Search arXiv⌕ Search

arXiv · 2610.27422

Approximating Directed Telephone Multicast

Abstract

In the telephone model a message starts at a source vertex, and in every round each informed vertex may call one neighbour. Telephone multicast asks for a schedule that informs a given set of $k$ terminals in the fewest rounds. On directed graphs, the best known ratio for this problem was $\tilde O(\sqrt k)$, due to Elkin and Kortsarz and to Hathcock, Kortsarz and Ravi. For every integer $\ell\ge2$, we give a deterministic combinatorial algorithm with running time $n^{O(\ell)}$ that computes a schedule of length $O(\ell^3\log k)\cdot(\mathrm{OPT}+k^{1/\ell})$, where $n$ is the number of vertices and $\mathrm{OPT}$ the optimum. Since $\mathrm{OPT}\ge\log_2(k+1)$, this gives ratio $O(k^ε)$ in time $n^{O(1/ε)}$ for every fixed $ε>0$, and ratio $O(\log^4k)$ in quasi-polynomial time. The method rests on jump trees with additive vertex loads, a height reduction that bounds the load of every vertex, and a recursive greedy algorithm with an exponential potential. We also show that directed multicast is a special case of the degree-bounded directed Steiner tree problem on a time-expanded digraph. With known algorithms for that problem this gives randomized ratios $O(\log^2n)$ in time $n^{O(\log n)}$ and $O(n^ε)$ in time $n^{O(1/ε)}$, which, unlike ours, grow with $n$. On the hardness side, directed multicast stays $(1-ε)\ln k$-hard even when $\mathrm{OPT}=O(\log k)$, the regime in which the previous algorithms were weakest.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shahin Kamali, Seyed-Mohammad Seyed-Javadi. 2026-10-20. Approximating Directed Telephone Multicast. https://arxiv.org/abs/2610.27422

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

KEEP EXPLORING

Related papers

Compressed Set Representations based on Set Difference

We introduce a compressed representation of sets of sets that exploits how similar they are to each other. Our representation supports access, membership, predecessor and successor queries on the sets within logarithmic time. It also computes the support of any element (i.e., the sets containing it) in optimal time. In addition, we give a new MST-based construction algorithm for the representation that outperforms standard ones.

cs.DS↗

Fast Approximation Algorithms for Balanced Edge and Vertex Separators

We present a family of fast pseudo-approximation algorithms for the minimum balanced element cut problem in a graph, which includes vertex and edge separators. Given a graph $G=(V,E)$ with polynomially-bounded finite integer element weights with $n$ vertices and $m$ edges, and a (constant) balance parameter $c\in(0,1/2)$, where $G$ has some (unknown) $c$-balanced element cut of size $OPT_c$, we give a (Monte-Carlo randomized) algorithm running in $O(n^{\varepsilon}m^{1+o(1)})$ time that produces a $Θ(1)$-balanced element cut of size $O(OPT_c\cdot\sqrt{(\log n)/\varepsilon})$ for any value $\varepsilon\in[Θ(1/\log(n)),Θ(1)]$. Our algorithms are based on a semidefinite programming (SDP) relaxation of the problem. A standard approach to solving an SDP relaxation for this kind of problem would use the primal-dual Matrix Multiplicative Weights Update framework of Arora and Kale, but this framework does not seem to apply, since we cannot express all of our SDPs primal variables explicitly. Instead, we present a new MMWU framework for our problem that allows us to represent some of our variables implicitly. We also extend our framework to a general "Partial-variable MMWU framework", which we expect will be useful for future work. Our oracle for the balanced element cut problem within our framework uses $O(n^{\varepsilon}\text{polylog}(n))$ almost-linear time maximum-flow computations, and would be sped up if the time complexity of maximum-flow improves. For graphs with padding bound $β$, another oracle gives an $O(\sqrtβ/\varepsilon)$ approximation in $m^{1+o(1)}n^{\varepsilon}\operatorname{polylog}(n)$ time. It is known that $β\le O(\log h)$ for $K_h$-minor-free graphs (and in particular $β\le O(\log n)$).

cs.DS↗

Optimal-Time Move Structure Construction

The move structure represents a permutation $π$ of $[0,n)$ by partitioning the domain into $O(r)$ disjoint, contiguously permuted intervals, with $r$ being the minimum number of such intervals. This data structure occupies $O(r)$ words of space and enables $O(1)$-time computation of $π(i)$ given the interval that contains $i$. For permutations where $r \ll n$, this provides an efficient, compressed representation for navigation. While the existing best $O(r)$-space construction approaches require $O(r\log r)$-time, we present an optimal $O(r)$-time and space construction algorithm. This is achieved by replacing balanced search trees with pointer-based lists through introducing a bidirectional strategy that synchronizes construction of the structures for $π$ and its inverse $π^{-1}$ in a single, unified pass. By applying this algorithm to an existing result, we achieve the first optimal $O(n)$-time construction of the longest common prefix (LCP) array from a run-length-encoded Burrows-Wheeler transform (RLBWT) of $r$ runs in $O(r)$ working space. Empirical evaluation on pangenome-scale data confirms that our move structure construction algorithm is consistently faster than the previous best, achieving speedups of up to $\sim 2\times$ while using less memory.

cs.DS↗