Abstract We consider minimum time multicasting problems in directed and undirected graphs: given a root node and a subset of t terminal nodes, multicasting seeks to find the minimum number of rounds within which all terminals can be informed with a message originating at the root. In each round, the telephone model we study allows the information to move via a matching from the informed nodes to the uninformed nodes. Since minimum time multicasting in digraphs is poorly understood compared to the undirected variant, we study an intermediate problem in undirected graphs that specifies a target $$k &lt; t$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> , and requires that only k of the terminals be informed in the minimum number of rounds. For this problem, we improve the implications of the previous results and obtain a multiplicative approximation factor of $$\tilde{O}(t^{1/3})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>t</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . For the directed version, we obtain an additive $$\tilde{O}(k^{1/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>k</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> approximation algorithm (with a polylogarithmic multiplicative factor). Our algorithms are based on reductions to the related problems of finding k -trees of minimum poise (sum of maximum degree and diameter) and applying a combination of greedy network decomposition techniques and set covering under partition matroid constraints. We also study the problem of bounded degree Directed Steiner Tree, for which we obtain improved polylogarithmic approximations for the special case of bounded treewidth graphs. This extends prior work on the Group Steiner Tree problem.

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 ($k$-ECSS), as well as nonuniform demands such as the Survivable Network Design problem. A weakness of these formulations, though, is that we are not able to ask for fault-tolerance larger than the connectivity. We introduce and study new variants of these problems under a notion of relative fault-tolerance. Informally, we require 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. That is, the subgraph we build must "behave" identically to the underlying graph with respect to connectivity after bounded faults. We define and introduce these problems, and provide the first approximation algorithms: a $(1+4/k)$-approximation for the unweighted relative version of $k$-ECSS, a $2$-approximation for the weighted relative version of $k$-ECSS, and a $27/4$-approximation for the special case of Relative Survivable Network Design with only a single demand with a connectivity requirement of $3$. To obtain these results, we introduce a number of technical ideas that may of independent interest. First, we give a generalization of Jain's iterative rounding analysis that works even when the cut-requirement function is not weakly supermodular, but instead satisfies a weaker definition we introduce and term local weak supermodularity. Second, we prove a structure theorem and design an approximation algorithm utilizing a new decomposition based on important separators, which are structures commonly used in fixed-parameter algorithms that have not commonly been used in approximation algorithms.

Directed Steiner Tree (DST) is a central problem in combinatorial optimization and theoretical computer science: Given a directed graph $$G=(V, E)$$ with edge costs $$c \in {\mathbb {R}}_{\ge 0}^E$$ , a root $$r \in V$$ and k terminals $$K\subseteq V$$ , we need to output the minimum-cost arborescence in G that contains an $$r \rightarrow t$$ path for every $$t \in K$$ . Recently, Grandoni, Laekhanukit and Li, and independently Ghuge and Nagarajan, gave quasi-polynomial time $$O(\log ^2k/\log \log k)$$ -approximation Algorithms for the problem, which are tight under popular complexity assumptions. In this paper, we consider the more general Degree-Bounded Directed Steiner Tree (DB-DST) problem, where we are additionally given a degree bound $$d_v$$ on each vertex $$v \in V$$ , and we require that every vertex v in the output tree has at most $$d_v$$ children. We give a quasi-polynomial time $$(O(\log n \log k), O(\log ^2 n))$$ -bicriteria approximation: The Algorithm produces a solution with cost at most $$O(\log n\log k)$$ times the cost of the optimum solution that violates the degree constraints by at most a factor of $$O(\log ^2n)$$ . This is the first non-trivial result for the problem. While our cost-guarantee is nearly optimal, the degree violation factor of $$O(\log ^2n)$$ is an $$O(\log n)$$ -factor away from the approximation lower bound of $$\Omega (\log n)$$ from the set-cover hardness. The hardness result holds even on the special case of the Degree-Bounded Group Steiner Tree problem on trees (DB-GST-T). With the hope of closing the gap, we study the question of whether the degree violation factor can be made tight for this special case. We answer the question in the affirmative by giving an $$(O(\log n\log k), O(\log n))$$ -bicriteria approximation Algorithm for DB-GST-T.

