// Computation will be useless without a checker (see "for-loop").
if (checker == null) {
throw new NullArgumentException();
}
RealVector currentPoint = lsp.getStart();
// iterate until convergence is reached
Evaluation current = null;
while (true) {
iterationCounter.incrementCount();
// evaluate the objective function and its jacobian
Evaluation previous = current;
// Value of the objective function at "currentPoint".
evaluationCounter.incrementCount();
current = lsp.evaluate(currentPoint);
final RealVector currentResiduals = current.getResiduals();
final RealMatrix weightedJacobian = current.getJacobian();
currentPoint = current.getPoint();
// Check convergence.
if (previous != null) {
if (checker.converged(iterationCounter.getCount(), previous, current)) {
return new OptimumImpl(
current,
evaluationCounter.getCount(),
iterationCounter.getCount());
}
}
// solve the linearized least squares problem
final RealVector dX = this.decomposition.solve(weightedJacobian, currentResiduals);
// update the estimated parameters
currentPoint = currentPoint.add(dX);
}
}