SearcharxivSearch

arXiv · 1707.09833

Random gluing of metric spaces

Abstract

We construct random metric spaces by gluing together an infinite sequence of pointed metric spaces that we call blocks. At each step, we glue the next block to the structure constructed so far by randomly choosing a point on the structure and then identifying it with the distinguished point of the block. The random object that we study is the completion of the structure we obtain after an infinite number of steps. We introduce a sequence $(w_n)_{n\geq1}$ that we call the weights of the blocks. The probability at each step that the next block is glued onto any of the preceding blocks is proportional to its weight. We suppose that the blocks are i.i.d. copies of the same random metric space, scaled by deterministic factors that we call $(\lambda_n)_{n\geq 1}$. We work under some conditions on the distribution of the blocks ensuring that they a.s. have dimension $d$, for some $d>0$. The main contribution of this paper is the computation of the Hausdorff dimension of the set $\mathcal{L}$ of points which appear during the completion procedure, which we call the leaves, when $(\lambda_n)_{n\geq 1}$ and $(w_n)_{n\geq1}$ typically behave like a power of $n$, say $n^{-\alpha}$ for the scaling factors and $n^{-\beta}$ for the weights. For a large domain of $\alpha$ and $\beta$, we have $\mathrm{dim_H}(\mathcal{L})=\alpha^{-1}$. However for $\beta>1$ and $\alpha<1/d$, our results reveal an interesting phenomenon: the dimension has a non-trivial dependence in $\alpha$, $\beta$ and $d$, namely $\mathrm{dim_H}(\mathcal{L})=\alpha^{-1}(2\beta-1-2\sqrt{(\beta-1)(\beta-d\alpha)})$.}

Explore related subjects

Keep this discovery

BibTeXRIS

Delphin Sénizergues. 2017-07-31. Random gluing of metric spaces. https://arxiv.org/abs/1707.09833

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection

In this paper, we study averaging principles for nonautonomous multiscale stochastic Burgers equations with reflection. First, we derive a general averaging principle applicable to such equations under minimal assumptions. Subsequently, since the coefficients of the obtained averaged equation still depend on the small scaling parameter $\e$, we impose either periodic or asymptotic conditions on the coefficients, thereby obtain two distinct averaged equations whose coefficients are independent of $\e$ and establish two averaging principles. Stopping times and Khasminskii's time discretization schemes play an important role. Finally, a concrete example is provided to illustrate the applicability and validity of the theoretical results.

math.PR

Spectral properties of Random Matrices

We give the theoretical foundations of random matrix theory through the definitions of a random matrix, a random probability measure and the corresponding empirical spectral distribution. The technical tool we use is the Stieltjes transform method through which we prove optimal convergence of the empirical spectral distribution of random sample covariance matrices to the deterministic Marchenko-Pastur distribution. We also give new results about the rigidity of the eigenvalues of this random sample covariance matrix and the rate of their convergence. We then define the Dyson equation method to prove new local laws about a random matrix model that interpolates between the Marchenko-Pastur distribution, the elliptical law and the circular law. Through our work these local laws can be considered universal.

math.PR

Moments approach for the elephant random walk

We discuss the method of moments for the one-dimensional elephant random walk (ERW). We first derive a differential recurrence relation for the characteristic function of the ERW, which yields a corresponding system of recurrence relations for its moments. We then obtain asymptotic approximations for the moments in each of the three parameter regimes of the ERW. Finally, by establishing the convergence of the moments and verifying the corresponding moment-determinacy conditions, we identify the limiting distributions of the ERW in each regime.

math.PR