We consider questions that arise from the intersection between the areas of polynomial-time approximation algorithms, subexponential-time algorithms, and fixed-parameter tractable (FPT) algorithms. The questions, which have been asked several times, are whether there is a nontrivial FPT-approximation algorithm for the Maximum Clique $({\sf Clique})$ and Minimum Dominating Set $({\sf DomSet})$ problems parameterized by the size of the optimal solution. In particular, letting ${\sf OPT}$ be the optimum and $N$ be the size of the input, is there an algorithm that runs in $t({\sf OPT}){\operatorname{poly}}(N)$ time and outputs a solution of size $f({\sf OPT})$ for any computable functions $t$ and $f$ that are independent of $N$ (for ${\sf Clique}$, we want $f({\sf OPT})=\omega(1)$)? In this paper, we show that both ${\sf Clique}$ and ${\sf DomSet}$ admit no nontrivial FPT-approximation algorithm, i.e., there is no $o({\sf OPT})$-FPT-approximation algorithm for ${\sf Clique}$ and no $f({\sf OPT})$-FPT-approximation algorithm for ${\sf DomSet}$ for any function $f$. In fact, our results imply something even stronger: The best way to solve ${\sf Clique}$ and ${\sf DomSet}$, even approximately, is to essentially enumerate all possibilities. Our results hold under the Gap Exponential Time Hypothesis [I. Dinur. ECCC, TR16-128, 2016; P. Manurangsi and P. Raghavendra, preprint, arXiv:1607.02986, 2016], which states that no $2^{o(n)}$-time algorithm can distinguish between a satisfiable 3 \sf SAT formula and one which is not even $(1 - \varepsilon)$-satisfiable for some constant $\varepsilon > 0$. Besides ${\sf Clique}$ and ${\sf DomSet}$, we also rule out nontrivial FPT-approximation for the Maximum Biclique problem, the problem of finding maximum subgraphs with hereditary properties (e.g., Maximum Induced Planar Subgraph), and Maximum Induced Matching in bipartite graphs, and we rule out the $k^{o(1)}$-FPT-approximation algorithm for the Densest $k$-Subgraph problem.

It was recently found that there are very close connections between the existence of additive spanners (subgraphs where all distances are preserved up to an additive stretch), distance preservers (subgraphs in which demand pairs have their distance preserved exactly), and pairwise spanners (subgraphs in which demand pairs have their distance preserved up to a multiplicative or additive stretch) [Abboud-Bodwin SODA’16 8 J.ACM’17, Bodwin-Williams SODA’16]. We study these problems from an optimization point of view, where rather than studying the existence of extremal instances, we are given an instance and are asked to find the sparsest possible spanner/preserver. We give an O ( n 3/5 + ε )-approximation for distance preservers and pairwise spanners (for arbitrary constant ε > 0). This is the first nontrivial upper bound for either problem, both of which are known to be as hard to approximate as Label Cover. We also prove Label Cover hardness for approximating additive spanners, even for the cases of additive 1 stretch (where one might expect a polylogarithmic approximation, since the related multiplicative 2-spanner problem admits an O (log n )-approximation) and additive polylogarithmic stretch (where the related multiplicative spanner problem has an O (1)-approximation). Interestingly, the techniques we use in our approximation algorithm extend beyond distance-based problem to pure connectivity network design problems. In particular, our techniques allow us to give an O ( n 3/5 + ε )-approximation for the Directed Steiner Forest problem (for arbitrary constant ε > 0) when all edges have uniform costs, improving the previous best O ( n 2/3 + ε )-approximation due to Berman et al. [ICALP’11] (which holds for general edge costs).

