Rosenbrock function for testing Nonlinear Conjugate Gradient Method

[varunagrawal]

I implemented the Rosenbrock function as a 1D factor graph to test the correctness and efficiency of NonlinearConjugateGradientOptimizer.

Good news is that it is able to optimize to the correct value $(x, y) = (a, a^2)$. The bad news is that it takes many iterations if the initial estimation is not close enough.
For example, given a=12 and x=3, y=5, it takes over 300 iterations for it to converge to the correct solution which seems like a lot for Conjugate Gradient in the 1D case.
I'll test this against simple steepest descent to see how long this takes so we can compare the two.

I feel this can be significantly improved by implementing additional line search methods which have theoretical bounds following Wolfe Conditions. The Golden Section Search which is currently performed is robust but slow. @mehregandor and I can analyze this further to improve the convergence rates.

[mehregandor]

I think there's an issue with the full Rosenbrock function formulation.

[mehregandor]

The way this is graph is constructed currently, it seems to me that the full objective function becomes $$f(x,y) = 2(x-a)^2 + 2\frac{1}{b^2}(x^2-y)^2$$. Instead it should be $$b(x^2-y)^2$$, so it should be $$\frac{1}{\sqrt{b}}$$ in the noise model sigma, no? Also, why do we need the $$\sqrt{2}$$, is it because the full cost is computed as $$\frac{1}{2}f_1^\top \Sigma_1^{-1} f_1 + \frac{1}{2}f_2^\top \Sigma_2^{-1} f_2$$? Instead consider taking out the $$\sqrt{2}$$ from the error and putting it into the covariance directly. Thanks for clarifying

[varunagrawal]

Yes this one is wrong. The correct definition is in GetRosenbrockGraph. That's a good suggestion to put 2 in the covariance/precision.

[varunagrawal]

Updated

[dellaert]

Please do note that GTSAM error is defined as 0.5*r^2, so please be consistent with that. No point in having 2 definitions of the objective function.

[varunagrawal]

Yup, I updated the tests to always use the same definition.

[JD-Florez]

Maybe I am missing something, why is b scaled here?

[varunagrawal]

The error function for factors is $\frac{1}{2}e^2$ so by defining $e^2 = (x^2 - y)\Sigma^{-1} (x^2 - y) = (x^2 - y)2b (x^2 - y) = 2b(x^2 - y)^2$ we get the second term as $b(x^2 - y)^2$.

[varunagrawal]

I re-ran the test with a=2, b=100, x=1.0, y=1.0 and it is able to converge in 13 steps.
Similarly, for a = 12, b = 100 and x = 10.0, y = 135.0, it takes about 23 steps, so it seems like the optimization is highly dependent on how close the initial estimate is to the final solution.

[mehregandor]

Looks good to me.

[varunagrawal]

Merging!