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Sharareh Alipour

Publications and source records attributed to Sharareh Alipour.

At least 19 recordsLinked to original sources

Extremal List Gaps and Inapproximability in Additive Graph Labeling

We study a vertex-labeling analogue of the $1$-$2$-$3$ problem and its list version. For a labeling $\ell:V(G)\to\mathbb N$, let $S_\ell(v)=\sum_{w\in N(v)}\ell(w)$. The additive number $η(G)$ is the least $k$ for which there exists $\ell:V(G)\to[k]$ such that $S_\ell(u)\ne S_\ell(v)$ for every $uv\in E(G)$, while the list additive number $η_\ell(G)$ is the least $k$ such that the same condition can be satisfied from every assignment of $k$-element lists $L(v)\subset\mathbb N$ with $\ell(v)\in L(v)$. We show that for every $k\ge2$, there is a graph $G$ with $η(G)=1$ and $η_\ell(G)\ge k$. The separation persists at the minimum possible ordinary value for positive-degree regular graphs: there is a regular graph $H$ with $η(H)=2$ and $η_\ell(H)\ge k$. We also determine a sharp lower bound for $η(G)$ in terms of the order and minimum degree of $G$, and show that the unbounded list gap persists at asymptotically extremal density. Finally, for every fixed $k\ge2$, it is NP-hard to distinguish $η(G)=2$ from $η(G)>k$, even on asymptotically extremal dense graphs. Consequently, $η(G)$ admits no polynomial-time constant-factor approximation unless $\mathrm P=\mathrm{NP}$. Together, these results reveal a robust gap phenomenon: the separation between ordinary and list additive labeling persists at the smallest possible ordinary values and even under asymptotically extremal density, while the ordinary parameter itself remains hard to approximate.

math.CO

Distributed Dominating Set With Optimal Rounds and Message Size in Bounded Arboricity Graphs

We study the distributed minimum dominating set problem on graphs of arboricity $α$. Dory, Ghaffari, and Ilchi [PODC'22] showed that any algorithm achieving a constant or poly-logarithmic approximation factor needs at least $Ω(\logΔ/\log\logΔ)$ rounds in graphs of maximum degree $Δ$ and arboricity $α$, even when $α=2$ and even when the message sizes are unbounded. Although there is a variety of algorithms with a near-optimal round complexity of $O(\logΔ)$, it is natural to ask: What is the best approximation factor in the optimal round complexity of $O(\logΔ/\log\logΔ)$? We make progress in answering this question by describing a deterministic algorithm that obtains a $O\left( α\log Δ/ \log\log Δ\right)$ approximation without prior knowledge of $α$ with optimal round complexity of $O\left( \log Δ/ \log\log Δ\right)$ and optimal message size of $1$ bit per round. Among all of the previous results, the only algorithm that achieves the optimal round complexity of $O\left( \log Δ/ \log\log Δ\right)$ without prior knowledge of $α$ is due to Lenzen and Wattenhofer [DISC'10] that obtains a $O(α\log^{1+\varepsilon}Δ/ (\varepsilon\log\log Δ))$ approximation in $O(\logΔ/(\varepsilon\log\logΔ))$ rounds and $O(\log(\varepsilon^{-1}\logΔ))$ message size. Our algorithm simplifies and improves upon this result. The only downside of our algorithm compared to the algorithm of Lenzen and Wattenhofer is that it needs prior knowledge of $Δ$. The previous state-of-the-art algorithm by Dory, Ghaffari, and Ilchi [PODC'22] has a dependency on $\log n$ in the round complexity for unknown $α$, which is far from optimal.

cs.DS

Robust Multiagent Collaboration Through Weighted Max-Min T-Joins

Many multiagent tasks -- such as reviewer assignment, coalition formation, or fair resource allocation -- require selecting a group of agents such that collaboration remains effective even in the worst case. The \emph{weighted max-min $T$-join problem} formalizes this challenge by seeking a subset of vertices whose minimum-weight matching is maximized, thereby ensuring robust outcomes against unfavorable pairings. We advance the study of this problem in several directions. First, we design an algorithm that computes an upper bound for the \emph{weighted max-min $2k$-matching problem}, where the chosen set must contain exactly $2k$ vertices. Building on this bound, we develop a general algorithm with a \emph{$2 \ln n$-approximation guarantee} that runs in $O(n^4)$ time. Second, using ear decompositions, we propose another upper bound for the weighted max-min $T$-join cost. We also show that the problem can be solved exactly when edge weights belong to $\{1,2\}$. Finally, we evaluate our methods on real collaboration datasets. Experiments show that the lower bounds from our approximation algorithm and the upper bounds from the ear decomposition method are consistently close, yielding empirically small constant-factor approximations. Overall, our results highlight both the theoretical significance and practical value of weighted max-min $T$-joins as a framework for fair and robust group formation in multiagent systems.