What approximation ratio can we achieve for the Facility Location problem if whenever a client u connects to a facility v, the opening cost of v is at most theta times the service cost of u? We show that this and many other problems are a particular case of the Activation Edge-Cover problem. Here we are given a multigraph G=(V,E), a set R subseteq V of terminals, and thresholds {t^e_u,t^e_v} for each uv-edge e in E. The goal is to find an assignment a={a_v:v in V} to the nodes minimizing sum_{v in V} a_v, such that the edge set E_a={e=uv: a_u >= t^e_u, a_v >= t^e_v} activated by a covers R. We obtain ratio 1+max_{x>=1}(ln x)/(1+x/theta)~= ln theta - ln ln theta for the problem, where theta is a problem parameter. This result is based on a simple generic algorithm for the problem of minimizing a sum of a decreasing and a sub-additive set functions, which is of independent interest. As an application, we get the same ratio for the above variant of {Facility Location}. If for each facility all service costs are identical then we show a better ratio 1+max_{k in N}(H_k-1)/(1+k/theta), where H_k=sum_{i=1}^k 1/i. For the Min-Power Edge-Cover problem we improve the ratio 1.406 of [Calinescu et al, 2019] (achieved by iterative randomized rounding) to 1.2785. For unit thresholds we improve the ratio 73/60~=1.217 of [Calinescu et al, 2019] to 1555/1347~=1.155.

We introduce the problem of finding a spanning tree along with a partition of the tree edges into fewest number of feasible sets, where constraints on the edges define feasibility. The motivation comes from wireless networking, where we seek to model the irregularities seen in actual wireless environments. Not all node pairs may be able to communicate, even if geographically close --- thus, the available pairs are modeled with a link graph $\mathcal{L}=(V,E)$. Also, signal attenuation need not follow a nice geometric formulas --- hence, interference is modeled by a conflict (hyper)graph $\mathcal{C}=(E,F)$ on the links. The objective is to maximize the efficiency of the communication, or equivalently minimizing the length of a schedule of the tree edges in the form of a coloring. We find that in spite of all this generality, the problem can be approximated linearly in terms of a versatile parameter, the inductive independence of the interference graph. Specifically, we give a simple algorithm that attains a $O(ρ\log n)$-approximation, where $n$ is the number of nodes and $ρ$ is the inductive independence, and show that near-linear dependence on $ρ$ is also necessary. We also treat an extension to Steiner trees, modeling multicasting, and obtain a comparable result. Our results suggest that several canonical assumptions of geometry, regularity and "niceness" in wireless settings can sometimes be relaxed without a significant hit in algorithm performance.

The densest $k$-subgraph (D$k$S) problem and its corresponding minimization problem smallest $p$-edge subgraph (S$p$ES) have come to play a central role in approximation algorithms. This is due both to their practical importance and to their usefulness as a tool for solving and establishing approximation bounds for other problems. These two problems are not well understood, and it is widely believed that they do not admit a subpolynomial approximation ratio (although the best-known hardness results do not rule this out). In this paper we generalize both D$k$S and S$p$ES from graphs to hypergraphs. We consider the densest $k$-subhypergraph (D$k$SH) problem (given a hypergraph $(V, E)$, find a subset $W\subseteq V$ of $k$ vertices so as to maximize the number of hyperedges contained in $W$), and define the minimum $p$-union (M$p$U) problem (given a hypergraph, choose $p$ of the hyperedges so as to minimize the number of vertices in their union). We focus in particular on the case where all hyperedges have size 3, as this is the simplest nongraph setting. For this case we provide an $O(n^{4(4-\sqrt{3})/13 + \epsilon}) < O(n^{0.697831+\epsilon})$-approximation (for arbitrary constant $\epsilon > 0$) for D$k$SH and an $\tilde{O}(n^{2/5})$-approximation for M$p$U. We also give an $O(\sqrt{m})$-approximation for M$p$U in general hypergraphs. Finally, we examine the interesting special case of interval hypergraphs (instances where the vertices are a subset of the natural numbers and the hyperedges are intervals of the line) and prove that both problems admit an exact polynomial-time solution on these instances.

