondenceFinderNN->init(*referenceCloud, *currentCloud);
// _correspondenceFinder->compute(*referenceCloud, *currentCloud, (_poses[_poses.size() - 2].inverse() * _poses.back()).inverse());
nicp_viewer::Drawable *d = viewer->drawableList().back();
nicp_viewer::DrawableCloud *dCloud = dynamic_cast<nicp_viewer::DrawableCloud*>(d);
if(dCloud) {
dCloud->setTransformation(_poses.back());
dCloud->clearDrawableObjects();
dCloud->constructDrawableObjects();
dCloud->drawableCorrespondences()->setReferencePointsTransformation((_poses[_poses.size() - 2].inverse() * _poses.back()).inverse());
dCloud->drawableCorrespondences()->setReferencePoints(&referenceCloud->points());
dCloud->drawableCorrespondences()->setCurrentPoints(¤tCloud->points());
dCloud->drawableCorrespondences()->setCorrespondences(&_correspondenceFinder->correspondences());
dCloud->drawableCorrespondences()->setNumCorrespondences(_correspondenceFinder->numCorrespondences());
dCloud->drawableCorrespondences()->parameter()->setShow(true);
std::cout << "[STATUS] " << _correspondenceFinder->numCorrespondences() << " correspondences computed" << std::endl;
}
visualizationUpdate();
viewer->updateGL();
}
FGSimplexTrim::FGSimplexTrim(FGFDMExec * fdm, TrimMode mode)
{
std::clock_t time_start=clock(), time_trimDone;
// variables
FGTrimmer::Constraints constraints;
if (fdm->GetDebugLevel() > 0) {
std::cout << "\n-----Performing Simplex Based Trim --------------\n" << std::endl;
}
// defaults
std::string aircraftName = fdm->GetAircraft()->GetAircraftName();
FGPropertyNode* node = fdm->GetPropertyManager()->GetNode();
double rtol = node->GetDouble("trim/solver/rtol");
double abstol = node->GetDouble("trim/solver/abstol");
double speed = node->GetDouble("trim/solver/speed"); // must be > 1, 2 typical
double random = node->GetDouble("trim/solver/random");
int iterMax = int(node->GetDouble("trim/solver/iterMax"));
bool showConvergence = node->GetBool("trim/solver/showConvergence");
bool pause = node->GetBool("trim/solver/pause");
bool showSimplex = node->GetBool("trim/solver/showSimplex");
// flight conditions
double phi = fdm->GetIC()->GetPhiRadIC();
double theta = fdm->GetIC()->GetThetaRadIC();
double gd = fdm->GetInertial()->gravity();
constraints.velocity = fdm->GetIC()->GetVtrueFpsIC();
constraints.altitude = fdm->GetIC()->GetAltitudeASLFtIC();
constraints.gamma = fdm->GetIC()->GetFlightPathAngleRadIC();
constraints.rollRate = 0;
constraints.pitchRate = 0;
constraints.yawRate = tan(phi)*gd*cos(theta)/constraints.velocity;
constraints.stabAxisRoll = true; // FIXME, make this an option
// initial solver state
int n = 6;
std::vector<double> initialGuess(n), lowerBound(n), upperBound(n), initialStepSize(n);
lowerBound[0] = node->GetDouble("trim/solver/throttleMin");
lowerBound[1] = node->GetDouble("trim/solver/elevatorMin");
lowerBound[2] = node->GetDouble("trim/solver/alphaMin");
lowerBound[3] = node->GetDouble("trim/solver/aileronMin");
lowerBound[4] = node->GetDouble("trim/solver/rudderMin");
lowerBound[5] = node->GetDouble("trim/solver/betaMin");
upperBound[0] = node->GetDouble("trim/solver/throttleMax");
upperBound[1] = node->GetDouble("trim/solver/elevatorMax");
upperBound[2] = node->GetDouble("trim/solver/alphaMax");
upperBound[3] = node->GetDouble("trim/solver/aileronMax");
upperBound[4] = node->GetDouble("trim/solver/rudderMax");
upperBound[5] = node->GetDouble("trim/solver/betaMax");
initialStepSize[0] = node->GetDouble("trim/solver/throttleStep");
initialStepSize[1] = node->GetDouble("trim/solver/elevatorStep");
initialStepSize[2] = node->GetDouble("trim/solver/alphaStep");
initialStepSize[3] = node->GetDouble("trim/solver/aileronStep");