cs.DS

Improved Wake-Up Time For Euclidean Freeze-Tag Problem

The Freeze-Tag Problem (FTP) involves activating a set of initially asleep robots as quickly as possible, starting from a single awake robot. Once activated, a robot can assist in waking up other robots. Each active robot moves at unit speed. The objective is to minimize the makespan, i.e., the time required to activate the last robot. A key performance measure is the wake-up ratio, defined as the maximum time needed to activate any number of robots in any primary positions. This work focuses on the geometric (Euclidean) version of FTP in $\mathbb{R}^d$ under the $\ell_p$ norm, where the initial distance between each asleep robot and the single active robot is at most 1. For $(\mathbb{R}^2, \ell_2)$, we improve the previous upper bound of 4.62 ([7], CCCG 2024) to 4.31. Note that it is known that 3.82 is a lower bound for the wake-up ratio. In $\mathbb{R}^3$, we propose a new strategy that achieves a wake-up ratio of 12 for $(\mathbb{R}^3, \ell_1)$ and 12.76 for $(\mathbb{R}^3, \ell_2)$, improving upon the previous bounds of 13 and $13\sqrt{3}$, respectively, reported in [2].

cs.CG

Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective

Integrating large language models (LLMs) like ChatGPT into computer science education offers transformative potential for complex courses such as data structures and algorithms (DSA). This study examines ChatGPT as a supplementary tool for teaching assistants (TAs), guided by structured prompts and human oversight, to enhance instruction and student outcomes. A controlled experiment compared traditional TA-led instruction with a hybrid approach where TAs used ChatGPT-4o and ChatGPT o1 to generate exercises, clarify concepts, and provide feedback. Structured prompts emphasized problem decomposition, real-world context, and code examples, enabling tailored support while mitigating over-reliance on AI. Results demonstrated the hybrid approach's efficacy, with students in the ChatGPT-assisted group scoring 16.50 points higher on average and excelling in advanced topics. However, ChatGPT's limitations necessitated TA verification. This framework highlights the dual role of LLMs: augmenting TA efficiency while ensuring accuracy through human oversight, offering a scalable solution for human-AI collaboration in education.

cs.HC

Geometric Freeze-Tag Problem

We study the Freeze-Tag Problem (FTP), introduced by Arkin et al. (SODA'02), where the goal is to wake up a group of $n$ robots, starting from a single active robot. Our focus is on the geometric version of the problem, where robots are positioned in $\mathbb{R}^d$, and once activated, a robot can move at a constant speed to wake up others. The objective is to minimize the time it takes to activate the last robot, also known as the makespan. We present new upper bounds for the $l_1$ and $l_2$ norms in $\mathbb{R}^2$ and $\mathbb{R}^3$. For $(\mathbb{R}^2, l_2)$, we achieve a makespan of at most $5.4162r$, improving on the previous bound of $7.07r$ by Bonichon et al. (DISC'24). In $(\mathbb{R}^3, l_1)$, we establish an upper bound of $13r$, which leads to a bound of $22.52r$ for $(\mathbb{R}^3, l_2)$. Here, $r$ denotes the maximum distance of a robot from the initially active robot under the given norm. To the best of our knowledge, these are the first known bounds for the makespan in $\mathbb{R}^3$ under these norms. We also explore the FTP in $(\mathbb{R}^3, l_2)$ for specific instances where robots are positioned on a boundary, providing further insights into practical scenarios.

cs.DC

Improved Approximation Algorithms for (1,2)-TSP and Max-TSP Using Path Covers in the Semi-Streaming Model