We consider questions that arise from the intersection between the areas of approximation algorithms, subexponential-time algorithms, and fixed-parameter tractable algorithms. The questions, which have been asked several times (e.g., [1], [2], [3]) are whether there is a non-trivial FPT-approximation algorithm for the Maximum Clique (Clique) and Minimum Dominating Set (DomSet) problems parameterized by the size of the optimal solution. In particular, letting OPT be the optimum and N be the size of the input, is there an algorithm that runs in t(OPT) poly(N) time and outputs a solution of size f(OPT), for any functions t and f that are independent of N (for Clique, we want f(OPT) = ω(1))? In this paper, we show that both Clique and DomSet admit no non-trivial FPT-approximation algorithm, i.e., there is no o(OPT)-FPT-approximation algorithm for Clique and no f(OPT)-FPT-approximation algorithm for DomSet, for any function f (e.g., this holds even if f is an exponential or the Ackermann function). In fact, our results imply something even stronger: The best way to solve Clique and DomSet, even approximately, is to essentially enumerate all possibilities. Our results hold under the Gap Exponential Time Hypothesis (GapETH) [4], [5], which states that no 2o(n)-time algorithm can distinguish between a satisfiable 3SAT formula and one which is not even (1 - ε)-satisfiable for some constant ε > 0. Besides Clique and DomSet, we also rule out non-trivial FPT-approximation for Maximum Balanced Biclique, the problem of finding maximum subgraphs with hereditary properties (e.g., Maximum Induced Planar Subgraph), and Maximum Induced Matching in bipartite graphs. Previously only exact versions of these problems were known to be W[1]-hard [6], [7], [8]. Additionally, we rule out ko(1)-FPT-approximation algorithm for Densest k-Subgraph although this ratio does not yet match the trivial O(k)-approximation algorithm. To the best of our knowledge, prior results only rule out constant factor approximation for Clique [9], [10] and log1/4+ε(OPT) approximation for DomSet for any constant ε > 0 [11]. Our result on Clique significantly improves on [9], [10]. However, our result on DomSet is incomparable to [11] since their results hold under ETH while our results hold under Gap-ETH, which is a stronger assumption.

It was recently found that there are very close connectionsbetween the existence of additive spanners (subgraphs where all distances are preserved up to an additive stretch), distance preservers (subgraphs in which demand pairs have their distance preserved exactly), and pairwise spanners (subgraphs in which demand pairs have their distance preserved up to a multiplicative or additive stretch) [Abboud-Bodwin SODA ‘16, Bodwin-Williams SODA ‘16]. We study these problemsfrom an optimization point of view, where ratherthan studying the existence of extremal instances we are given an instance and are asked to find the sparsest possible spanner/preserver. We give an O(n3/5+∊)-approximation for distance preservers and pairwisespanners (for arbitrary constant ∊ > 0). This is the first nontrivial upper bound for either problem, both of which are known to be as hard to approximate as Label Cover. We also prove Label Cover hardness for approximating additive spanners, even for the cases of additive 1 stretch (where one might expect a polylogarithmic approximation, since the related multiplicative 2-spanner problem admits an O(logn)-approximation) and additive polylogarithmic stretch (where the related multiplicative spanner problem has an O(1)-approximation). Interestingly, the techniques we use in our approximation algorithm extend beyond distance-based problem to pure connectivity network design problems. In particular, our techniques allow us to give an O(n3/5+∊)- approximation for the Directed Steiner Forest problem (for arbitrary constant ∊ > 0) when all edges have uniform costs, improving the previous best O(n2/3+∊)- approximation due to Berman et al. [ICALP ‘11] (whichholds for general edge costs).

