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Esther Ezra

Publications and source records attributed to Esther Ezra.

At least 19 recordsLinked to original sources
arXiv2026

Nearly-Tight Bounds for Vertical Decomposition in Three and Four Dimensions

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 ${\mathbb R}^4$.

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arXiv2025

Intersection Queries for Flat Semi-Algebraic Objects in Three Dimensions and Related Problems

Let $\mathcal{T}$ be a set of $n$ flat (planar) semi-algebraic regions in $\mathbb{R}^3$ of constant complexity (e.g., triangles, disks), which we call plates. We wish to preprocess $\mathcal{T}$ into a data structure so that for a query object $γ$, which is also a plate, we can quickly answer various intersection queries, such as detecting whether $γ$ intersects any plate of $\mathcal{T}$, reporting all the plates intersected by $γ$, or counting them. We also consider two simpler cases of this general setting: (i) the input objects are plates and the query objects are constant-degree parametrized algebraic arcs in $\mathbb{R}^3$ (arcs, for short), or (ii) the input objects are arcs and the query objects are plates in $\mathbb{R}^3$. Besides being interesting in their own right, the data structures for these two special cases form the building blocks for handling the general case. By combining the polynomial-partitioning technique with additional tools from real algebraic geometry, we present many different data structures for intersection queries, which also provide trade-offs between their size and query time. For example, if $\mathcal{T}$ is a set of plates and the query objects are algebraic arcs, we obtain a data structure that uses $O^*(n^{4/3})$ storage (where the $O^*(\cdot)$ notation hides factors of the form $n^ε$, for an arbitrarily small $ε>0$) and answers an arc-intersection query in $O^*(n^{2/3})$ time. This result is significant since the exponents do not depend on the specific shape of the input and query objects. We generalize and slightly improve this result: for a parameter $s\in [n^{4/3}, n^{t_q}]$, where ${t_q}\ge 3$ is the number of real parameters needed to specify a query arc, the query time can be decreased to $O^*((n/s^{1/{t_q}})^{\tfrac{2/3}{1-1/{t_q}}})$ by increasing the storage to $O^*(s)$.

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arXiv2024

Semi-Algebraic Off-line Range Searching and Biclique Partitions in the Plane

Let $P$ be a set of $m$ points in ${\mathbb R}^2$, let $Σ$ be a set of $n$ semi-algebraic sets of constant complexity in ${\mathbb R}^2$, let $(S,+)$ be a semigroup, and let $w: P \rightarrow S$ be a weight function on the points of $P$. We describe a randomized algorithm for computing $w(P\capσ)$ for every $σ\inΣ$ in overall expected time $O^*\bigl( m^{\frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3} + m + n \bigr)$, where $s>0$ is a constant that bounds the maximum complexity of the regions of $Σ$, and where the $O^*(\cdot)$ notation hides subpolynomial factors. For $s\ge 3$, surprisingly, this bound is smaller than the best-known bound for answering $m$ such queries in an on-line manner. The latter takes $O^*(m^{\frac{s}{2s-1}}n^{\frac{2s-2}{2s-1}}+m+n)$ time. Let $Φ: Σ\times P \rightarrow \{0,1\}$ be the Boolean predicate (of constant complexity) such that $Φ(σ,p) = 1$ if $p\inσ$ and $0$ otherwise, and let $Σ\mathopΦ P = \{ (σ,p) \in Σ\times P \mid Φ(σ,p)=1\}$. Our algorithm actually computes a partition ${\mathcal B}_Φ$ of $Σ\mathopΦ P$ into bipartite cliques (bicliques) of size (i.e., sum of the sizes of the vertex sets of its bicliques) $O^*\bigl( m^{\frac{2s}{5s-4}}n^{\frac{5s-6}{5s-4}} + m^{2/3}n^{2/3} + m + n \bigr)$. It is straightforward to compute $w(P\capσ)$ for all $σ\in Σ$ from ${\mathcal B}_Φ$. Similarly, if $η: Σ\rightarrow S$ is a weight function on the regions of $Σ$, $\sum_{σ\in Σ: p \in σ} η(σ)$, for every point $p\in P$, can be computed from ${\mathcal B}_Φ$ in a straightforward manner. A recent work of Chan et al. solves the online version of this dual point enclosure problem within the same performance bound as our off-line solution. We also mention a few other applications of computing ${\mathcal B}_Φ$.