initialStepSize[4] = node->GetDouble("trim/solver/rudderStep");
initialStepSize[5] = node->GetDouble("trim/solver/betaStep");
initialGuess[0] = node->GetDouble("trim/solver/throttleGuess");
initialGuess[1] = node->GetDouble("trim/solver/elevatorGuess");
initialGuess[2] = node->GetDouble("trim/solver/alphaGuess");
initialGuess[3] = node->GetDouble("trim/solver/aileronGuess");
initialGuess[4] = node->GetDouble("trim/solver/rudderGuess");
initialGuess[5] = node->GetDouble("trim/solver/betaGuess");
// solve
FGTrimmer * trimmer = new FGTrimmer(fdm, &constraints);
Callback callback(aircraftName, trimmer);
FGNelderMead * solver = NULL;
solver = new FGNelderMead(trimmer,initialGuess,
lowerBound, upperBound, initialStepSize,iterMax,rtol,
abstol,speed,random,showConvergence,showSimplex,pause,&callback);
while(solver->status()==1) solver->update();
time_trimDone = std::clock();
// output
if (fdm->GetDebugLevel() > 0) {
trimmer->printSolution(std::cout,solver->getSolution());
std::cout << "\nfinal cost: " << std::scientific << std::setw(10) << trimmer->eval(solver->getSolution()) << std::endl;
std::cout << "\ntrim computation time: " << (time_trimDone - time_start)/double(CLOCKS_PER_SEC) << "s \n" << std::endl;
}
delete solver;
delete trimmer;
}
Disposable<std::vector<boost::shared_ptr<CalibrationHelper> > >
BasketGeneratingEngine::calibrationBasket(
const boost::shared_ptr<Exercise> &exercise,
boost::shared_ptr<SwapIndex> standardSwapBase,
boost::shared_ptr<SwaptionVolatilityStructure> swaptionVolatility,
const CalibrationBasketType basketType) const {
QL_REQUIRE(
!standardSwapBase->forwardingTermStructure().empty(),
"standard swap base forwarding term structure must not be empty.");
QL_REQUIRE(
!standardSwapBase->exogenousDiscount() ||
!standardSwapBase->discountingTermStructure().empty(),
"standard swap base discounting term structure must not be empty.");
std::vector<boost::shared_ptr<CalibrationHelper> > result;
int minIdxAlive =
std::upper_bound(exercise->dates().begin(), exercise->dates().end(),
Settings::instance().evaluationDate()) -
exercise->dates().begin();
boost::shared_ptr<RebatedExercise> rebEx =
boost::dynamic_pointer_cast<RebatedExercise>(exercise);
for (Size i = minIdxAlive; i < exercise->dates().size(); i++) {
Date expiry = exercise->date(i);
Real rebate = 0.0;
Date rebateDate = expiry;
if (rebEx != NULL) {
rebate = rebEx->rebate(i);
rebateDate = rebEx->rebatePaymentDate(i);
}
boost::shared_ptr<SwaptionHelper> helper;
switch (basketType) {
case Naive: {
Real swapLength = swaptionVolatility->dayCounter().yearFraction(
standardSwapBase->valueDate(expiry), underlyingLastDate());
boost::shared_ptr<SmileSection> sec =
swaptionVolatility->smileSection(
expiry,
static_cast<Size>(swapLength * 12.0 + 0.5) * Months,
true);
Real atmStrike = sec->atmLevel();
Real atmVol;
if (atmStrike == Null<Real>())
atmVol = sec->volatility(0.03);
else
atmVol = sec->volatility(atmStrike);
helper = boost::shared_ptr<SwaptionHelper>(new SwaptionHelper(
expiry, underlyingLastDate(),
Handle<Quote>(boost::make_shared<SimpleQuote>(atmVol)),
standardSwapBase->iborIndex(),
standardSwapBase->fixedLegTenor(),
standardSwapBase->dayCounter(),
standardSwapBase->iborIndex()->dayCounter(),
standardSwapBase->exogenousDiscount()
? standardSwapBase->discountingTermStructure()
: standardSwapBase->forwardingTermStructure(),
CalibrationHelper::RelativePriceError, Null<Real>(), 1.0));
break;
}
case MaturityStrikeByDeltaGamma: {
// determine the npv, first and second order derivatives at
// $y=0$ of the underlying swap
const Real h = 0.0001; // finite difference step in $y$, make
// this a parameter of the engine ?