We investigate semi-streaming algorithms for the Traveling Salesman Problem (TSP). Specifically, we focus on a variant known as the $(1,2)$-TSP, where the distances between any two vertices are either one or two. Our primary emphasis is on the closely related Maximum Path Cover Problem, which aims to find a collection of vertex-disjoint paths that cover the maximum number of edges in a graph. We propose an algorithm that, for any $ε> 0$, achieves a $(\frac{2}{3}-ε)$-approximation of the maximum path cover size for an $n$-vertex graph, using $\text{poly}(\frac{1}ε)$ passes. This result improves upon the previous $\frac{1}{2}$-approximation by Behnezhad et al. [ICALP 2024] in the semi-streaming model. Building on this result, we design a semi-streaming algorithm that constructs a tour for an instance of $(1,2)$-TSP with an approximation factor of $(\frac{4}{3} + ε)$, improving upon the previous $\frac{3}{2}$-approximation actor algorithm by Behnezhad et al. [ICALP 2024] (Although it is not explicitly stated in the paper that their algorithm works in the semi-streaming model, it is easy to verify). Furthermore, we extend our approach to develop an approximation algorithm for the Maximum TSP (Max-TSP), where the goal is to find a Hamiltonian cycle with the maximum possible weight in a given weighted graph $G$. Our algorithm provides a $(\frac{7}{12} - ε)$-approximation for Max-TSP in $\text{poly}(\frac{1}ε)$ passes, improving on the previously known $(\frac{1}{2}-ε)$-approximation obtained via maximum weight matching in the semi-streaming model.

cs.DS

On the Ratio of Shannon Numbers of Graphs

Let $Γ$ be a function that maps two arbitrary graphs $G$ and $H$ to a non-negative real number such that $$α(G^{\boxtimes n})\leq α(H^{\boxtimes n})Γ(G,H)^n$$ where $n$ is any natural number and $G^{\boxtimes n}$ is the strong product of $G$ with itself $n$ times. We establish the equivalence of two different approaches for finding such a function $Γ$. The common solution obtained through either approach is termed ``the relative fractional independence number of a graph $G$ with respect to another graph $H$". We show this function by $α^*(G|H)$ and discuss some of its properties. In particular, we show that $α^*(G|H)\geq \frac{X(G)}{X(H)} \geq \frac{1}{α^*(H|G)},$ where $X(G)$ can be the independence number, the Shannon capacity, the fractional independence number, the Lovász number, or the Schrijver's or Szegedy's variants of the Lovász number of a graph $G$. This inequality is the first explicit non-trivial upper bound on the ratio of the invariants of two arbitrary graphs, as mentioned earlier, which can also be used to obtain upper or lower bounds for these invariants. As explicit applications, we present new upper bounds for the ratio of the Shannon capacity of two Cayley graphs and compute new lower bounds on the Shannon capacity of certain Johnson graphs (yielding the exact value of their Haemers number). Moreover, we show that $α^*(G|H)$ can be used to present a stronger version of the well-known No-Homomorphism Lemma.

math.CO

On Planar Visibility Counting Problem

For a set $S$ of $n$ disjoint line segments in $\mathbb{R}^{2}$, the visibility counting problem is to preprocess $S$ such that the number of visible segments in $S$ from any query point $p$ can be computed quickly. There have been approximation algorithms for this problem with trade off between space and query time. We propose a new randomized algorithm to compute the exact answer of the problem. For any $0<α<1$, the space, preprocessing time and query time are $O_ε(n^{4-4α})$, $O_ε(n^{4-2α})$ and $O_ε(n^{2α})$, respectively. Where $O_ε(f(n)) = O(f(n)n^ε)$ and $ε>0$ is an arbitrary constant number.

cs.CG

On distributed algorithms for minimum dominating set problem and beyond

In this paper, we study the minimum dominating set (MDS) problem and the minimum total dominating set MTDS) problem which have many applications in real world. We propose a new idea to compute approximate MDS and MTDS. Next, we give an upper bound on the size of MDS of a graph. We also present a distributed randomized algorithm that produces a (total) dominating subset of a given graph whose expected size equals the upper bound. Next, we give fast distributed algorithms for computing approximated solutions for the MDS and MTDS problems using our theoretical results. The MDS problem arises in diverse areas, for example in social networks, wireless networks, robotics, and etc. Most often, we need to compute MDS in a distributed or parallel model. So we implement our algorithm on massive networks and compare our results with the state of the art algorithms to show the efficiency of our proposed algorithms in practice. We also show how to extend our idea to propose algorithms for solving $k$-dominating set problem and set cover problem. Our algorithms can also handle the case where the network is dynamic or in the case where we have constraints in choosing the elements of MDS.

