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Zeev Nutov

Publications and source records attributed to Zeev Nutov.

At least 19 recordsLinked to original sources

On $k$-connectivity oracles in $k$-connected graphs

A $k$-connectivity oracle for a graph $G=(V,E)$ is a data structure that given $s,t \in V$ determines whether there are at least $k+1$ internally disjoint $st$-paths in $G$. For undirected graphs, Pettie, Saranurak & Yin [STOC 2022, pp. 151-161] proved that any $k$-connectivity oracle requires $\Omega(kn)$ bits of space. They asked whether $\Omega(kn)$ bits are still necessary if $G$ is $k$-connected. We will show by a very simple proof that this is so even if $G$ is $k$-connected, answering this open question.

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Approximation and parameterized algorithms for covering disjointness-compliable set families

A set-family ${\cal F}$ is disjointness-compliable if $A' \subseteq A \in {\cal F}$ implies $A' \in {\cal F}$ or $A \setminus A' \in {\cal F}$; if ${\cal F}$ is also symmetric then ${\cal F}$ is proper. A classic result of Goemans and Williamson [SODA 92:307-316] states that the problem of covering a proper set-family by a min-cost edge set admits approximation ratio $2$, by a classic primal-dual algorithm. However, there are several famous algorithmic problems whose set-family ${\cal F}$ is disjointness-compliable but not symmetric -- among them $k$-Minimum Spanning Tree ($k$-MST), Generalized Point-to-Point Connection (G-P2P), Group Steiner, Covering Steiner, multiroot versions of these problems, and others. We will show that any such problem admits approximation ratio $O(\alpha \log \tau)$, where $\tau$ is the number of inclusion-minimal sets in the family ${\cal F}$ that models the problem and $\alpha$ is the best known approximation ratio for the case when $\tau=1$. This immediately implies several results, among them the following two. (i) The first deterministic polynomial time $O(\log n)$-approximation algorithm for the G-P2P problem. Here the $\tau=1$ case is the $k$-MST problem. (ii) Approximation ratio $O(\log^4 n)$ for the multiroot version of the Covering Steiner problem, where each root has its own set of groups. Here the $\tau=1$ case is the Covering Steiner problem. We also discuss the parameterized complexity of covering a disjointness-compliable family ${\cal F}$, when parametrized by $\tau$. We will show that if ${\cal F}$ is proper then the problem is fixed parameter tractable and can be solved in time $O^*(3^\tau)$. For the non-symmetric case we will show that the problem admits approximation ratio between $\alpha$ and $\alpha+1$ in time $O^*(3^\tau)$, which is essentially the best possible.

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A tight example for approximation ratio 5 for covering small cuts by the primal-dual method

In the Small Cuts Cover problem we seek to cover by a min-cost edge-set the set family of cuts of size/capacity $<k$ of a graph. Recently, Simmons showed that the primal-dual algorithm of Williamson, Goemans, Mihail, and Vazirani achieves approximation ratio $5$ for this problem, and asked whether this bound is tight. We will answer this question positively, by providing an example in which the ratio between the solution produced by the primal-dual algorithm and the optimum is arbitrarily close to $5$.

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Improved bicriteria approximation for $k$-edge-connectivity

In the $k$-Edge Connected Spanning Subgraph ($k$-ECSS) problem we are given a (multi-)graph $G=(V,E)$ with edge costs and an integer $k$, and seek a min-cost $k$-edge-connected spanning subgraph of $G$. The problem admits a $2$-approximation algorithm and no better approximation ratio is known. Hershkowitz, Klein, and Zenklusen [STOC 24] gave a bicriteria $(1,k-10)$-approximation algorithm that computes a $(k-10)$-edge-connected spanning subgraph of cost at most the optimal value of a standard Cut-LP for $k$-ECSS. This LP bicriteria approximation was recently improved by Cohen and Nutov [ESA 25] to $(1,k-4)$, where also was given a bicriteria approximation $(3/2,k-2)$. In this paper we improve the bicriteria approximation to $(1,k-2)$ for $k$ even and to $\left(1-\frac{1}{k},k-3\right)$ for $k$ is odd, and also give another bicriteria approximation $(3/2,k-1)$. After this paper was written, we became aware that the same result was achieved earlier by Kumar and Swamy. The $k$-Edge-Connected Spanning Multi-subgraph ($k$-ECSM) problem is almost the same as $k$-ECSS, except that any edge can be selected multiple times at the same cost. The previous best approximation ratio for $k$-ECSM was $1+4/k$. Our result improves this to $1+\frac{2}{k}$ for $k$ even and to $1+\frac{3}{k}$ for $k$ odd, where for $k$ odd the computed subgraph is in fact $(k+1)$-edge-connected.