Real zSpreadDsc =
oas_.empty() ? 1.0
: exp(-oas_->value() *
onefactormodel_->termStructure()
->dayCounter()
.yearFraction(expiry, rebateDate));
Real npvm = underlyingNpv(expiry, -h) +
rebate *
onefactormodel_->zerobond(rebateDate, expiry,
-h, discountCurve_) *
zSpreadDsc;
Real npv = underlyingNpv(expiry, 0.0) +
rebate * onefactormodel_->zerobond(
rebateDate, expiry, 0, discountCurve_) *
zSpreadDsc;
Real npvp = underlyingNpv(expiry, h) +
rebate *
onefactormodel_->zerobond(rebateDate, expiry, h,
discountCurve_) *
zSpreadDsc;
Real delta = (npvp - npvm) / (2.0 * h);
Real gamma = (npvp - 2.0 * npv + npvm) / (h * h);
QL_REQUIRE(npv * npv + delta * delta + gamma * gamma > 0.0,
"(npv,delta,gamma) must have a positive norm");
// debug output
// std::cout << "EXOTIC npv " << npv << " delta " << delta
// << " gamma " << gamma << std::endl;
// Real xtmp = -5.0;
// std::cout
// << "********************************************EXERCISE "
// << expiry << " ******************" << std::endl;
// std::cout << "globalExoticNpv;";
// while (xtmp <= 5.0 + QL_EPSILON) {
// std::cout << underlyingNpv(expiry, xtmp) << ";";
// xtmp += 0.1;
// }
// std::cout << std::endl;
// end debug output
// play safe, we restrict the maximum maturity so to easily fit
// in the date class restriction
Real maxMaturity =
swaptionVolatility->dayCounter().yearFraction(
expiry, Date::maxDate() - 365);
boost::shared_ptr<MatchHelper> matchHelper_;
matchHelper_ = boost::shared_ptr<MatchHelper>(new MatchHelper(
underlyingType(), npv, delta, gamma, onefactormodel_,
standardSwapBase, expiry, maxMaturity, h));
// Optimize
Array initial = initialGuess(expiry);
QL_REQUIRE(initial.size() == 3,
"initial guess must have size 3 (but is "
<< initial.size() << ")");
EndCriteria ec(1000, 200, 1E-8, 1E-8, 1E-8); // make these
// criteria and the
// optimizer itself
// parameters of
// the method ?
