So, about three weeks ago a fast ($$$\mathcal{O}(n\log^2(n))$$$) algorithm for composition and compositional inverse of formal power series has been discovered by noshi91, here is the original blog. Previous best algorithm was $$$\mathcal{O}((n\log n)^{3/2})$$$. I hope someone more competent than myself will make an in-deapth blog about this later, I'll link it here as soon as I see it. For now I'll give a "quick" retelling of the algorithm.

Boston-Mori algorithm

(Summary of section 2.1 of this article).

We want to compute $$$[x^N]\frac{P(x)}{Q(x)}$$$. To do that we will use an observation that for any polynomial $$$Q(x)$$$ the polynomial $$$Q(x)Q(-x)$$$ is even, so it is equal so $$$V(x^2)$$$ for some $$$V$$$. Also let $$$U(x)=P(x)Q(-x), U(x)=U_e(x^2)+xU_o(x^2)$$$.

So if $$$N$$$ is even $$$[x^N]\dfrac{P(x)}{Q(x)} = [x^{\frac{N}{2}}]\dfrac{U_e(x)}{V(x)}$$$ and if $$$N$$$ is odd $$$[x^N]\dfrac{P(x)}{Q(x)}=[x^{\frac{N-1}{2}}]\dfrac{U_o(x)}{V(x)}$$$. Note that $$$deg(V)=deg(Q)$$$ and $$$deg(U_i) \approx \dfrac{deg(P)+deg(Q)}{2}$$$, so if initially $$$deg(P) \leqslant n$$$ and $$$deg(Q) \leqslant n$$$, those inequalities still hold after we make $$$P=U_i, Q=V, N = \left[\frac{N}{2} \right]$$$. So each step can be made in $$$2$$$ multiplications of polynomials of degree $$$\leqslant n$$$ and there are $$$\log(N)$$$ steps untill we have $$$N=0$$$ and the task is trivial, so the algorithm works in $$$\mathcal{O}(n\log n \log N)$$$

Application to bivariate polynomials

It turns out that this algorithm works great with some bivariate polynomials as well.

Now we want to find $$$[x^k]\frac{P(x, y)}{Q(x, y)}$$$ where $$$deg_y(P), deg_y(Q) \leqslant 1$$$. Note that we don't need any coeffitients with $$$[x^{>k}]$$$ in $$$P$$$ and $$$Q$$$, so we can assume $$$deg_x(P), deg_x(Q) \leqslant k$$$. Now if we make one step of Boston-Mori algorithm we will double limits on degrees of $$$P$$$ and $$$Q$$$ in respect to $$$y$$$ and halve $$$k$$$, so after we take $$$P$$$ and $$$Q$$$ modulo $$$x^{k+1}$$$ again, we'll halve their degrees in respect to $$$x$$$. So the product of $$$x$$$ and $$$y$$$ degrees stays the same and we'll be able to find $$$U$$$ and $$$V$$$ on each step in $$$\mathcal{O}(k\log k)$$$ and solve the problem in $$$\mathcal{O}(k\log^2(k))$$$ total time.

Compositional inverse

From the "Coefficient of fixed $$$x^k$$$ in $$$f(x)^i$$$ " example of this blog by zscoder we know a connection between $$$[x^k]\dfrac{1}{1-yf(x)}$$$ and $$$\left(\dfrac{x}{f^{(-1)}(x)}\right)^k \mod x^k$$$, where $$$f^{(-1)}(f(x))=x$$$. Specifically, we can find either one from the other in linear time. We can now find the first value with Boston-Mori algorithm in $$$\mathcal{O}(k\log^2(k))$$$, and getting $$$f^{(-1)}$$$ from the second value is easy (take $$$ln$$$, multiply by $$$-\frac{1}{k}$$$, take $$$exp$$$), we can now find $$$f^{(-1)} \mod x^k$$$ in $$$\mathcal{O}(k\log^2(k))$$$ time.

Composition

Finding $$$H(F(x)) \mod x^n$$$ is a linear transformation of vector of coefficients of $$$H$$$. If we transpose this transformation we'll get some linear transformation of $$$n$$$-dimansional vector $$$\overline{c}$$$ into a $$$deg(H)+1$$$-dimansional one. If $$$C(x)$$$ is the polynomial whose coeffitients correspond to coordinates of $$$\overline{c}$$$, then the transposed transformation is finding $$$[x^{n-1}]\dfrac{rev(C)}{1-tF} \mod t^{deg(H)+1}$$$, where $$$rev(C)$$$ is $$$x^{n-1}C(\frac{1}{x})$$$ or just reversed $$$C$$$. This can be found in $$$\mathcal{O}(n\log^2(n))$$$, so by Tellegen's principle the original problem of polynomial composition can be solved in $$$\mathcal{O}(n\log^2(n))$$$