- lsqcurvefit is better for a problem of that form.
- Are you supplying your own derivative calculations using the GradObj option (and Hessian option if applicable)? You should do so, since the analytical derivatives are easy here. With lsqcurvefit, there are similar options, e.g. Jacobian.
- How are you initializing the optimization? Because your model is loglinear, it is likely that the initial guess as generated below will be more effective than random guessing.
n=numel(Y);
x0=[ones(n,1) X1(:), X2(:)]\log(Y(:)); %x0=[a;b_1;b_2]