Constraint constraint = NoConstraint();
Problem p(*matchHelper_, constraint, initial);
LevenbergMarquardt lm;
EndCriteria::Type ret = lm.minimize(p, ec);
QL_REQUIRE(ret != EndCriteria::None &&
ret != EndCriteria::Unknown &&
ret != EndCriteria::MaxIterations,
"optimizer returns error (" << ret << ")");
Array solution = p.currentValue();
Real maturity = fabs(solution[1]);
Size years = (Size)std::floor(maturity);
maturity -= (Real)years;
maturity *= 12.0;
Size months = (Size)std::floor(maturity + 0.5);
if (years == 0 && months == 0)
months = 1; // ensure a maturity of at least one months
// maturity -= (Real)months; maturity *= 365.25;
// Size days = (Size)std::floor(maturity);
Period matPeriod =
years * Years + months * Months; //+days*Days;
// we have to floor the strike of the calibration instrument,
// see warning in the header
solution[2] = std::max(solution[2], 0.00001); // floor at 0.1bp
// also the calibrated nominal may be zero, so we floor it, too
solution[0] =
std::max(solution[0], 0.000001); // float at 0.01bp
helper = boost::shared_ptr<SwaptionHelper>(new SwaptionHelper(
expiry, matPeriod,
Handle<Quote>(boost::make_shared<SimpleQuote>(
swaptionVolatility->volatility(
expiry, matPeriod, solution[2], true))),
standardSwapBase->iborIndex(),
standardSwapBase->fixedLegTenor(),
standardSwapBase->dayCounter(),
standardSwapBase->iborIndex()->dayCounter(),
standardSwapBase->exogenousDiscount()
? standardSwapBase->discountingTermStructure()
: standardSwapBase->forwardingTermStructure(),
CalibrationHelper::RelativePriceError, solution[2],
fabs(solution[0])));
break;
}
default:
QL_FAIL("Calibration basket type not known (" << basketType
<< ")");
}
result.push_back(helper);
}
return result;
}
// Generate the rational approximation x^(pnum/pden)
double AlgRemez::generateApprox(int num_degree, int den_degree,
unsigned long pnum, unsigned long pden,
int a_len, double *a_param, int *a_pow)
{
char *fname = "generateApprox(int, unsigned long, unsigned long)";
printf("Degree of the approximation is (%d,%d)\n", num_degree, den_degree);
printf("Approximating the function x^(%d/%d)\n", pnum, pden);
// Reallocate arrays, since degree has changed
if (num_degree != n || den_degree != d) allocate(num_degree,den_degree);
if (a_len > SUM_MAX) {
printf("Error: a_length > SUM_MAX");
exit(0);
}
step = new bigfloat[num_degree+den_degree+2];
a_length = a_len;
for (int j=0; j<a_len; j++) {
a[j]= a_param[j];
a_power[j] = a_pow[j];
}
power_num = pnum;
power_den = pden;
spread = 1.0e37;
iter = 0;
n = num_degree;
d = den_degree;
neq = n + d + 1;
initialGuess();
stpini(step);
while (spread > tolerance) { //iterate until convergance
if (iter++%100==0)
printf("Iteration %d, spread %e delta %e\n",
iter-1,(double)spread,(double)delta);
equations();
if (delta < tolerance) {
printf("Delta too small, try increasing precision\n");
exit(0);
}
search(step);
}
int sign;
double error = (double)getErr(mm[0],&sign);
printf("Converged at %d iterations, error = %e\n",iter,error);
// Once the approximation has been generated, calculate the roots
if(!root()) {
printf("Root finding failed\n");
} else {
foundRoots = 1;
}
delete [] step;
// Return the maximum error in the approximation
return error;
}
int main(int argc, char *argv[])
{
int ierr = 0;
int MyPID = 0;
double alpha = 0.25;
double beta = 1.5;
double D1 = 1.0/40.0;
double D2 = 1.0/40.0;
int maxNewtonIters = 10;
try {
// Initialize MPI
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
// Create a communicator for Epetra objects
#ifdef HAVE_MPI
Epetra_MpiComm Comm( MPI_COMM_WORLD );
#else
Epetra_SerialComm Comm;
#endif
// Get the process ID and the total number of processors
MyPID = Comm.MyPID();
int NumProc = Comm.NumProc();
// Check for verbose output
bool verbose = false;
if (argc>1)
if (argv[1][0]=='-' && argv[1][1]=='v')
verbose = true;
// Get the number of elements from the command line
int NumGlobalNodes = 0;
if ((argc > 2) && (verbose))
NumGlobalNodes = atoi(argv[2]) + 1;
else if ((argc > 1) && (!verbose))
NumGlobalNodes = atoi(argv[1]) + 1;
else
NumGlobalNodes = 101;
// The number of unknowns must be at least equal to the
// number of processors.
if (NumGlobalNodes < NumProc) {
std::cout << "numGlobalNodes = " << NumGlobalNodes
<< " cannot be < number of processors = " << NumProc
<< std::endl;
exit(1);
}
// Create the Brusselator problem class. This creates all required
// Epetra objects for the problem and allows calls to the
// function (F) and Jacobian evaluation routines.
