I'm trying to model convergence of $x\in \mathbb{R}^{+d}$ which follows the following recurrence

$$x\leftarrow (\mathbf{1}-h)^2 x + h\langle x, h\rangle$$

Here $\mathbf{1}$ indicates a vector of $1$'s, $v^2$ means squaring each component of vector $v$ and $h\propto (1^{-p},2^{-p},\ldots ,d^{-p})$ for some $p\in (1,2)$. 

I know that continuous approximation $x_t\approx \exp(At)x_0$ holds and need to know how trajectory of $\|x_t\|_1$ depends on $p$ in the case of $t<d$ and $d\to\infty$

Things are easy if we didn't have the the $h\langle x, h\rangle$ term (mixing term). $A$ is diagonal, and approximating $\|x_t\|_1$ with an integral I get formulas which match observed behavior very well.


[![enter image description here][1]][1]
<sub><sup>[Notebook](https://www.wolframcloud.com/obj/yaroslavvb/nn-linear/mathoverflow-rank1-recurrence-simple.nb)</sup></sub>


However, keeping the mixing term makes things much harder to handle. Integration approach as before [gives formula](https://math.stackexchange.com/a/4668173/998) which is cumbersome. There's also numeric [approach](https://mathoverflow.net/a/443030/7655) which works but doesn't give insight on the role of $p$.

Any advice on the approaches to follow to get a nice **upper bound on $\|x_t\|_1$ in terms of $p$**?


Motivation: this kind of equation gives evolution of expected value of iterated Gaussian linear system [like this](https://mathoverflow.net/questions/443143/when-is-prod-i-0-infty-i-x-i-x-it-0-for-isotropic-gaussian-x-i) for non-isotropic Gaussian case. Used to model training curve of neural network training.

  [1]: https://i.sstatic.net/3a3jd.png