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arXiv2023

Vertical Decomposition in 3D and 4D with Applications to Line Nearest-Neighbor Searching in 3D

Vertical decomposition is a widely used general technique for decomposing the cells of arrangements of semi-algebraic sets in $d$-space 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 $d=3,4$: (i) Let $\mathcal{S}$ be a collection of $n$ semi-algebraic sets of constant complexity in 3D, and let $U(m)$ be an upper bound on the complexity of the union $\mathcal{U}(\mathcal{S}')$ of any subset $\mathcal{S}'\subseteq \mathcal{S}$ of size at most $m$. We prove that the complexity of the vertical decomposition of the complement of $\mathcal{U}(\mathcal{S})$ is $O^*(n^2+U(n))$ (where the $O^*(\cdot)$ notation hides subpolynomial factors). We also show that the complexity of the vertical decomposition of the entire arrangement $\mathcal{A}(\mathcal{S})$ is $O^*(n^2+X)$, where $X$ is the number of vertices in $\mathcal{A}(\mathcal{S})$. (ii) Let $\mathcal{F}$ be a collection of $n$ trivariate functions whose graphs are semi-algebraic sets of constant complexity. We show that the complexity of the vertical decomposition of the portion of the arrangement $\mathcal{A}(\mathcal{F})$ in 4D lying below the lower envelope of $\mathcal{F}$ is $O^*(n^3)$. These results lead to efficient algorithms for a variety of problems involving these decompositions, including algorithms for constructing the decompositions themselves, and for constructing $(1/r)$-cuttings of substructures of arrangements of the kinds considered above. One additional algorithm of interest is for output-sensitive point enclosure queries amid semi-algebraic sets in three or four dimensions. In addition, as a main domain of applications, we study various proximity problems involving points and lines in 3D.

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arXiv2022

Intersection Searching amid Tetrahedra in Four Dimensions

We develop data structures for intersection queries in four dimensions that involve segments, triangles and tetrahedra. Specifically, we study three main problems: (i) Preprocess a set of $n$ tetrahedra in $\reals^4$ into a data structure for answering segment-intersection queries amid the given tetrahedra (referred to as \emph{segment-tetrahedron intersection queries}). (ii) Preprocess a set of $n$ triangles in $\reals^4$ into a data structure that supports triangle-intersection queries amid the input triangles (referred to as \emph{triangle-triangle intersection queries}). (iii) Preprocess a set of $n$ segments in $\reals^4$ into a data structure that supports tetrahedron-intersection queries amid the input segments (referred to as \emph{tetrahedron-segment intersection queries}). In each problem we want either to detect an intersection, or to count or report all intersections. As far as we can tell, these problems have not been previously studied. For problem (i), we first present a "standard" solution which, for any prespecified value $n \le s \le n^6$ of a so-called storage parameter $s$, yields a data structure with $O^*(s)$ storage and expected preprocessing, which answers an intersection query in $O^*(n/s^{1/6})$ time (here and in what follows, the $O^*(\cdot)$ notation hides subpolynomial factors). For problems (ii) and (iii), using similar arguments, we present a solution that has the same asymptotic performance bounds. We then improve the solution for problem (i), and present a more intricate data structure that uses $O^*(n^{2})$ storage and expected preprocessing, and answers a segment-tetrahedron intersection query in $O^*(n^{1/2})$ time.

