#StackBounty: #machine-learning A maximization problem, with motivation in machine learning

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 $S$.

enter image description here

enter image description here

enter image description here

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

enter image description here

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.


Get this bounty!!!

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.