Let $A$ be a $2\times 2$ matrix which we assume to be *contracting*, i.e., the exists $\alpha\in(0,1)$ such that


$$
\|A {\mathbf x}\|\_2\le \alpha\|{\mathbf x}\|\_2,\quad \forall {\mathbf x}\in\mathbb R^2.
$$
Let for $i=1,2$,
$$
T\_i(\mathbf x)=A\mathbf x+\mathbf v\_i
$$
for some vectors $\mathbf v\_1, \mathbf v\_2$. Let now $\Lambda$ denote the *attractor* for the iterated function system $\{T\_1, T\_2\}$, i.e., the unique set such that $\Lambda=T\_1(\Lambda)\cup T\_2(\Lambda)$ (a *self-affine set*). 


**Question.** Assuming that $T\_1(\Lambda)\cap T\_2(\Lambda)=\varnothing$, are there closed formulae for the Hausdorff and box-counting (Minkowski) dimensions of $\Lambda$? 


I know that there are plenty of papers on this subject but it looks like every time I follow yet another link, I only fall further down the rabbit hole... 



Dimensions of self-affine sets
I'm going to expand on Gerald's answer. Indeed, there is no known general formula for the Hausdorff and/or box counting dimension of self-affine sets, even in your situation which is a priori the simplest possible (ambient dimension $2$, only two maps, and strong separation).


Falconer's classical theorem from "The Hausdorff dimension of self-affine fractals" says that if $\alpha<1/2$, then for *almost every* choice of translations $v\_1,v\_2$, the Hausdorff and box counting dimensions of $\Lambda$ agree and are given by what has come to be known as the *affinity* or *singularity* dimension. However, even this number is defined as a limit which is impossible to compute or even estimate rigorously outside of special cases, so it is definitely not a closed formula.


*Edit*: when all affine maps have the same linear part, as in the question, or when all linear parts are diagonal, there are explicit formulas for the affinity dimension. It seems the case Nikita is interested in satisfies both of these assumptions. 


Moreover, even in the setting of your questions, it is not even known whether the box counting dimension exists! (i.e. whether the lower and upper box counting dimensions coincide). And it is well known since McMullen's paper in the 80s that Hausdorff and box-counting dimensions may differ.


There are some settings in which the Hausdorff and/or box-counting dimensions can be calculated, even explicitly, or at least where they can be shown to equal the affinity dimension. Generally speaking there are two lines of research in this direction: trying to verify Falconer's formula in special cases (obtaining sure rather than almost sure results), or looking into self-affine carpets which are exceptional in the sense that the special alignment makes Falconer's formula fail. Let me know if you want some references.


To conclude, let me mention that the reason why assuming separation of the pieces $T\_1(\Lambda)$ and $T\_2(\Lambda)$ doesn't help (or helps very little) in the self-affine context, is because the *projections* of the set $\Lambda$ onto lines play a crucial role, and even if $T\_1(\Lambda)$ and $T\_2(\Lambda)$ are disjoint, their projections can and very often will have a very complicated overlapping structure.


*Edit after Nik's comment*: in the diagonal case, the box dimension calculation becomes easier, but only assuming the rectangular open set condition (ROSC) - defined as the existence of an rectangle $R$ with axes-parallel sides such that the images $T\_1(R), T\_2(R)$ are non-overlapping. I think this can't hold if both eigenvalues of $A$ are larger than $1$. However, assuming ROSC, D.J. Feng and Y. Wang ("A class of self-affine sets and self-affine measures", Corollary 1) give an explicit formula for the box-counting dimension in terms of the projections of the set. In turn, when the linear parts agree and have norm more than $1/2$, it is well known that the relevant projection contains an interval, in particular is of dimension $1$, so the Feng-Wang formula becomes completely explicit.


For Hausdorff dimension things are much more difficult, even in the diagonal case, but M. Hochman's recent breakthrough on self-similar measures ("On self-similar sets with overlaps and inverse theorems for entropy") can be used to gain much better information than we had before (only in the diagonal case). An example (or class of examples) is in my recent preprint with J. Fraser, and we are currently working on developing this further. 


