Advanced Composition in DP is worse than Basic Composition

I have problems with understanding the advanced composition theorem in DP.

Let I have two approximate-DP mechanisms ($k = 2)$ where each satisfies $(\epsilon = 0.5, \delta = 0.1)$-DP. By basic composition, I know that using two queries sequentially will guarantee $(2 \cdot 0.5, 2 \cdot 0.1) = (1, 0.2)$-DP.

Advanced composition, however, says that, instead of the composition having $\delta' = k\cdot \delta$, if we are willing to take $\delta ' = k\cdot \delta + \tilde{\delta}$ for some $\tilde{\delta}>0$, then our $\epsilon'$ improves from $2\epsilon$ to $\epsilon' = k\cdot \epsilon(\exp(\epsilon) - 1) + \epsilon\sqrt{2 \cdot k \cdot \log (1/\tilde{\delta})}$.

Now, assume I am happy with $\delta' = 0.3$ instead of $\delta' = 0.2$. This means $\tilde{\delta}= 0.1$. So, $$\epsilon' = 2\cdot 0.5(\exp(0.5) - 1) + 0.5\sqrt{2 \cdot 2 \cdot \log (1/0.1)} = 2.16 \gg 1.$$

I don't understand how this is improving upon the basic composition, as obviously here this is not the case! Am I doing something wrongly?

Edit:

The numbers I have fixed play no role. In general, we know that we can compose $k$ mechanisms (assume each is $(\epsilon, \delta)$-DP) and get $(k\epsilon, k\delta)$-DP just by basic composition. But, by increasing $k\delta$ a bit, we get an $\epsilon'$ which is equal to:

$$k \epsilon \underbrace{(e^\epsilon - 1)}_{\geq 0} + \underbrace{\epsilon \sqrt{2 k \log(1 / \tilde{\delta})}}_{\geq 0} $$ which is not always less than $k\epsilon$.

Specifically, let my extra allowance be $\tilde{\delta} = 0.1$. I want to see when the advanced composition improves upon the basic composition. So, in summary I want to see when the following holds:

\begin{align} & k\epsilon > k \epsilon (e^\epsilon - 1) + \epsilon \sqrt{2 k \log(1 / \tilde{\delta})} \\ \iff & k > k (e^\epsilon - 1) + \sqrt{2 k \log(1 / \tilde{\delta})} \\ \iff & \sqrt{k}(2 - e^\epsilon) > \sqrt{2 \log(1 / \tilde{\delta})} \\ \iff & k > \frac{2 \log(1 / \tilde{\delta})}{(2 - e^\epsilon)^2} \\ \iff & k > \frac{2 \log(10)}{(2 - e^\epsilon)^2}. \end{align} Now assume I want to use $2$ mechanisms. Then, I need to have:

\begin{align} & \log(10) <(2 - e^\epsilon)^2 \\ \iff & \epsilon < \log(2 - \sqrt{\log(10)}) = -0.7286 \end{align} which is never possible. So, when $k = 2$, and if I am willing to only add $0.1$ to $\delta'$, then I can never improve basic composition with advanced composition?

Edit 2: We can, in general, say that advanced composition only improves upon the basic composition if the following is satisfied:

$$ \epsilon < \log\left[2 - \sqrt{\frac{2 \log ( 1/\tilde{\delta})}{k}} \right] $$

which requires $k > 4$ when, e.g., $\tilde{\delta} = 0.1$ and this number increases when $\tilde{\delta}$ decreases.

Overall, I feel like advanced composition is really useless when $k$ is not large. Is this true?

First, there are other composition results, for example I believe this one improves on advanced composition. I'll answer a more general question though (which I think you are getting at).

Given mechanisms $M_1,\dots, M_n$, how does one get the best parameters for their composition?

Ideally we could say "use basic composition for small $k$", and "use advanced composition for large $k$". Unfortunately, one can formally show that this is not straightforward. The Complexity of Computing the Optimal Composition of Differential Privacy studies, for "input" mechanisms $M_1,\dots, M_n$ of parameters $(\epsilon_1,\delta_1),\dots, (\epsilon_n, \delta_n)$, the problem of finding the "minimal" parameters $(\epsilon^*, \delta^*)$ of the composition mechanism.

Prior results were known, for example

If $M_1,\dots, M_n$ are all $(\epsilon,\delta)$-private for a fixed pair of $(\epsilon, \delta)$, and a target $\delta^*$ is given, then the optimal value of $\epsilon^*$ is the minimal $\epsilon^*\geq 0$ such that $$\frac{1}{(1+e^\epsilon)^n}\sum_{\ell = \lceil (\epsilon^*+n\epsilon)/2\epsilon\rceil}^n\binom{n}{\ell}(e^{\ell \epsilon}-e^{\epsilon^*}e^{(n-\ell)\epsilon}) \leq 1 - \frac{1-\delta^*}{(1-\delta)^n}.$$

The paper I linked extends this result to the case of $M_1,\dots, M_n$ being private of parameters $(\epsilon_1,\delta_1),\dots,(\epsilon_n,\delta_n)$ not all the same. They find a similar (but more complicated) expression characterizing the optimal value of $\epsilon^*$ (when given a "target" $\delta^*$), and find that computing this optimum solution is $\#P$-complete, e.g. is unlikely to be able to be efficiently done. This is true even in the case of composition for pure mechanisms, meaning $\delta_i = 0$ for all $i$.

Perhaps the most interesting to you is that there are approximation algorithms for this problem (also in that paper). I do not know if anyone has implemented them, but if they have then it seems like a good option for concrete selection of parameters.

It is worth mentioning as well that there are closely-related notions of privacy (namely concentrated differential privacy) where the composition story is more straightforward, while one still (in a certain sense) achieves $O(\sqrt{k})$ scaling with $k$-fold composition, rather than $O(k)$ scaling "basic composition" gives you.