Brusselator Problem(NumGlobalNodes, Comm);
// Get the vector from the Problem
Epetra_Vector& soln = Problem.getSolution();
// Begin LOCA Solver ************************************
// Create the top level parameter list
Teuchos::RCP<Teuchos::ParameterList> paramList =
Teuchos::rcp(new Teuchos::ParameterList);
// Create LOCA sublist
Teuchos::ParameterList& locaParamsList = paramList->sublist("LOCA");
// Create the stepper sublist and set the stepper parameters
Teuchos::ParameterList& stepperList = locaParamsList.sublist("Stepper");
stepperList.set("Bordered Solver Method", "Householder");
stepperList.set("Continuation Parameter", "beta");
stepperList.set("Initial Value", beta);
stepperList.set("Max Value", 1.6);
stepperList.set("Min Value", 0.0);
stepperList.set("Max Steps", 100);
stepperList.set("Max Nonlinear Iterations", maxNewtonIters);
#ifdef HAVE_LOCA_ANASAZI
// Create Anasazi Eigensolver sublist (needs --with-loca-anasazi)
stepperList.set("Compute Eigenvalues",true);
Teuchos::ParameterList& aList = stepperList.sublist("Eigensolver");
aList.set("Method", "Anasazi");
if (!verbose)
aList.set("Verbosity", Anasazi::Errors);
aList.set("Block Size", 1); // Size of blocks
aList.set("Num Blocks", 50); // Size of Arnoldi factorization
aList.set("Num Eigenvalues", 3); // Number of eigenvalues
aList.set("Convergence Tolerance", 1.0e-7); // Tolerance
aList.set("Step Size", 1); // How often to check convergence
aList.set("Maximum Restarts",1); // Maximum number of restarts
aList.set("Operator", "Cayley");
aList.set("Cayley Pole", 0.1);
aList.set("Cayley Zero", -0.1);
aList.set("Sorting Order", "CA");
#else
stepperList.set("Compute Eigenvalues",false);
#endif
// Create predictor sublist
Teuchos::ParameterList& predictorList =
locaParamsList.sublist("Predictor");
predictorList.set("Method", "Constant");
// Create step size sublist
Teuchos::ParameterList& stepSizeList = locaParamsList.sublist("Step Size");
stepSizeList.set("Initial Step Size", 0.01);
stepSizeList.set("Min Step Size", 1.0e-3);
stepSizeList.set("Max Step Size", 0.01);
stepSizeList.set("Aggressiveness", 0.1);
// Create the "Solver" parameters sublist to be used with NOX Solvers
Teuchos::ParameterList& nlParams = paramList->sublist("NOX");
// Set the printing parameters in the "Printing" sublist
Teuchos::ParameterList& printParams = nlParams.sublist("Printing");
printParams.set("MyPID", MyPID);
printParams.set("Output Precision", 3);
printParams.set("Output Processor", 0);
if (verbose)
printParams.set("Output Information",
NOX::Utils::OuterIteration +
NOX::Utils::OuterIterationStatusTest +
NOX::Utils::InnerIteration +
NOX::Utils::Details +
NOX::Utils::Warning +
NOX::Utils::TestDetails +
NOX::Utils::Error +
NOX::Utils::StepperIteration +
NOX::Utils::StepperDetails +
NOX::Utils::StepperParameters);
else
printParams.set("Output Information", NOX::Utils::Error);
// Sublist for "Linear Solver"
Teuchos::ParameterList& dirParams = nlParams.sublist("Direction");
Teuchos::ParameterList& newtonParams = dirParams.sublist("Newton");
Teuchos::ParameterList& lsParams = newtonParams.sublist("Linear Solver");
lsParams.set("Aztec Solver", "GMRES");
lsParams.set("Max Iterations", 800);
lsParams.set("Tolerance", 1e-6);
lsParams.set("Output Frequency", 50);
lsParams.set("Preconditioner", "Ifpack");
// Create the interface between the test problem and the nonlinear solver
Teuchos::RCP<Problem_Interface> interface =
Teuchos::rcp(new Problem_Interface(Problem));