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arXiv2021

Subquadratic Algorithms for Some \textsc{3Sum}-Hard Geometric Problems in the Algebraic Decision Tree Model

We present subquadratic algorithms in the algebraic decision-tree model for several \textsc{3Sum}-hard geometric problems, all of which can be reduced to the following question: Given two sets $A$, $B$, each consisting of $n$ pairwise disjoint segments in the plane, and a set $C$ of $n$ triangles in the plane, we want to count, for each triangle $Δ\in C$, the number of intersection points between the segments of $A$ and those of $B$ that lie in $Δ$. The problems considered in this paper have been studied by Chan~(2020), who gave algorithms that solve them, in the standard real-RAM model, in $O((n^2/\log^2n)\log^{O(1)}\log n)$ time. We present solutions in the algebraic decision-tree model whose cost is $O(n^{60/31+\varepsilon})$, for any $\varepsilon>0$. Our approach is based on a primal-dual range searching mechanism, which exploits the multi-level polynomial partitioning machinery recently developed by Agarwal, Aronov, Ezra, and Zahl~(2020). A key step in the procedure is a variant of point location in arrangements, say of lines in the plane, which is based solely on the \emph{order type} of the lines, a "handicap" that turns out to be beneficial for speeding up our algorithm.

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arXiv2021

On Ray Shooting for Triangles in 3-Space and Related Problems

We consider several problems that involve lines in three dimensions, and present improved algorithms for solving them. The problems include (i) ray shooting amid triangles in $R^3$, (ii) reporting intersections between query lines (segments, or rays) and input triangles, as well as approximately counting the number of such intersections, (iii) computing the intersection of two nonconvex polyhedra, (iv) detecting, counting, or reporting intersections in a set of lines in $R^3$, and (v) output-sensitive construction of an arrangement of triangles in three dimensions. Our approach is based on the polynomial partitioning technique. For example, our ray-shooting algorithm processes a set of $n$ triangles in $R^3$ into a data structure for answering ray shooting queries amid the given triangles, which uses $O(n^{3/2+\varepsilon})$ storage and preprocessing, and answers a query in $O(n^{1/2+\varepsilon})$ time, for any $\varepsilon>0$. This is a significant improvement over known results, obtained more than 25 years ago, in which, with this amount of storage, the query time bound is roughly $n^{5/8}$. The algorithms for the other problems have similar performance bounds, with similar improvements over previous results. We also derive a nontrivial improved tradeoff between storage and query time. Using it, we obtain algorithms that answer $m$ queries on $n$ objects in \[ \max \left\{ O(m^{2/3}n^{5/6+\varepsilon} + n^{1+\varepsilon}),\; O(m^{5/6+\varepsilon}n^{2/3} + m^{1+\varepsilon}) \right\} \] time, for any $\varepsilon>0$, again an improvement over the earlier bounds.

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arXiv2021

An Efficient Algorithm for Generalized Polynomial Partitioning and Its Applications

In 2015, Guth proved that if $S$ is a collection of $n$ $g$-dimensional semi-algebraic sets in $\mathbb{R}^d$ and if $D\geq 1$ is an integer, then there is a $d$-variate polynomial $P$ of degree at most $D$ so that each connected component of $\mathbb{R}^d\setminus Z(P)$ intersects $O(n/D^{d-g})$ sets from $S$. Such a polynomial is called a generalized partitioning polynomial. We present a randomized algorithm that computes such polynomials efficiently -- the expected running time of our algorithm is linear in $|S|$. Our approach exploits the technique of quantifier elimination combined with that of $ε$-samples. We also present an extension of our construction to multi-level polynomial partitioning for semi-algebraic sets in $\mathbb{R}^d$. We present five applications of our result. The first is a data structure for answering point-enclosure queries among a family of semi-algebraic sets in $\mathbb{R}^d$ in $O(\log n)$ time, with storage complexity and expected preprocessing time of $O(n^{d+ε})$. The second is a data structure for answering range-searching queries with semi-algebraic ranges in $\mathbb{R}^d$ in $O(\log n)$ time, with $O(n^{t+ε})$ storage and expected preprocessing time, where $t > 0$ is an integer that depends on $d$ and the description complexity of the ranges. The third is a data structure for answering vertical ray-shooting queries among semi-algebraic sets in $\mathbb{R}^{d}$ in $O(\log^2 n)$ time, with $O(n^{d+ε})$ storage and expected preprocessing time. The fourth is an efficient algorithm for cutting algebraic curves in $\mathbb{R}^2$ into pseudo-segments. The fifth application is for eliminating depth cycles among triangles in $\mathbb{R}^3$, where we show a nearly-optimal algorithm to cut $n$ pairwise disjoint non-vertical triangles in $\mathbb{R}^3$ into pieces that form a depth order.