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Bicriteria approximation for $k$-edge-connectivity

In the $k$-Edge Connected Spanning Subgraph ($k$-ECSS) problem we are given a (multi-)graph $G=(V,E)$ with edge costs and an integer $k$, and seek a min-cost $k$-edge-connected spanning subgraph of $G$. The problem admits a $2$-approximation algorithm and no better approximation ratio is known. Recently, Hershkowitz, Klein, and Zenklusen [STOC 24] gave a bicriteria $(1,k-10)$-approximation algorithm that computes a $(k-10)$-edge-connected spanning subgraph of cost at most the optimal value of a standard Cut-LP for $k$-ECSS. We improve the bicriteria approximation to $(1,k-4)$, and also give another non-trivial bicriteria approximation $(3/2,k-2)$. The $k$-Edge-Connected Spanning Multi-subgraph ($k$-ECSM) problem is almost the same as $k$-ECSS, except that any edge can be selected multiple times at the same cost. A $(1,k-p)$ bicriteria approximation for $k$-ECSS w.r.t. Cut-LP implies approximation ratio $1+p/k$ for $k$-ECSM, hence our result also improves the approximation ratio for $k$-ECSM.

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Tight analysis of the primal-dual method for edge-covering pliable set families

A classic result of Williamson, Goemans, Mihail, and Vazirani [STOC 1993: 708-717] states that the problem of covering an uncrossable set family by a min-cost edge set admits approximation ratio $2$, by a primal-dual algorithm with a reverse delete phase. Bansal, Cheriyan, Grout, and Ibrahimpur [ICALP 2023: 15:1-15:19] showed that this algorithm achieves approximation ratio $16$ for a larger class of so called $γ$-pliable set families, that have much weaker uncrossing properties. The approximation ratio $16$ was improved to $10$ by the author [WAOA 2025: 151-166]. Recently, Bansal [arXiv:2308.15714] stated approximation ratio $8$ for $γ$-pliable families and an improved approximation ratio $5$ for an important particular case of the family of cuts of size $<k$ of a graph $H$, but his proof has an error. We will improve the approximation ratio to $7$ for the former case and give a simple proof of approximation ratio $6$ for the latter case. Furthermore, if $H$ is $λ$-edge-connected then we will show a slightly better approximation ratio $6-\frac{1}{β+1}$, where $β=\left\lfloor\frac{k-1}{\lceil(λ+1)/2\rceil}\right\rfloor$. Our analysis is supplemented by examples showing that these approximation ratios are tight for the primal-dual algorithm.

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Improved approximation ratio for covering pliable set families

A classic result of Williamson, Goemans, Mihail, and Vazirani [STOC 1993: 708-717] states that the problem of covering an uncrossable set family by a min-cost edge set admits approximation ratio $2$, by a primal-dual algorithm with a reverse delete phase. Recently, Bansal, Cheriyan, Grout, and Ibrahimpur [ICALP 2023: 15:1-15:19] showed that this algorithm achieves approximation ratio $16$ for a larger class of set families, that have much weaker uncrossing properties. In this paper we will refine their analysis and show an approximation ratio of $10$. This also improves approximation ratios for several variants of the Capacitated $k$-Edge Connected Spanning Subgraph problem.

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An $2\sqrt{k}$-approximation algorithm for minimum power $k$ edge disjoint $st$ -paths

In minimum power network design problems we are given an undirected graph $G=(V,E)$ with edge costs $\{c_e:e \in E\}$. The goal is to find an edge set $F\subseteq E$ that satisfies a prescribed property of minimum power $p_c(F)=\sum_{v \in V} \max \{c_e: e \in F \mbox{ is incident to } v\}$. In the Min-Power $k$ Edge Disjoint $st$-Paths problem $F$ should contains $k$ edge disjoint $st$-paths. The problem admits a $k$-approximation algorithm, and it was an open question whether it admits approximation ratio sublinear in $k$ even for unit costs. We give a $2\sqrt{2k}$-approximation algorithm for general costs.