// Create the Epetra_RowMatrixfor the Jacobian/Preconditioner
Teuchos::RCP<Epetra_RowMatrix> A =
Teuchos::rcp(&Problem.getJacobian(),false);
// Use an Epetra Scaling object if desired
Teuchos::RCP<Epetra_Vector> scaleVec =
Teuchos::rcp(new Epetra_Vector(soln));
NOX::Epetra::Scaling scaling;
scaling.addRowSumScaling(NOX::Epetra::Scaling::Left, scaleVec);
Teuchos::RCP<LOCA::Epetra::Interface::Required> iReq = interface;
// Create the Linear System
Teuchos::RCP<NOX::Epetra::Interface::Jacobian> iJac = interface;
Teuchos::RCP<NOX::Epetra::LinearSystemAztecOO> linSys =
Teuchos::rcp(new NOX::Epetra::LinearSystemAztecOO(printParams, lsParams,
iReq, iJac, A, soln));
//&scaling);
Teuchos::RCP<NOX::Epetra::LinearSystemAztecOO> shiftedLinSys =
Teuchos::rcp(new NOX::Epetra::LinearSystemAztecOO(printParams, lsParams,
iReq, iJac, A, soln));
// Create initial guess
NOX::Epetra::Vector initialGuess(Teuchos::rcp(&soln,false),
NOX::Epetra::Vector::CreateView,
NOX::DeepCopy);
// Create and initialize the parameter vector
LOCA::ParameterVector pVector;
pVector.addParameter("alpha",alpha);
pVector.addParameter("beta",beta);
pVector.addParameter("D1",D1);
pVector.addParameter("D2",D2);
// Create Epetra factory
Teuchos::RCP<LOCA::Abstract::Factory> epetraFactory =
Teuchos::rcp(new LOCA::Epetra::Factory);
// Create global data object
Teuchos::RCP<LOCA::GlobalData> globalData =
LOCA::createGlobalData(paramList, epetraFactory);
// Create the Group
Teuchos::RCP<LOCA::Epetra::Interface::TimeDependent> iTime = interface;
Teuchos::RCP<LOCA::Epetra::Group> grp =
Teuchos::rcp(new LOCA::Epetra::Group(globalData, printParams,
iTime, initialGuess, linSys,
shiftedLinSys, pVector));
grp->computeF();
// Create the convergence tests
Teuchos::RCP<NOX::StatusTest::NormF> absresid =
Teuchos::rcp(new NOX::StatusTest::NormF(1.0e-8,
NOX::StatusTest::NormF::Unscaled));
Teuchos::RCP<NOX::StatusTest::MaxIters> maxiters =
Teuchos::rcp(new NOX::StatusTest::MaxIters(maxNewtonIters));
Teuchos::RCP<NOX::StatusTest::Combo> combo =
Teuchos::rcp(new NOX::StatusTest::Combo(NOX::StatusTest::Combo::OR));
combo->addStatusTest(absresid);
combo->addStatusTest(maxiters);
// Create stepper
LOCA::Stepper stepper(globalData, grp, combo, paramList);
LOCA::Abstract::Iterator::IteratorStatus status = stepper.run();
if (status != LOCA::Abstract::Iterator::Finished) {
ierr = 1;
if (globalData->locaUtils->isPrintType(NOX::Utils::Error))
globalData->locaUtils->out()
<< "Stepper failed to converge!" << std::endl;
}
// Get the final solution from the stepper
Teuchos::RCP<const LOCA::Epetra::Group> finalGroup =
Teuchos::rcp_dynamic_cast<const LOCA::Epetra::Group>(stepper.getSolutionGroup());
const NOX::Epetra::Vector& finalSolution =
dynamic_cast<const NOX::Epetra::Vector&>(finalGroup->getX());
// Output the parameter list
if (globalData->locaUtils->isPrintType(NOX::Utils::StepperParameters)) {
globalData->locaUtils->out()
<< std::endl << "Final Parameters" << std::endl
<< "****************" << std::endl;
stepper.getList()->print(globalData->locaUtils->out());
globalData->locaUtils->out() << std::endl;
}
// Check some statistics on the solution
NOX::TestCompare testCompare(globalData->locaUtils->out(),
*(globalData->locaUtils));
if (globalData->locaUtils->isPrintType(NOX::Utils::TestDetails))
globalData->locaUtils->out()
<< std::endl
<< "***** Checking solution statistics *****"
<< std::endl;
// Check number of continuation steps
int numSteps = stepper.getStepNumber();