We study the following basic problem called Bi-Covering. Given a graph $G(V,E)$, find two (not necessarily disjoint) sets $A\subseteq V$ and $B\subseteq V$ such that $A\cup B = V$ and such that every edge $e$ belongs to either the graph induced by $A$ or the graph induced by $B$. The goal is to minimize $\max\{|A|,|B|\}$. This is the most simple case of the Channel Allocation problem [R. Gandhi et al., Networks, 47 (2006), pp. 225--236]. A solution that outputs $V,\emptyset$ gives ratio at most 2. We show that under a similar strong Unique Games Conjecture by Bansal and Khot [ Optimal long code test with one free bit, in Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science, FOCS'09, IEEE, 2009, pp. 453--462] there is no $2-\epsilon$ ratio algorithm for the problem, for any constant $\epsilon>0$. Given a bipartite graph, Max-Bi-Clique is a problem of finding the largest $k\times k$ complete bipartite subgraph. For the Max-Bi-Clique problem, a constant factor hardness was known under a random 3-SAT hypothesis of Feige [ Relations between average case complexity and approximation complexity, in Proceedings of the 34th Annual ACM Symposium on Theory of Computing, ACM, 2002, pp. 534--543] and also under the assumption that ${{\sc NP}}\nsubseteq \mathop{\cap}_{\epsilon>0} \mathsf{DTIME}(2^{n^\epsilon})$ [S. Khot, SIAM J. Comput., 36 (2006), pp. 1025--1071]. It was an open problem in [C. Ambühl, M. Mastrolilli, and O. Svensson, SIAM J. Comput., 40 (2011), pp. 567--596] to prove inapproximability of Max-Bi-Clique assuming weaker conjecture. Our result implies a similar hardness result assuming the Strong Unique Games Conjecture. On the algorithmic side, we also give better than 2 approximation for Bi-Covering on numerous special graph classes. In particular, we get 1.876 approximation for chordal graphs, an exact algorithm for interval graphs, $1+o(1)$ for minor free graphs, $2-4\delta/3$ for graphs with minimum degree $\delta n$, $2/(1+\delta^2/8)$ for $\delta$-vertex expander, $8/5$ for split graphs, $2-(6/5)\cdot 1/d$ for graphs with minimum constant degree $d$, etc. Our algorithmic results are quite nontrivial. In achieving these results, we use various known structural results about the graphs combined with the techniques that we develop tailored to getting better than 2 approximation.

In the Steiner k -Forest problem, we are given an edge weighted graph, a collection D of node pairs, and an integer k ⩽ | D |. The goal is to find a min-weight subgraph that connects at least k pairs. The best known ratio for this problem is min { O (√ n ), O (√ k )} [Gupta et al. 2010]. In Gupta et al. [2010], it is also shown that ratio ρ for Steiner k -Forest implies ratio O (ρ · log 2 n ) for the related Dial-a-Ride problem. The only other algorithm known for Dial-a-Ride, besides the one resulting from Gupta et al. [2010], has ratio O (√ n ) [Charikar and Raghavachari 1998]. We obtain approximation ratio n 0.448 for Steiner k -Forest and Dial-a-Ride with unit weights, breaking the O (√ n ) approximation barrier for this natural case. We also show that if the maximum edge-weight is O ( n ϵ ), then one can achieve ratio O ( n (1 + ϵ) · 0.448 ), which is less than √ n if ϵ is small enough. The improvement for Dial-a-Ride is the first progress for this problem in 15 years. To prove our main result, we consider the following generalization of the Minimum k -Edge Subgraph (M k -ES) problem, which we call Min-Cost ℓ-Edge-Profit Subgraph (MCℓ-EPS): Given a graph G = ( V , E ) with edge-profits p = { p e : e ∈ E } and node-costs c = { c v : v ∈ V }, and a lower profit bound ℓ, find a minimum node-cost subgraph of G of edge-profit at least ℓ. The M k -ES problem is a special case of MCℓ-EPS with unit node costs and unit edge profits. The currently best known ratio for M k -ES is n 3-2√2 + ϵ [Chlamtac et al. 2012]. We extend this ratio to MCℓ-EPS for general node costs and profits bounded by a polynomial in n , which may be of independent interest.