cs.DC

On guarding polygons with holes

There is an old conjecture by Shermer \cite{sher} that in a polygon with $n$ vertices and $h$ holes, $\lfloor \dfrac{n+h}{3} \rfloor$ vertex guards are sufficient to guard the entire polygon. The conjecture is proved for $h=1$ by Shermer \cite{sher} and Aggarwal \cite{aga} seperately. In this paper, we prove a theorem similar to the Shermer's conjecture for a special case where the goal is to guard the vertices of the polygon (not the entire polygon) which is equivalent to finding a dominating set for the visibility graph of the polygon. Our proof also guarantees that the selected vertex guards also cover the entire outer boundary (outer perimeter of the polygon) as well.

cs.CG

On Distributed Algorithms for Minimum Dominating Set problem, from theory to application

In this paper, we propose a distributed algorithm for the minimum dominating set problem. For some especial networks, we prove theoretically that the achieved answer by our proposed algorithm is a constant approximation factor of the exact answer. This problem arises naturally in social networks, for example in news spreading, avoiding rumor spreading and recommendation spreading. So we implement our algorithm on massive social networks and compare our results with the state of the art algorithms. Also, we extend our algorithm to solve the $k$-distance dominating set problem and experimentally study the efficiency of the proposed algorithm. Our proposed algorithm is fast and easy to implement and can be used in dynamic networks where the edges and vertices are added or deleted constantly. More importantly, based on the experimental results the proposed algorithm has reasonable solutions and running time which enables us to use it in distributed model practically.

cs.DC

Hardness of Segment Cover, Contiguous SAT and Visibility with Uncertain Obstacles

We define the problem segment cover as follows. We are given a set of pairs of sub-intervals of the unit interval. The problem asks if there is a choice of a single interval from each pair such that the union of the chosen intervals covers the entire unit interval. This problem arises naturally while attempting to compute visibility between a point and a line segment in the plane in the presence of uncertain obstacles. Segment cover is equivalent to a restricted version of SAT which we call contiguous SAT. Consider a SAT with the following restrictions. An input formula is in CNF form and an ordering of the clauses is given in which clauses containing any fixed literal appear contiguously. We call this restricted problem contiguous SAT. Our main result is that the problems segment cover and contiguous SAT are NP-hard. We also discuss hardness of approximation for these problems.

cs.CG

Improved constant approximation factor algorithms for $k$-center problem for uncertain data

In real applications, database systems should be able to manage and process data with uncertainty. Any real dataset may have missing or rounded values, also the values of data may change by time. So, it becomes important to handle these uncertain data. An important problem in database technology is to cluster these uncertain data. In this paper, we study the $k$-center problem for uncertain points in a general metric space. First we present a greedy approximation algorithm that builds $k$ centers using a farthest-first traversal in $k$ iterations. This algorithm improves the approximation factor of the unrestricted assigned $k$-center problem from $10$ to $6$. Next we restrict the centers to be selected from a finite set of points and we show that the optimal solution for this restricted setting is a $2$-approximation factor solution for the optimal solution of the assigned $k$-center problem. Using this idea we improve the approximation factor of the unrestricted assigned $k$-center problem to $4$ by increasing the running time mildly.

cs.CG

On Triangluar Separation of Bichromatic Point Sets in Polygonal Environment

Let $\mathcal P$ be a simple polygonal environment with $k$ vertices in the plane. Assume that a set $B$ of $b$ blue points and a set $R$ of $r$ red points are distributed in $\mathcal P$. We study the problem of computing triangles that separate the sets $B$ and $R$, and fall in $\mathcal P$. We call these triangles \emph{inscribed triangular separators}. We propose an output-sensitive algorithm to solve this problem in $O(r \cdot (r+c_B+k)+h_\triangle)$ time, where $c_B$ is the size of convex hull of $B$, and $h_\triangle$ is the number of inscribed triangular separators. We also study the case where there does not exist any inscribed triangular separators. This may happen due to the tight distribution of red points around convex hull of $B$ while no red points are inside this hull. In this case we focus to compute a triangle that separates most of the blue points from the red points. We refer to these triangles as \emph{maximum triangular separators}. Assuming $n=r+b$, we design a constant-factor approximation algorithm to compute such a separator in $O(n^{4/3} \log^3 n)$ time. "Eligible for best student paper"

cs.CG

Upper bounds for domination numbers of graphs using Turán's Theorem and Lovász local lemma