int numSteps_expected = 11;
ierr += testCompare.testValue(numSteps, numSteps_expected, 0.0,
"number of continuation steps",
NOX::TestCompare::Absolute);
// Check number of failed steps
int numFailedSteps = stepper.getNumFailedSteps();
int numFailedSteps_expected = 0;
ierr += testCompare.testValue(numFailedSteps, numFailedSteps_expected, 0.0,
"number of failed continuation steps",
NOX::TestCompare::Absolute);
// Check final value of continuation parameter
double beta_final = finalGroup->getParam("beta");
double beta_expected = 1.6;
ierr += testCompare.testValue(beta_final, beta_expected, 1.0e-14,
"final value of continuation parameter",
NOX::TestCompare::Relative);
// Check final of solution
NOX::Epetra::Vector final_x_expected(finalSolution);
int n = final_x_expected.getEpetraVector().MyLength()/2;
for (int i=0; i<n; i++) {
final_x_expected.getEpetraVector()[2*i] = alpha;
final_x_expected.getEpetraVector()[2*i+1] = beta_final/alpha;
}
ierr += testCompare.testVector(finalSolution, final_x_expected,
1.0e-6, 1.0e-6,
"value of final solution");
LOCA::destroyGlobalData(globalData);
}
catch (std::exception& e) {
std::cout << e.what() << std::endl;
ierr = 1;
}
catch (const char *s) {
std::cout << s << std::endl;
ierr = 1;
}
catch (...) {
std::cout << "Caught unknown exception!" << std::endl;
ierr = 1;
}
if (MyPID == 0) {
if (ierr == 0)
std::cout << "All tests passed!" << std::endl;
else
std::cout << ierr << " test(s) failed!" << std::endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
return ierr;
}
RadialCorrection RadialCorrection::invertCorrection(int h, int w, int step)
{
LensDistortionModelParameters input = this->mParams;
LensDistortionModelParameters result;
/* make initial guess */
result.setPrincipalX(input.principalX());
result.setPrincipalY(input.principalY());
result.setNormalizingFocal(input.normalizingFocal());
result.setTangentialX(-input.tangentialX());
result.setTangentialY(-input.tangentialY());
result.setScale(1.0 / input.scale());
result.setAspect(1.0 / input.scale()); /*< bad guess I believe */
result.mKoeff.resize(RadialCorrectionInversionCostFunction::MODEL_POWER);
for (unsigned i = 0; i < RadialCorrectionInversionCostFunction::MODEL_POWER; i++)
{
if (i < input.mKoeff.size()) {
result.mKoeff[i] = -input.mKoeff[i];
} else {
result.mKoeff[i] = 0.0;
}
}
/* Pack the guess and launch optimization */
RadialCorrection guess(result);
RadialCorrectionInversionCostFunction cost(*this, guess, step, h, w);
LevenbergMarquardt lmFit;
lmFit.maxIterations = 101;
lmFit.maxLambda = 10e8;
lmFit.lambdaFactor = 8;
lmFit.f = &cost;
lmFit.traceCrucial = true;
lmFit.traceProgress = true;
lmFit.traceMatrix = true;
vector<double> initialGuess(cost.inputs);
RadialCorrectionInversionCostFunction::fillWithRadial(guess, &(initialGuess[0]));
cout << guess.mParams << endl;
EllipticalApproximation1d stats;
stats = cost.aggregatedCost(&(initialGuess[0]));
SYNC_PRINT(("Start Mean Error: %f px\n", stats.getRadiusAround0()));
SYNC_PRINT(("Start Max Error: %f px\n", stats.getMax()));
vector<double> target(cost.outputs, 0.0);
vector<double> optimal = lmFit.fit(initialGuess, target);
guess = RadialCorrectionInversionCostFunction::updateWithModel(guess, &(optimal[0]));
/* Cost */
cout << guess.mParams << endl;
stats = cost.aggregatedCost(&(optimal[0]));
SYNC_PRINT(("Final Mean Error: %f px\n", stats.getRadiusAround0()));
SYNC_PRINT(("Final Max Error: %f px\n", stats.getMax()));
return guess;
}