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Improved Approximations for Relative Survivable Network Design

One of the most important and well-studied settings for network design is edge-connectivity requirements. This encompasses uniform demands such as the Minimum $k$-Edge-Connected Spanning Subgraph problem as well as nonuniform demands such as the Survivable Network Design problem (SND). In a recent paper by [Dinitz, Koranteng, Kortsarz APPROX '22] , the authors observed that a weakness of these formulations is that it does not enable one to consider fault-tolerance in graphs that have just a few small cuts. To remedy this, they introduced new variants of these problems under the notion "relative" fault-tolerance. Informally, this requires not that two nodes are connected if there are a bounded number of faults (as in the classical setting), but that two nodes are connected if there are a bounded number of faults and the two nodes are connected in the underlying graph post-faults. The problem is already highly non-trivial even for the case of a single demand. Due to difficulties introduced by this new notion of fault-tolerance, the results in [Dinitz, Koranteng, Kortsarz APPROX '22] are quite limited. For the Relative Survivable Network Design problem (RSND), when the demands are not uniform they give a nontrivial result only when there is a single demand with a connectivity requirement of $3$: a non-optimal $27/4$-approximation. We strengthen this result in two significant ways: We give a $2$-approximation for RSND where all requirements are at most $3$, and a $2^{O(k^2)}$-approximation for RSND with a single demand of arbitrary value $k$. To achieve these results, we first use the "cactus representation'' of minimum cuts to give a lossless reduction to normal SND. Second, we extend the techniques of [Dinitz, Koranteng, Kortsarz APPROX '22] to prove a generalized and more complex version of their structure theorem, which we then use to design a recursive approximation algorithm.

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A logarithmic approximation algorithm for the activation edge multicover problem

In the Activation Edge-Multicover problem we are given a multigraph $G=(V,E)$ with activation costs $\{c_{e}^u,c_{e}^v\}$ for every edge $e=uv \in E$, and degree requirements $r=\{r_v:v \in V\}$. The goal is to find an edge subset $J \subseteq E$ of minimum activation cost $\sum_{v \in V}\max\{c_{uv}^v:uv \in J\}$,such that every $v \in V$ has at least $r_v$ neighbors in the graph $(V,J)$. Let $k= \max_{v \in V} r_v$ be the maximum requirement and let $θ=\max_{e=uv \in E} \frac{\max\{c_e^u,c_e^v\}}{\min\{c_e^u,c_e^v\}}$ be the maximum quotient between the two costs of an edge. For $θ=1$ the problem admits approximation ratio $O(\log k)$. For $k=1$ it generalizes the Set Cover problem (when $θ=\infty$), and admits a tight approximation ratio $O(\log n)$. This implies approximation ratio $O(k \log n)$ for general $k$ and $θ$, and no better approximation ratio was known. We obtain the first logarithmic approximation ratio $O(\log k +\log\min\{θ,n\})$, that bridges between the two known ratios -- $O(\log k)$ for $θ=1$ and $O(\log n)$ for $k=1$. This implies approximation ratio $O\left(\log k +\log\min\{θ,n\}\right) +β\cdot (θ+1)$ for the Activation $k$-Connected Subgraph problem, where $β$ is the best known approximation ratio for the ordinary min-cost version of the problem.

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On rooted $k$-connectivity problems in quasi-bipartite digraphs

We consider the directed Min-Cost Rooted Subset $k$-Edge-Connection problem: given a digraph $G=(V,E)$ with edge costs, a set $T \subseteq V$ of terminals, a root node $r$, and an integer $k$, find a min-cost subgraph of $G$ that contains $k$ edge disjoint $rt$-paths for all $t \in T$. The case when every edge of positive cost has head in $T$ admits a polynomial time algorithm due to Frank [Discret. Appl. Math. 157(6):1242-1254, 2009], and the case when all positive cost edges are incident to $r$ is equivalent to the $k$-Multicover problem. Chan, Laekhanukit, Wei, and Zhang [APPROX/RANDOM, 63:1-63:20, 2020] gave an LP-based $O(\ln k \ln |T|)$-approximation algorithm for quasi-bipartite instances, when every edge in $G$ has an end (tail or head) in $T \cup \{r\}$. We give a simple combinatorial algorithm with the same ratio for a more general problem of covering an arbitrary $T$-intersecting supermodular set function by a minimum cost edge set, and for the case when only every positive cost edge has an end in $T \cup \{r\}$.