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arXiv2020

On rich lenses in planar arrangements of circles and related problems

We show that the maximum number of pairwise non-overlapping $k$-rich lenses (lenses formed by at least $k$ circles) in an arrangement of $n$ circles in the plane is $O\left(\frac{n^{3/2}\log{(n/k^3)}}{k^{5/2}} + \frac{n}{k} \right)$, and the sum of the degrees of the lenses of such a family (where the degree of a lens is the number of circles that form it) is $O\left(\frac{n^{3/2}\log{(n/k^3)}}{k^{3/2}} + n\right)$. Two independent proofs of these bounds are given, each interesting in its own right (so we believe). We then show that these bounds lead to the known bound of Agarwal et al. (JACM 2004) and Marcus and Tardos (JCTA 2006) on the number of point-circle incidences in the plane. Extensions to families of more general algebraic curves and some other related problems are also considered.

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arXiv2020

Testing Polynomials for Vanishing on Cartesian Products of Planar Point Sets: Collinearity Testing and Related Problems

We present subquadratic algorithms, in the algebraic decision-tree model of computation, for detecting whether there exists a triple of points, belonging to three respective sets $A$, $B$, and $C$ of points in the plane, that satisfy a certain polynomial equation or two equations. The best known instance of such a problem is testing for the existence of a collinear triple of points in $A\times B\times C$, a classical 3SUM-hard problem that has so far defied any attempt to obtain a subquadratic solution, whether in the (uniform) real RAM model, or in the algebraic decision-tree model. While we are still unable to solve this problem, in full generality, in subquadratic time, we obtain such a solution, in the algebraic decision-tree model, that uses only roughly $O(n^{28/15})$ constant-degree polynomial sign tests, for the special case where two of the sets lie on two respective one-dimensional curves and the third is placed arbitrarily in the plane. Our technique is fairly general, and applies to many other problems where we seek a triple that satisfies a single polynomial equation, e.g., determining whether $A\times B\times C$ contains a triple spanning a unit-area triangle. This result extends recent work by Barba \etal~(2017) and by Chan (2018), where all three sets $A$,~$B$, and~$C$ are assumed to be one-dimensional. As a second application of our technique, we again have three $n$-point sets $A$, $B$, and $C$ in the plane, and we want to determine whether there exists a triple $(a,b,c) \in A\times B\times C$ that simultaneously satisfies two independent real polynomial equations. For example, this is the setup when testing for collinearity in the complex plane, when each of the sets $A$, $B$, $C$ lies on some constant-degree algebraic curve. We show that problems of this kind can be solved with roughly $O(n^{24/13})$ constant-degree polynomial sign tests.

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arXiv2020

Constructive Polynomial Partitioning for Algebraic Curves in $\mathbb{R}^3$ with Applications

In 2015, Guth proved that for any set of $k$-dimensional bounded complexity varieties in $\mathbb{R}^d$ and for any positive integer $D$, there exists a polynomial of degree at most $D$ whose zero set divides $\mathbb{R}^d$ into open connected sets, so that only a small fraction of the given varieties intersect each of these sets. Guth's result generalized an earlier result of Guth and Katz for points. Guth's proof relies on a variant of the Borsuk-Ulam theorem, and for $k>0$, it is unknown how to obtain an explicit representation of such a partitioning polynomial and how to construct it efficiently. In particular, it is unknown how to effectively construct such a polynomial for bounded-degree algebraic curves (or even lines) in $\mathbb{R}^3$. We present an efficient algorithmic construction for this setting. Given a set of $n$ input algebraic curves and a positive integer $D$, we efficiently construct a decomposition of space into $O(D^3\log^3{D})$ open "cells," each of which meets $O(n/D^2)$ curves from the input. The construction time is $O(n^2)$. For the case of lines in $3$-space we present an improved implementation, whose running time is $O(n^{4/3} \log^{O(1)} n)$. The constant of proportionality in both time bounds depends on $D$ and the maximum degree of the polynomials defining the input curves. As an application, we revisit the problem of eliminating depth cycles among non-vertical lines in $3$-space, recently studied by Aronov and Sharir (2018), and show an algorithm that cuts $n$ such lines into $O(n^{3/2+ε})$ pieces that are depth-cycle free, for any $ε> 0$. The algorithm runs in $O(n^{3/2+ε})$ time, which is a considerable improvement over the previously known algorithms.

