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Posted online: 2020-04-11 10:54:41Z by Antoine Henrot9

Cite as: P-200411.2

We consider several obstacle problems for the first eigenvalue of the Dirichlet-Laplacian: how to place an obstacle $K$ into a domain $\Omega$ to minimize or maximize the first Dirichlet eigenvalue $\lambda_1(\Omega\setminus K)$. We introduce the following notations: Let $\Omega\subset \mathbb{R}^2$ a bounded open set and $K\subset \Omega$ a compact subset included in $\Omega$. Here $\Omega$ is fixed and $K$ is considered as the unknown. We are interested in $\lambda_1(\Omega\setminus K)$ the first Dirichlet eigenvalue of the set $\Omega\setminus K$ which can be defined as: $$ \lambda_1(\Omega\setminus K):=\min_{u\in H_0^1(\Omega\setminus K)\atop u\neq 0} \frac{\int_{\Omega\setminus K} |\nabla u(x)|^2\,dx}{\int_{\Omega\setminus K} u(x)^2\, dx}. $$

The corresponding *maximization* problem (of Problem 1) has no solutions. Indeed, one can construct a sequence of closed sets $K_n\subset\overline\Omega$ of Lebesgue measure $A$ so that $\lambda_1(\Omega\setminus K_n)\uparrow \infty$ as $n\to\infty$ (for instance by taking $K_n$ as the union of a *given* closed set in $\overline \Omega$ of area $A$ with a curve filling $\overline \Omega$ as $n$ increases, see [9], [10] where the limit distribution in $\overline{\Omega}$ of such curves is studied in detail).
To guarantee the existence of a maximizer one needs to prevent maximizing sequences to spread out over $\overline\Omega$. This can be achieved by imposing stronger geometrical constraints on the class of admissible obstacles (notice that connectedness is still not sufficient).
Therefore, for a fixed $A\in (0, \mathcal{L}(\Omega))$, we are led to consider the **maximization problem**
\begin{equation}\label{prob2}
\max \{ \lambda_1(\Omega\setminus K) : \; \text{$K\subset \overline{\Omega}$, $K$ closed and convex, $\mathcal{L}(K)=A$}\}.
\end{equation}
Now, the existence of a maximizer in the restricted class of convex sets is straightforward (see [3], [5]). Moreover, as convexity seems necessary for the existence, it is natural to expect every solution of this maximization problem to *saturate*
the convexity constraint, in the sense that the boundary of any solution should contain non-strictly convex parts.
In particular, it would be interesting to know whether this maximization problem has only polygonal sets as solutions, see [7], [8] for results in this direction for shape optimization problems with convexity constraints.

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Created at: 2020-04-11 10:54:41Z

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