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A 1.5-pproximation algorithms for activating 2 disjoint $st$-paths

In the $Activation$ $k$ $Disjoint$ $st$-$Paths$ ($Activation$ $k$-$DP$) problem we are given a graph $G=(V,E)$ with activation costs $\{c_{uv}^u,c_{uv}^v\}$ for every edge $uv \in E$, a source-sink pair $s,t \in V$, and an integer $k$. The goal is to compute an edge set $F \subseteq E$ of $k$ internally node disjoint $st$-paths of minimum activation cost $\displaystyle \sum_{v \in V}\max_{uv \in E}c_{uv}^v$. The problem admits an easy $2$-approximation algorithm. Alqahtani and Erlebach [CIAC, pages 1-12, 2013] claimed that Activation 2-DP admits a $1.5$-approximation algorithm. Their proof has an error, and we will show that the approximation ratio of their algorithm is at least $2$. We will then give a different algorithm with approximation ratio $1.5$.

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Extending the primal-dual 2-approximation algorithm beyond uncrossable set families

A set family ${\cal F}$ is $uncrossable$ if $A \cap B,A \cup B \in {\cal F}$ or $A \setminus B,B \setminus A \in {\cal F}$ for any $A,B \in {\cal F}$. A classic result of Williamson, Goemans, Mihail, and Vazirani [STOC 1993:708-717] states that the problem of covering an uncrossable set family by a min-cost edge set admits approximation ratio $2$, by a primal-dual algorithm. They asked whether this result extends to a larger class of set families and combinatorial optimization problems. We define a new class of $semi$-$uncrossable$ $set$ $families$, when for any $A,B \in {\cal F}$ we have that $A \cap B \in {\cal F}$ and one of $A \cup B,A \setminus B ,B \setminus A$ is in ${\cal F}$, or $A \setminus B,B \setminus A \in {\cal F}$. We will show that the Williamson et al. algorithm extends to this new class of families and identify several ``non-uncrossable'' algorithmic problems that belong to this class. In particular, we will show that the union of an uncrossable family and a monotone family, or of an uncrossable family that has the disjointness property and a proper family, is a semi-uncrossable family, that in general is not uncrossable. For example, our result implies approximation ratio $2$ for the problem of finding a min-cost subgraph $H$ such that $H$ contains a Steiner forest and every connected component of $H$ contains at least $k$ nodes from a given set $T$ of terminals.

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Improved approximation algorithms for some capacitated $k$ edge connectivity problems

We consider the following two variants of the Capacitated $k$-Edge Connected Subgraph} (Cap-k-ECS) problem. Near Min-Cuts Cover: Given a graph $G=(V,E)$ with edge costs and $E_0 \subseteq E$, find a min-cost edge set $J \subseteq E \setminus E_0$ that covers all cuts with at most $k-1$ edges of the graph $G_0=(V,E_0)$. We obtain approximation ratio $k-λ_0+1+ε$, improving the ratio $2\min\{k-λ_0,8\}$ of Bansal, Cheriyan, Grout, and Ibrahimpur for $k-λ_0 \leq 14$,where $λ_0$ is the edge connectivity of $G_0$. $(k,q)$-Flexible Graph Connectivity ($(k,q)$-FGC): Given a graph $G=(V,E)$ with edge costs and a set $U \subseteq E$ of ''unsafe'' edges and integers $k,q$, find a min-cost subgraph $H$ of $G$ such that every cut of $H$ has at least $k$ safe edges or at least $k+q$ edges. We show that $(k,1)$-FGC admits approximation ratio $3.5+ε$ if $k$ is odd (improving the previous ratio $4$), and that $(k,2)$-FGC admits approximation ratio $6$ if $k$ is even and $7+ε$ if $k$ is odd (improving the previous ratio $20$).

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Data structure for node connectivity and cut queries

Let $κ(s,t)$ denote the maximum number of internally disjoint $st$-paths in an undirected graph $G$. We consider designing a compact data structure that answers $k$-bounded node connectivity queries: given $s,t \in V$ return $\min\{κ(s,t),k+1\}$. A trivial data structure has space $O(n^2)$ and query time $O(1)$. A data structure of Hsu and Lu has space $O(k^2n)$ and query time $O(\log k)$,and a randomized data structure of Iszak and Nutov has space $O(kn\log n)$ and query time $O(k \log n)$. We extend the Hsu-Lu data structure to answer queries in time $O(1)$. In parallel to our work, Pettie, Saranurak and Yin extended the Iszak-Nutov data structure to answer queries in time $O(\log n)$. Our data structure is more compact for $k<\log n$, and our query time is always better. We then augment our data structure by a list of cuts that enables to return a pointer to a minimum $st$-cut in the list (or to a cut of size $\leq k$) whenever $κ(s,t) \leq k$. A trivial data structure has cut list size $n(n-1)/2$, and cut query time $O(1)$, while the Pettie, Saranurak and Yin data structure has list size $O(kn \log n)$ and cut query time $O(\log n)$. We show that $O(kn)$ cuts suffice to return an $st$-cut of size $\leq k$, and a list of $O(k^2 n)$ cuts contains a minimum $st$-cut for every $s,t \in V$. In the case when $S$ is a node subset with $κ(s,t) \geq k$ for all $s,t \in V$, we show that $3|S|$ cuts suffice, and that these cuts can be partitioned into $O(k)$ laminar families. Thus using space $O(kn)$ we can answers each connectivity and cut queries for $s,t \in S$ in $O(1)$ time, generalizing and substantially simplifying the proof of a result of Pettie and Yin for the case $|S|=V$.