Let $G$ be a connected graph of order $n$ with vertex set $V(G)$. A subset $S\subseteq V(G)$ is an $(a,b)$-dominating set if every vertex $v\in S$ is adjacent to at least $a$ vertices in $S$ and every $v\in V\setminus S$ is adjacent to at least $b$ vertices in $S$. The minimum cardinality of an $(a,b)$-dominating set of $G$ is the $(a,b)$-domination number of $G$, denoted by $γ_{a,b}(G)$. There are various results about upper bounds for $γ_{a,b}(G)$ when $G$ is regular or $a$ and $b$ are small numbers. In the first part of this paper, for a given graph $G$ with the minimum degree of $\max\{a,b\}$, we define a new graph $G'$ associated to $G$ and show that the independence number of this graph is related to $γ_{a,b}(G)$. In the next part, using Lovász local lemma, we give a randomized approach to improve previous results in some special cases.

math.CO

On $k$-tuple and $k$-tuple total domination numbers of regular graphs

Let $G$ be a connected graph of order $n$, whose minimum vertex degree is at least $k$. A subset $S$ of vertices in $G$ is a $k$-tuple total dominating set if every vertex of $G$ is adjacent to at least $k$ vertices in $S$. The minimum cardinality of a $k$-tuple total dominating set of $G$ is the $k$-tuple total domination number of $G$, denoted by $γ_{\times k,t}(G)$. Henning and Yeo in \cite{hen} proved that if $G$ is a cubic graph different from the Heawood graph, $γ_{\times 2, t}(G) \leq \frac{5}{6}n$, and this bound is sharp. Similarly, a $k$-tuple dominating set is a subset $S$ of vertices of $G$, $V (G)$ such that $|N[v] \cap S| \geq k$ for every vertex $v$, where $N[v] = \{v\}\cup \{u \in V(G) : uv \in E(G)\}$. The $k$-tuple domination number of $G$, denoted by $γ_{\times k}(G)$, is the minimum cardinality of a $k$-tuple dominating set of $G$. In this paper, we give a simple approach to compute an upper bound for $(r-1)$-tuple total domination number of $r$-regular graphs. Also, we give an upper bound for the $r$-tuple dominating number of $r$-regular graphs. In addition, our method gives algorithms to compute dominating sets with the given bounds, while the previous methods are existential.

math.CO

Improvements on the k-center problem for uncertain data

In real applications, there are situations where we need to model some problems based on uncertain data. This leads us to define an uncertain model for some classical geometric optimization problems and propose algorithms to solve them. In this paper, we study the $k$-center problem, for uncertain input. In our setting, each uncertain point $P_i$ is located independently from other points in one of several possible locations $\{P_{i,1},\dots, P_{i,z_i}\}$ in a metric space with metric $d$, with specified probabilities and the goal is to compute $k$-centers $\{c_1,\dots, c_k\}$ that minimize the following expected cost $$Ecost(c_1,\dots, c_k)=\sum_{R\in Ω} prob(R)\max_{i=1,\dots, n}\min_{j=1,\dots k} d(\hat{P}_i,c_j)$$ here $Ω$ is the probability space of all realizations $$R=\{\hat{P}_1,\dots, \hat{P}_n\}$$ of given uncertain points and $$prob(R)=\prod_{i=1}^n prob(\hat{P}_i).$$ In restricted assigned version of this problem, an assignment $A:\{P_1,\dots, P_n\}\rightarrow \{c_1,\dots, c_k\}$ is given for any choice of centers and the goal is to minimize $$Ecost_A(c_1,\dots, c_k)=\sum_{R\in Ω} prob(R)\max_{i=1,\dots, n} d(\hat{P}_i,A(P_i)).$$ In unrestricted version, the assignment is not specified and the goal is to compute $k$ centers $\{c_1,\dots, c_k\}$ and an assignment $A$ that minimize the above expected cost. We give several improved constant approximation factor algorithms for the assigned versions of this problem in a Euclidean space and in a general metric space. Our results significantly improve the results of \cite{guh} and generalize the results of \cite{wang} to any dimension. Our approach is to replace a certain center point for each uncertain point and study the properties of these certain points. The proposed algorithms are efficient and simple to implement.

cs.CG