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arXiv2018

On Pseudo-disk Hypergraphs

Let $F$ be a family of pseudo-disks in the plane, and $P$ be a finite subset of $F$. Consider the hypergraph $H(P,F)$ whose vertices are the pseudo-disks in $P$ and the edges are all subsets of $P$ of the form $\{D \in P \mid D \cap S \neq \emptyset\}$, where $S$ is a pseudo-disk in $F$. We give an upper bound of $O(nk^3)$ for the number of edges in $H(P,F)$ of cardinality at most $k$. This generalizes a result of Buzaglo et al. (2013). As an application of our bound, we obtain an algorithm that computes a constant-factor approximation to the smallest _weighted_ dominating set in a collection of pseudo-disks in the plane, in expected polynomial time.

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arXiv2017

Decomposing arrangements of hyperplanes: VC-dimension, combinatorial dimension, and point location

$\renewcommand{\Re}{\mathbb{R}}$ We re-examine parameters for the two main space decomposition techniques---bottom-vertex triangulation, and vertical decomposition, including their explicit dependence on the dimension $d$, and discover several unexpected phenomena, which show that, in both techniques, there are large gaps between the VC-dimension (and primal shatter dimension), and the combinatorial dimension. For vertical decomposition, the combinatorial dimension is only $2d$, the primal shatter dimension is at most $d(d+1)$, and the VC-dimension is at least $1 + d(d+1)/2$ and at most $O(d^3)$. For bottom-vertex triangulation, both the primal shatter dimension and the combinatorial dimension are $Θ(d^2)$, but there seems to be a significant gap between them, as the combinatorial dimension is $\frac12d(d+3)$, whereas the primal shatter dimension is at most $d(d+1)$, and the VC-dimension is between $d(d+1)$ and $5d^2 \log{d}$ (for $d\ge 9$). Our main application is to point location in an arrangement of $n$ hyperplanes is $\Re^d$, in which we show that the query cost in Meiser's algorithm can be improved if one uses vertical decomposition instead of bottom-vertex triangulation, at the cost of some increase in the preprocessing cost and storage. The best query time that we can obtain is $O(d^3\log n)$, instead of $O(d^4\log d\log n)$ in Meiser's algorithm. For these bounds to hold, the preprocessing and storage are rather large (super-exponential in $d$). We discuss the tradeoff between query cost and storage (in both approaches, the one using bottom-vertex trinagulation and the one using vertical decomposition).

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arXiv2015

On the Beck-Fiala Conjecture for Random Set Systems

Motivated by the Beck-Fiala conjecture, we study discrepancy bounds for random sparse set systems. Concretely, these are set systems $(X,Σ)$, where each element $x \in X$ lies in $t$ randomly selected sets of $Σ$, where $t$ is an integer parameter. We provide new bounds in two regimes of parameters. We show that when $|Σ| \ge |X|$ the hereditary discrepancy of $(X,Σ)$ is with high probability $O(\sqrt{t \log t})$; and when $|X| \gg |Σ|^t$ the hereditary discrepancy of $(X,Σ)$ is with high probability $O(1)$. The first bound combines the Lov{á}sz Local Lemma with a new argument based on partial matchings; the second follows from an analysis of the lattice spanned by sparse vectors.