In the Tree Augmentation Problem (TAP) the goal is to augment a tree T by a minimum size edge set F from a given edge set E such that T+F is 2-edge-connected. The best approximation ratio known for TAP is 1.5. In the more general Weighted TAP problem, F should be of minimum weight. Weighted TAP admits several 2-approximation algorithms w.r.t. the standard cut-LP relaxation. The problem is equivalent to the problem of covering a laminar set family. Laminar set families play an important role in the design of approximation algorithms for connectivity network design problems. In fact, Weighted TAP is the simplest connectivity network design problem for which a ratio better than 2 is not known. Improving this "natural" ratio is a major open problem, which may have implications on many other network design problems. It seems that achieving this goal requires finding an LP-relaxation with integrality gap better than 2, which is an old open problem even for TAP. In this paper we introduce two different LP-relaxations, and for each of them give a simple algorithm that computes a feasible solution for TAP of size at most 7/4 times the optimal LP value. This gives some hope to break the ratio 2 for the weighted case.

We study the following basic problem called Bi-Covering. Given a graph G(V, E), find two (not necessarily disjoint) sets A subseteq V and B subseteq V such that A union B = V and that every edge e belongs to either the graph induced by A or to the graph induced by B. The goal is to minimize max{|A|, |B|}. This is the most simple case of the Channel Allocation problem [Gandhi et al., Networks, 2006]. A solution that outputs V,emptyset gives ratio at most 2. We show that under the similar Strong Unique Game Conjecture by [Bansal-Khot, FOCS, 2009] there is no 2 - epsilon ratio algorithm for the problem, for any constant epsilon > 0. Given a bipartite graph, Max-bi-clique is a problem of finding largest k*k complete bipartite sub graph. For Max-bi-clique problem, a constant factor hardness was known under random 3-SAT hypothesis of Feige [Feige, STOC, 2002] and also under the assumption that NP !subseteq intersection_{epsilon > 0} BPTIME(2^{n^{epsilon}}) [Khot, SIAM J. on Comp., 2011]. It was an open problem in [Ambühl et. al., SIAM J. on Comp., 2011] to prove inapproximability of Max-bi-clique assuming weaker conjecture. Our result implies similar hardness result assuming the Strong Unique Games Conjecture. On the algorithmic side, we also give better than 2 approximation for Bi-Covering on numerous special graph classes. In particular, we get 1.876 approximation for Chordal graphs, exact algorithm for Interval Graphs, 1 + o(1) for Minor Free Graph, 2 - 4*delta/3 for graphs with minimum degree delta*n, 2/(1+delta^2/8) for delta-vertex expander, 8/5 for Split Graphs, 2 - (6/5)*1/d for graphs with minimum constant degree d etc. Our algorithmic results are quite non-trivial. In achieving these results, we use various known structural results about the graphs, combined with the techniques that we develop tailored to getting better than 2 approximation.

We study the well-known Label Cover problem under the additional requirement that problem instances have large girth. We show that if the girth is some k , the problem is roughly 2 log 1-ϵ n)/k hard to approximate for all constant ϵ > 0. A similar theorem was claimed by Elkin and Peleg [2000] as part of an attempt to prove hardness for the basic k -spanner problem, but their proof was later found to have a fundamental error. Thus, we give both the first nontrivial lower bound for the problem of Label Cover with large girth as well as the first full proof of strong hardness for the basic k -spanner problem, which is both the simplest problem in graph spanners and one of the few for which super-logarithmic hardness was not known. Assuming NP ⊆ BPTIME (2 polylog(n) , we show (roughly) that for every k ⩾ 3 and every constant ϵ > 0, it is hard to approximate the basic k -spanner problem within a factor better than 2 log 1-ϵ n)/k . This improves over the previous best lower bound of only Ω(log n )/ k from Kortsarz [2001]. Our main technique is subsampling the edges of 2-query probabilistically checkable proofs (PCPs), which allows us to reduce the degree of a PCP to be essentially equal to the soundness desired. This turns out to be enough to basically guarantee large girth.

