/**
* Return gradient term of quadratic. This instance will be in the vector space of the
* perturbModel, if provided. Otherwise, uses an instance of the referenceModel.
*
* @return Value of <code> b = -F' N [data - f(m)] + Mm </code> if dampOnlyPerturbation is false,
* and <code> b = -F' N [data - f(m)] </code> if dampOnlyPerturbation is true.
*/
public Vect getB() {
Vect data = VectUtil.cloneZero(_data); // data is data with zeros
_transform.forwardNonlinear(data, _referenceModel); // data is f(m)
data.add(1., -1., _data); // data is -e = -(d - f(m))
_transform.adjustRobustErrors(data); // (remove outliers from e)
data.multiplyInverseCovariance(); // data is -Ne
Vect b = null;
if (_dampOnlyPerturbation) {
if (_perturbModel != null) {
b = VectUtil.cloneZero(_perturbModel); // b is 0
} else {
b = VectUtil.cloneZero(_referenceModel); // b is 0
}
} else {
if (_perturbModel != null) {
b = _perturbModel.clone();
b.project(0., 1., _referenceModel); // b is m
} else {
b = _referenceModel.clone(); // b is m
}
b.multiplyInverseCovariance(); // b is M m
}
_transform.addTranspose(data, b, _referenceModel); // b is -F'Ne [+ Mm]
data.dispose();
return b;
}
/**
* Evaluate the full objective function without approximation. Provide a model in the vector space
* of the referenceModel. perturbModel is unused. Useful for line-search of best scale factor. If
* dampedPerturbation, evaluates
*
* <pre>
* [f(m)-data]'N[f(m)-data] + (m-m0)'M(m-m0)
* </pre>
*
* where m0 is the reference model. Otherwise evaluates
*
* <pre>
* [f(m)-data]'N[f(m)-data] + m'M m
* </pre>
*
* @param m Model to be evaluated.
* @return Value of objective function.
*/
public double evalFullObjectiveFunction(VectConst m) {
Vect data = VectUtil.cloneZero(_data); // data is zeros
Vect model = m.clone(); // model is m
model.constrain(); // may have been done already
_transform.forwardNonlinear(data, model); // data is f(m)
data.add(1., -1., _data); // data is e = f(m) - data
_transform.adjustRobustErrors(data);
double eNe = data.magnitude(); // eNe is e'Ne
checkNaN(eNe);
data.dispose();
// damp perturbation if requested
if (_dampOnlyPerturbation) {
model.add(1., -1., _referenceModel); // model is (m-m0)
}
double mMm = model.magnitude(); // mMm is (m-m0)'M(m-m0)
checkNaN(mMm);
model.dispose();
return (eNe + mMm);
}
