# A maximization problem, with motivation in machine learning

Consider the minimization problem described this paper. Let $$f_{\lambda}$$ be the minimizer. As a part of extending my work, I am able to show the following facts

$$\lim_\limits{\lambda \to 0}\|f_{\lambda}\|_{L^2} = 0$$ and $$\lim_\limits{\lambda \to \infty}\|f_{\lambda}\|_{L^2} = 0$$

My problem now is (as I would like to extend my work), find $$\lambda \in (0,\infty)$$ for which $$\|f_{\lambda}\|_{L^2}$$ is maximum. Appreciate your suggestions to solve this problem.

The minimization problem from the linked paper is given below for the self containment of the post. If given that $$k>\frac{m}{2}$$, the paper proves that there is a unique minimizer for the functional $$C(f)$$ in the set $$S$$.   It is given that $$k>\frac{m}{2}$$

## My progress

Partial Progress :

Progress : I am able to derive the corresponding PDE equations for the problem.

Let $$f(\lambda,x) = f_{\lambda}(x)$$. Then The first equation corresponds to maximizing $$\|f_{\lambda}\|$$, while the second PDE is for the minimization problem associated with the parameter $$\lambda$$.

The second equation (minimization problem), given any $$\lambda$$, I can solve for $$f(\lambda,.)$$ either using linear algebra or steepest descent algorithm, which I have described in my article. Now I need to use this solution and the first equation to obtain $$\lambda$$, which is a problem I am facing.

Trying to solve using linear algebra, by formulating the discrete version of the problem using Fourier series coefficients and Plancheral theorem, I get stuck at the matrix problem described here.

More Partial Progress

An Iterative algorithm which is a modified steepest descent.

1. Initialize $$f$$.

2. Assuming some $$\lambda$$ and assuming gradient of $$C_{\lambda}(f)$$ wrt $$f$$ be $$\nabla_f C_{\lambda}(f)$$, and if we were to update $$f$$ with this gradient as in we do in steepest descent, it would be $$f^u_\lambda = f - \delta \nabla_f C_{\lambda}(f)$$, where $$\delta$$ is a constant learning rate. Now set $$\frac{\partial\|f^u_\lambda\|}{\partial \lambda} = 0$$ and solve for $$\lambda$$. Let the root be $$\lambda_0$$.

3. Update $$f = f^u_{\lambda_0}$$. (update $$f$$ as in steepest descent, but using $$\lambda$$ value as $$\lambda_0$$ which was computed in step 2.)

4. check some convergence criterion and if not met, go to step 2.

I have implemented this numerically and it converges as desired. Need to work on the proof.

PS : This was first posted on MO by me, 3 months back. Link and cross posted on math.SE here.