In the Movement Repairmen (MR) problem, we are given a metric space ( V , d ) along with a set R of k repairmen r 1 , r 2 , …, r k with their start depots s 1 , s 2 , …, s k ∈ V and speeds v 1 , v 2 , …, v k ⩾ 0, respectively, and a set C of m clients c 1 , c 2 , …, c m having start locations s ′ 1 , s ′ 2 , …, s ′ m ∈ V and speeds v ′ 1 , v ′ 2 , …, v ′ m ⩾ 0, respectively. If t is the earliest time a client c j is collocated with any repairman (say, r i ) at a node u , we say that the client is served by r i at u and that its latency is t . The objective in the (S um -MR) problem is to plan the movements for all repairmen and clients to minimize the sum (average) of the clients’ latencies. The motivation for this problem comes, for example, from Amazon Locker Delivery [Amazon 2010] and USPS gopost [Service 2010]. We give the first O (log n )-approximation algorithm for the S um -MR problem. In order to approximate S um -MR, we formulate an LP for the problem and bound its integrality gap. Our LP has exponentially many variables; therefore, we need a separation oracle for the dual LP. This separation oracle is an instance of the Neighborhood Prize Collecting Steiner Tree (NPCST) problem in which we want to find a tree with weight at most L collecting the maximum profit from the clients by visiting at least one node from their neighborhoods. The NPCST problem, even with the possibility to violate both the tree weight and neighborhood radii, is still very hard to approximate. We deal with this difficulty by using LP with geometrically increasing segments of the timeline, and by giving a tricriteria approximation for the problem. The rounding needs a relatively involved analysis. We give a constant approximation algorithm for S um -MR in Euclidean Space where the speed of the clients differs by a constant factor. We also give a constant approximation for the makespan variant.

The Tree Augmentation Problem (TAP) is as follows: given a connected graph G =( V , ε ) and an edge set E on V , find a minimum size subset of edges F ⊆ E such that ( V , ε ∪ F ) is 2-edge-connected. In the conference version [Even et al. 2001] was sketched a 1.5-approximation algorithm for the problem. Since a full proof was very complex and long, the journal version was cut into two parts. The first part [Even et al. 2009] only proved ratio 1.8. An attempt to simplify the second part produced an error in Even et al. [2011]. Here we give a correct, different, and self-contained proof of the ratio 1.5 that is also substantially simpler and shorter than the previous proofs.

In the Steiner k-Forest problem we are given an edge weighted graph, a collection D of node pairs, and an integer k \leq |D|. The goal is to find a minimum cost subgraph that connects at least k pairs. The best known ratio for this problem is min{O(sqrt{n}),O(sqrt{k})} [Gupta et al., 2008]. In [Gupta et al., 2008] it is also shown that ratio rho for Steiner k-Forest implies ratio O(rho log^2 n) for the Dial-a-Ride problem: given an edge weighted graph and a set of items with a source and a destination each, find a minimum length tour to move each object from its source to destination, but carrying at most k objects at a time. The only other algorithm known for Dial-a-Ride, besides the one resulting from [Gupta et al., 2008], has ratio O(sqrt{n}) [Charikar and Raghavachari, 1998]. We obtain ratio n^{0.448} for Steiner k-Forest and Dial-a-Ride with unit weights, breaking the O(sqrt{n}) ratio barrier for this natural special case. We also show that if the maximum weight of an edge is O(n^{epsilon}), then one can achieve ratio O(n^{(1+epsilon) 0.448}), which is less than sqrt{n} if epsilon is small enough. To prove our main result we consider the following generalization of the Minimum k-Edge Subgraph (Mk-ES) problem, which we call Min-Cost l-Edge-Profit Subgraph (MCl-EPS): Given a graph G=(V,E) with edge-profits p={p_e: e in E} and node-costs c={c_v: v in V}, and a lower profit bound l, find a minimum node-cost subgraph of G of edge profit at least l. The Mk-ES problem is a special case of MCl-EPS with unit node costs and unit edge profits. The currently best known ratio for Mk-ES is n^{3-2*sqrt{2} + epsilon} (note that 3-2*sqrt{2} < 0.1716). We extend this ratio to MCl-EPS for arbitrary node weights and edge profits that are polynomial in n, which may be of independent interest.