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arXiv2014

Shallow Packings in Geometry

We refine the bound on the packing number, originally shown by Haussler, for shallow geometric set systems. Specifically, let $\V$ be a finite set system defined over an $n$-point set $X$; we view $\V$ as a set of indicator vectors over the $n$-dimensional unit cube. A $δ$-separated set of $\V$ is a subcollection $\W$, s.t. the Hamming distance between each pair $\uu, \vv \in \W$ is greater than $δ$, where $δ> 0$ is an integer parameter. The $δ$-packing number is then defined as the cardinality of the largest $δ$-separated subcollection of $\V$. Haussler showed an asymptotically tight bound of $Θ((n/δ)^d)$ on the $δ$-packing number if $\V$ has VC-dimension (or \emph{primal shatter dimension}) $d$. We refine this bound for the scenario where, for any subset, $X' \subseteq X$ of size $m \le n$ and for any parameter $1 \le k \le m$, the number of vectors of length at most $k$ in the restriction of $\V$ to $X'$ is only $O(m^{d_1} k^{d-d_1})$, for a fixed integer $d > 0$ and a real parameter $1 \le d_1 \le d$ (this generalizes the standard notion of \emph{bounded primal shatter dimension} when $d_1 = d$). In this case when $\V$ is "$k$-shallow" (all vector lengths are at most $k$), we show that its $δ$-packing number is $O(n^{d_1} k^{d-d_1}/δ^d)$, matching Haussler's bound for the special cases where $d_1=d$ or $k=n$. As an immediate consequence we conclude that set systems of halfspaces, balls, and parallel slabs defined over $n$ points in $d$-space admit better packing numbers when $k$ is smaller than $n$. Last but not least, we describe applications to (i) spanning trees of low total crossing number, and (ii) geometric discrepancy, based on previous work by the author.

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arXiv2013

A Size-Sensitive Discrepancy Bound for Set Systems of Bounded Primal Shatter Dimension

Let $(X,§)$ be a set system on an $n$-point set $X$. The \emph{discrepancy} of $§$ is defined as the minimum of the largest deviation from an even split, over all subsets of $S \in §$ and two-colorings $χ$ on $X$. We consider the scenario where, for any subset $X' \subseteq X$ of size $m \le n$ and for any parameter $1 \le k \le m$, the number of restrictions of the sets of $§$ to $X'$ of size at most $k$ is only $O(m^{d_1} k^{d-d_1})$, for fixed integers $d > 0$ and $1 \le d_1 \le d$ (this generalizes the standard notion of \emph{bounded primal shatter dimension} when $d_1 = d$). In this case we show that there exists a coloring $χ$ with discrepancy bound $O^{*}(|S|^{1/2 - d_1/(2d)} n^{(d_1 - 1)/(2d)})$, for each $S \in §$, where $O^{*}(\cdot)$ hides a polylogarithmic factor in $n$. This bound is tight up to a polylogarithmic factor \cite{Mat-95, Mat-99} and the corresponding coloring $χ$ can be computed in expected polynomial time using the very recent machinery of Lovett and Meka for constructive discrepancy minimization \cite{LM-12}. Our bound improves and generalizes the bounds obtained from the machinery of Har-Peled and Sharir \cite{HS-11} (and the follow-up work in \cite{SZ-12}) for points and halfspaces in $d$-space for $d \ge 3$.

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arXiv2012

Convex Hull of Points Lying on Lines in o(n log n) Time after Preprocessing

Motivated by the desire to cope with data imprecision, we study methods for taking advantage of preliminary information about point sets in order to speed up the computation of certain structures associated with them. In particular, we study the following problem: given a set L of n lines in the plane, we wish to preprocess L such that later, upon receiving a set P of n points, each of which lies on a distinct line of L, we can construct the convex hull of P efficiently. We show that in quadratic time and space it is possible to construct a data structure on L that enables us to compute the convex hull of any such point set P in O(n alpha(n) log* n) expected time. If we further assume that the points are "oblivious" with respect to the data structure, the running time improves to O(n alpha(n)). The analysis applies almost verbatim when L is a set of line-segments, and yields similar asymptotic bounds. We present several extensions, including a trade-off between space and query time and an output-sensitive algorithm. We also study the "dual problem" where we show how to efficiently compute the (<= k)-level of n lines in the plane, each of which lies on a distinct point (given in advance). We complement our results by Omega(n log n) lower bounds under the algebraic computation tree model for several related problems, including sorting a set of points (according to, say, their x-order), each of which lies on a given line known in advance. Therefore, the convex hull problem under our setting is easier than sorting, contrary to the "standard" convex hull and sorting problems, in which the two problems require Theta(n log n) steps in the worst case (under the algebraic computation tree model).

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