# Nonlinear low-rank approximation - corrected

I would like to state that this is related to a past question of mine which contained errors and now appears in the corrected form, with the erroneous one deleted and closed. In my research of linear algebra and optimization I have recently come across a problem related to the well-known low-rank optimization problem:

Given a constant matrix $X\in\mathbb{R}^{n \times n}$, $$\min_{A\in\mathbb{R}^{n\times k}, B\in\mathbb{R}^{k \times n}} \| X - A B \|_F$$

Where the norm is the Frobenius norm. I was considering this interesting possible extension:

Given a constant matrix $X\in\mathbb{R}^{n \times n}$, $$\min_{A\in\mathbb{R}^{n\times k}, B\in\mathbb{R}^{k \times n}} \| X - A \phi(B) \|_F$$ where $\phi$ is a smooth convex function $\phi : \mathbb{R}\to\mathbb{R}$ (a scalar function applied entrywise to the matrix argument).

Of course if $\phi$ is linear the problem is quite easy, but when $\phi$ is non-linear things are more complicated and interesting. My question is, can I hope to find specific functions/families of nonlinear functions $\phi$ for which the problem is analytically solvable or approximated (by bounds or approximations of some kind)? I was thinking perhaps power series expansions can help or probability tricks of some sort, but I cannot really proceed. I was thinking in the direction where $\phi(x)=\max{(x,0)}$ meaning entrywise positive part of the rectangular matrix $B$. Also, can someone suggest an algorithm to solve this? I thank all helpers.

Since $\phi$ is a convex function, you can approximate it with linear functions with the maximum operator. The problem is then turned into a quadratic programming problem due to the Frobenius norm. If it is an $L_1$ norm, then it is a linear programming problem.