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Parameterized algorithms for node connectivity augmentation problems

A graph $G$ is $k$-out-connected from its node $s$ if it contains $k$ internally disjoint $sv$-paths to every node $v$; $G$ is $k$-connected if it is $k$-out-connected from every node. In connectivity augmentation problems the goal is to augment a graph $G_0=(V,E_0)$ by a minimum costs edge set $J$ such that $G_0 \cup J$ has higher connectivity than $G_0$. In the $k$-Out-Connectivity Augmentation ($k$-OCA) problem, $G_0$ is $(k-1)$-out-connected from $s$ and $G_0 \cup J$ should be $k$-out-connected from $s$; in the $k$-Connectivity Augmentation ($k$-CA) problem $G_0$ is $(k-1)$-connected and $G_0 \cup J$ should be $k$-connected. The parameterized complexity status of these problems was open even for $k=3$ and unit costs. We will show that $k$-OCA and $3$-CA can be solved in time $9^p \cdot n^{O(1)}$, where $p$ is the size of an optimal solution. Our paper is the first that shows fixed parameter tractability of a $k$-node-connectivity augmentation problem with high values of $k$. We will also consider the $(2,k)$-Connectivity Augmentation problem where $G_0$ is $(k-1)$-edge-connected and $G_0 \cup J$ should be both $k$-edge-connected and $2$-connected. We will show that this problem can be solved in time $9^p \cdot n^{O(1)}$, and for unit costs approximated within $1.892$.

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$2$-node-connectivity network design

We consider network design problems in which we are given a graph and seek a min-size $2$-connected subgraph that satisfies a prescribed property. $\bullet$ In the 1-Connectivity Augmentation problem the goal is to augment a connected graph by a min-size edge subset of a specified edge set such that the augmented graph is $2$-connected. We breach the natural ratio of $2$ for this problem and also for the more general Crossing Family Cover problem. $\bullet$ In the $2$-Connected Dominating Set problem we seek a minimum size $2$-connected subgraph that dominates all nodes. We give the first non-trivial approximation algorithm for this problem, with expected ratio $O(σ\log^3 n)$, where $σ=O(\log n \cdot\log\log n\cdot(\log\log\log n)^{3})$. The unifying technique of both results is a reduction to the Subset Steiner Connected Dominating Set problem. Such a reduction was known for edge-connectivity, and we extend it to $2$-node connectivity problems. We show that the same method can be used to obtain easily polylogarithmic approximation ratios that are not too far from the best known ones for several other problems.

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$k$ disjoint $st$-paths activation in polynomial time

In activation network design problems we are given an undirected graph $G=(V,E)$ and a pair of activation costs $\{c_e^u,c_e^v\}$ for each $e=uv \in E$. The goal is to find an edge set $F \subseteq E$ that satisfies a prescribed property of minimum activation cost $τ(F)=\sum_{v \in V} \max \{c_e^v: e \in F \mbox{ is incident to } v\}$. In the Activation $k$ Disjoint Paths problem we are given $s,t \in V$ and an integer $k$, and seek an edge set $F \subseteq E$ of $k$ internally disjoint $st$-paths of minimum activation cost. The problem admits an easy $2$-approximation algorithm. However, it was an open question whether the problem is in P even for $k=2$ and power activation costs, when $c_e^u=c_e^v$ for all $e=uv \in E$. Here we will answer this question by giving a polynomial time algorithm using linear programing. We will also mention several consequences, among them a polynomial time algorithm for the Activation 2 Edge Disjoint Paths problem, and improved approximation ratios for the Min-Power $k$-Connected Subgraph problem.

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