/******************** * remez *======= * Calculates the optimal (in the Chebyshev/minimax sense) * FIR filter impulse response given a set of band edges, * the desired reponse on those bands, and the weight given to * the error in those bands. * * INPUT: * ------ * int numtaps - Number of filter coefficients * int numband - Number of bands in filter specification * double[] bands - User-specified band edges [2 * numband] * double[] des - User-specified band responses [numband] * double[] weight - User-specified error weights [numband] * int type - Type of filter * * OUTPUT: * ------- * double[] h - Impulse response of final filter [numtaps] ************************************************************************** * Converted to C++ by Tony Kirke July 2001 ************************************************************************** * Copyright (C) 1995 Jake Janovetz ([email protected]) * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. * * Converted to Java by Iain A Robin, June 1998 *************************************************************************/ double* remez_fir::remez(int numtaps, int numband, double bands[], double des[], double weight[], int type) { double c; int i; int symmetry = (type == BANDPASS)? POSITIVE : NEGATIVE; int r = numtaps/2; // number of extrema if ((numtaps%2 != 0) && (symmetry == POSITIVE)) r++; // Predict dense grid size in advance for array sizes int gridSize = 0; for (i=0; i<numband; i++) { gridSize += (int)floor(0.5+2*r*GRIDDENSITY*(bands[2*i+1] - bands[2*i])); } if (symmetry == NEGATIVE) gridSize--; double* grid = new double[gridSize]; double* d = new double[gridSize]; double* w = new double[gridSize]; double* e = new double[gridSize]; double* x = new double[r+1]; double* y = new double[r+1]; double* ad = new double[r+1]; double* taps = new double[r+1]; int* ext = new int[r+1]; // Create dense frequency grid createDenseGrid(r, numtaps, numband, bands, des, weight, gridSize, grid, d, w, symmetry); initialGuess(r, ext, gridSize); // For Differentiator: (fix grid) if (type == DIFFERENTIATOR) { for (i=0; i<gridSize; i++) { if (d[i] > 0.0001) w[i] /= grid[i]; } } // For odd or Negative symmetry filters, alter the // d[] and w[] according to Parks McClellan if (symmetry == POSITIVE) { if (numtaps % 2 == 0) { for (i=0; i<gridSize; i++) { c = cos(PI * grid[i]); d[i] /= c; w[i] *= c; } } } else { if (numtaps % 2 != 0) { for (i=0; i<gridSize; i++) { c = sin(TWOPI * grid[i]); d[i] /= c; w[i] *= c; } } else { for (i=0; i<gridSize; i++) { c = sin(PI * grid[i]); d[i] /= c; w[i] *= c; } } } // Perform the Remez Exchange algorithm int iter; for (iter=0; iter<MAXITERATIONS; iter++) { calcParms(r, ext, grid, d, w, ad, x, y); calcError(r, ad, x, y, gridSize, grid, d, w, e); search(r, ext, gridSize, e); if (isDone(r, ext, e)) break; } if (iter == MAXITERATIONS) { cout << "Reached maximum iteration count.\n"; cout << "Results may be bad\n"; } calcParms(r, ext, grid, d, w, ad, x, y); // Find the 'taps' of the filter for use with Frequency // Sampling. If odd or Negative symmetry, fix the taps // according to Parks McClellan for (i=0; i<=numtaps/2; i++) { if (symmetry == POSITIVE) { if (numtaps%2 != 0) c = 1; else c = cos(PI * (double)i/numtaps); } else { if (numtaps%2 != 0) c = sin(TWOPI * (double)i/numtaps); else c = sin(PI * (double)i/numtaps); } taps[i] = computeA((double)i/numtaps, r, ad, x, y)*c; } // Frequency sampling design with calculated taps return freqSample(taps, numtaps, symmetry); }
