*Bounty: 50*

*Bounty: 50*

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.

- Initialize $f$.
- 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 $lambda_0$.- 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.)- 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.