public AbcdCoeffHolder(double?a,
double?b,
double?c,
double?d,
bool aIsFixed,
bool bIsFixed,
bool cIsFixed,
bool dIsFixed)
{
a_ = a;
b_ = b;
c_ = c;
d_ = d;
aIsFixed_ = false;
bIsFixed_ = false;
cIsFixed_ = false;
dIsFixed_ = false;
k_ = new List <double>();
error_ = null;
maxError_ = null;
abcdEndCriteria_ = EndCriteria.Type.None;
if (a_ != null)
{
aIsFixed_ = aIsFixed;
}
else
{
a_ = -0.06;
}
if (b_ != null)
{
bIsFixed_ = bIsFixed;
}
else
{
b_ = 0.17;
}
if (c_ != null)
{
cIsFixed_ = cIsFixed;
}
else
{
c_ = 0.54;
}
if (d_ != null)
{
dIsFixed_ = dIsFixed;
}
else
{
d_ = 0.17;
}
AbcdMathFunction.validate(a_.Value, b_.Value, c_.Value, d_.Value);
}
// to constrained <- from unconstrained
public AbcdCalibration(List <double> t,
List <double> blackVols,
double aGuess = -0.06,
double bGuess = 0.17,
double cGuess = 0.54,
double dGuess = 0.17,
bool aIsFixed = false,
bool bIsFixed = false,
bool cIsFixed = false,
bool dIsFixed = false,
bool vegaWeighted = false,
EndCriteria endCriteria = null,
OptimizationMethod method = null)
{
aIsFixed_ = aIsFixed;
bIsFixed_ = bIsFixed;
cIsFixed_ = cIsFixed;
dIsFixed_ = dIsFixed;
a_ = aGuess;
b_ = bGuess;
c_ = cGuess;
d_ = dGuess;
abcdEndCriteria_ = QLCore.EndCriteria.Type.None;
endCriteria_ = endCriteria;
optMethod_ = method;
weights_ = new InitializedList <double>(blackVols.Count, 1.0 / blackVols.Count);
vegaWeighted_ = vegaWeighted;
times_ = t;
blackVols_ = blackVols;
AbcdMathFunction.validate(aGuess, bGuess, cGuess, dGuess);
Utils.QL_REQUIRE(blackVols.Count == t.Count, () =>
"mismatch between number of times (" + t.Count + ") and blackVols (" + blackVols.Count + ")");
// if no optimization method or endCriteria is provided, we provide one
if (optMethod_ == null)
{
double epsfcn = 1.0e-8;
double xtol = 1.0e-8;
double gtol = 1.0e-8;
bool useCostFunctionsJacobian = false;
optMethod_ = new LevenbergMarquardt(epsfcn, xtol, gtol, useCostFunctionsJacobian);
}
if (endCriteria_ == null)
{
int maxIterations = 10000;
int maxStationaryStateIterations = 1000;
double rootEpsilon = 1.0e-8;
double functionEpsilon = 0.3e-4; // Why 0.3e-4 ?
double gradientNormEpsilon = 0.3e-4; // Why 0.3e-4 ?
endCriteria_ = new EndCriteria(maxIterations, maxStationaryStateIterations, rootEpsilon, functionEpsilon,
gradientNormEpsilon);
}
}