I looked only at the GuassNewton class as a general guide to how
things work. I like it a lot! I only wish all of the optimizations
were rewritten in this way.
Several comments
1) I believe this code can now be simplified
// build the linear problem
final double[] b = new double[nC];
final double[][] a = new double[nC][nC];
for (int i = 0; i < nR; ++i) {
final double[] grad = weightedJacobian.getRow(i);
final double residual = currentResiduals.getEntry(i);
// compute the normal equation
//residual is already weighted
for (int j = 0; j < nC; ++j) {
b[j] += residual * grad[j];
}
// build the contribution matrix for measurement i
for (int k = 0; k < nC; ++k) {
double[] ak = a[k];
//Jacobian/gradient is already weighted
for (int l = 0; l < nC; ++l) {
ak[l] += grad[k] * grad[l];
}
}
}
Should be just this (I haven't checked the math)
jt = weightedJacobian.transpose();
a = jt* weightedJacobian;
b= jt*currentResiduals;
2) Based on the reasons I previously describe, would it be possible to
separate the newton step into a separate function (which you would
also add to the interface)?
The idea here is that solving for the newton step can be
computationally intensive depending on the size of the problem. So
there is no one universal approach. There are several somewhat
different ways that his can be solved, with
some methods requiring information from previous steps in order to
work. This would also allow to plug in line search or trust region
control of step size. In order to allow all of these approaches, I
would suggest this function:
//this function would solve for dX
public NewtonStepResult solveStep(LeastSquaresProblem lsp, RealVector
currentPoint, Object prevStepInfo)
{
NewtonStepResult result = new NewtonStepResult();
//code to solve for the next step dx followed by
result.newtonStep = dx;
//code to update the step information
//leave blank for now
result.stepInfo = null;
}
where
public class NewtonStepResult
{
public class RealVector newtonStep;
public class Object stepInfo;
}
Please tell me if what I wrote is not clear.
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