Micha Sharir

Jean Cardinal, Micha Sharir

Semialgebraic graphs are graphs whose vertices are points in $\mathbb{R}^d$, and adjacency between two vertices is determined by the truth value of a semialgebraic predicate of constant complexity. We show how to harness polynomial partitioning methods to construct compact adjacency labeling schemes for families of semialgebraic graphs. That is, we show that for any family of semialgebraic graphs, given a graph on $n$ vertices in this family, we can assign a label consisting of $O(n^{1-2/(d+1) + \varepsilon})$ bits to each vertex (where $\varepsilon > 0$ can be made arbitrarily small and the constant of proportionality depends on $\varepsilon$ and on the complexity of the adjacency-defining predicate), such that adjacency between two vertices can be determined solely from their two labels, without any additional information. We obtain for instance that unit disk graphs and segment intersection graphs have such labelings with labels of $O(n^{1/3 + \varepsilon})$ bits. This is in contrast to their natural implicit representation consisting of the coordinates of the disk centers or segment endpoints, which sometimes require exponentially many bits. It also improves on the best known bound of $O(n^{1-1/d}\log n)$ for $d$-dimensional semialgebraic families due to Alon (Discrete Comput. Geom., 2024), a bound that holds more generally for graphs with shattering functions bounded by a degree-$d$ polynomial. We also give new bounds on the size of adjacency labels for other families of graphs. In particular, we consider semilinear graphs, which are semialgebraic graphs in which the predicate only involves linear polynomials. We show that semilinear graphs have adjacency labels of size $O(\log n)$. We also prove that polygon visibility graphs, which are not semialgebraic in the above sense, have adjacency labels of size $O(\log^3 n)$.

Pankaj K. Agarwal, Matthew J. Katz, Micha Sharir

Let $K$ be a compact, centrally-symmetric, strictly-convex region in ${\mathbb R}^3$, which is a semi-algebraic set of constant complexity, i.e. the unit ball of a corresponding metric, denoted as $\|\cdot\|_K$. Let ${\mathcal{K}}$ be a set of $n$ homothetic copies of $K$. This paper contains two main sets of results: (i) For a storage parameter $s\in[n,n^3]$, ${\mathcal{K}}$ can be preprocessed in $O^*(s)$ expected time into a data structure of size $O^*(s)$, so that for a query homothet $K_0$ of $K$, an intersection-detection query (determine whether $K_0$ intersects any member of ${\mathcal{K}}$, and if so, report such a member) or a nearest-neighbor query (return the member of ${\mathcal{K}}$ whose $\|\cdot\|_K$-distance from $K_0$ is smallest) can be answered in $O^*(n/s^{1/3})$ time; all $k$ homothets of ${\mathcal{K}}$ intersecting $K_0$ can be reported in additional $O(k)$ time. In addition, the data structure supports insertions/deletions in $O^*(s/n)$ amortized expected time per operation. Here the $O^*(\cdot)$ notation hides factors of the form $n^\varepsilon$, where $\varepsilon>0$ is an arbitrarily small constant, and the constant of proportionality depends on $\varepsilon$. (ii) Let $\mathcal{G}(\mathcal{K})$ denote the intersection graph of ${\mathcal{K}}$. Using the above data structure, breadth-first or depth-first search on $\mathcal{G}(\mathcal{K})$ can be performed in $O^*(n^{3/2})$ expected time. Combining this result with the so-called shrink-and-bifurcate technique, the reverse-shortest-path problem in a suitably defined proximity graph of ${\mathcal{K}}$ can be solved in $O^*(n^{62/39})$ expected time. Dijkstra's shortest-path algorithm, as well as Prim's MST algorithm, on a $\|\cdot\|_K$-proximity graph on $n$ points in ${\mathbb R}^3$, with edges weighted by $\|\cdot\|_K$, can also be performed in $O^*(n^{3/2})$ time.

Pankaj K. Agarwal, Esther Ezra, Micha Sharir

Vertical decomposition is a widely used general technique for decomposing the cells of arrangements of semi-algebraic sets in ${\mathbb R}^d$ into constant-complexity subcells. In this paper, we settle in the affirmative a few long-standing open problems involving the vertical decomposition of substructures of arrangements for $d = 3, 4$. For example, we obtain sharp bounds on the complexity of the vertical decomposition of the complement of the union of a set of semi-algebraic regions of constant complexity in ${\mathbb R}^3$, and of the minimization diagram of a set of trivariate functions. These results lead to efficient algorithms for a variety of problems involving vertical decompositions, including algorithms for constructing the decompositions themselves and for constructing $(1/r)$-cuttings of substructures of arrangements. They also lead to a data structure for answering point-enclosure queries amid semi-algebraic sets in ${\mathbb R}^3$ and ${\mathbb R}^4$.

Haim Kaplan, Micha Sharir, Uri Stemmer

We study the question of how to compute a point in the convex hull of an input set $S$ of $n$ points in ${\mathbb R}^d$ in a differentially private manner. This question, which is trivial non-privately, turns out to be quite deep when imposing differential privacy. In particular, it is known that the input points must reside on a fixed finite subset $G\subseteq{\mathbb R}^d$, and furthermore, the size of $S$ must grow with the size of $G$. Previous works focused on understanding how $n$ needs to grow with $|G|$, and showed that $n=O\left(d^{2.5}\cdot8^{\log^*|G|}\right)$ suffices (so $n$ does not have to grow significantly with $|G|$). However, the available constructions exhibit running time at least $|G|^{d^2}$, where typically $|G|=X^d$ for some (large) discretization parameter $X$, so the running time is in fact $Ω(X^{d^3})$. In this paper we give a differentially private algorithm that runs in $O(n^d)$ time, assuming that $n=Ω(d^4\log X)$. To get this result we study and exploit some structural properties of the Tukey levels (the regions $D_{\ge k}$ consisting of points whose Tukey depth is at least $k$, for $k=0,1,...$). In particular, we derive lower bounds on their volumes for point sets $S$ in general position, and develop a rather subtle mechanism for handling point sets $S$ in degenerate position (where the deep Tukey regions have zero volume). A naive approach to the construction of the Tukey regions requires $n^{O(d^2)}$ time. To reduce the cost to $O(n^d)$, we use an approximation scheme for estimating the volumes of the Tukey regions (within their affine spans in case of degeneracy), and for sampling a point from such a region, a scheme that is based on the volume estimation framework of Lovász and Vempala (FOCS 2003) and of Cousins and Vempala (STOC 2015). Making this framework differentially private raises a set of technical challenges that we address.