In the matroid secretary problem we are given a stream of elements and asked to choose a set of elements that maximizes the total value of the set, subject to being an independent set of a matroid given in advance. The difficulty comes from the assumption that decisions are irrevocable: if we choose to accept an element when it is presented by the stream then we can never get rid of it, and if we choose not to accept it then we cannot later add it. Babaioff, Immorlica, and Kleinberg [SODA 2007] introduced this problem, gave O(1)-competitive algorithms for certain classes of matroids, and conjectured that every matroid admits an O(1)-competitive algorithm. However, most matroids that are known to admit an O(1)-competitive algorithm can be easily represented using graphs (e.g. graphic, cographic, and transversal matroids). In particular, there is very little known about F-representable matroids (the class of matroids that can be represented as elements of a vector space over a field F), which are one of the foundational types of matroids. Moreover, most of the known techniques are as dependent on graph theory as they are on matroid theory. We go beyond graphs by giving O(1)-competitive algorithms for regular matroids (the class of matroids that are representable over any field), and use techniques that are fundamentally matroid-theoretic rather than graph-theoretic. Our main technique is to leverage the seminal regular matroid decomposition theorem of Seymour, which gives a method for decomposing any regular matroid into matroids which are either graphic, cographic, or isomorphic to a simple 10-element matroid. We show how to combine in a black-box manner any algorithms for these basic classes into an algorithm for a given regular matroid, i.e. how to respect the decomposition. In fact, this allows us to generalize beyond regular matroids to any class of matroids that admits such a decomposition into classes for which we already have good algorithms. In particular, we give an O(1)-competitive algorithm for the class of max-flow min-cut matroids, which Seymour showed can be decomposed into regular matroids and copies of the Fano matroid.

We consider connectivity problems with orientation constraints. Given a directed graph $D$ and a collection of ordered node pairs $P$ let $P[D]=\{(u,v) \in P: D \mbox{ contains a } uv\mbox{-path}\}$. In the \sf Steiner Forest Orientation problem we are given an undirected graph $G=(V,E)$ with edge-costs and a set $P \subseteq V \times V$ of ordered node pairs. The goal is to find a minimum-cost subgraph $H$ of $G$ and an orientation $D$ of $H$ such that $P[D]=P$. We give a $4$-approximation algorithm for this problem. In the \sf Maximum Pairs Orientation problem we are given a graph $G$ and a multicollection of ordered node pairs $P$ on $V$. The goal is to find an orientation $D$ of $G$ such that $|P[D]|$ is maximum. Generalizing the result of Arkin and Hassin [Discrete Appl. Math., 116 (2002), pp. 271--278] for $|P|=2$, we will show that for a mixed graph $G$ (that may have both directed and undirected edges), one can decide in $n^{O(|P|)}$ time whether $G$ has an orientation $D$ with $P[D]=P$. (For undirected graphs this problem admits a polynomial time algorithm for any $P$, but it is NP-complete on mixed graphs.) For undirected graphs, we will show that one can decide whether $G$ admits an orientation $D$ with $|P[D]| \geq k$ in $O(n+m)+2^{O(k\cdot \log \log k)}$ time; hence this decision problem is fixed-parameter tractable, which answers an open question from Dorn et al. [Algorithms Molecular Biol., 6 (2011)]. We also show that \sf Maximum Pairs Orientation admits ratio $O(\log |P|/\log\log |P|)$, which is better than the ratio $O(\log n/\log\log n)$ of Gamzu, Segev, and Sharan [Proceedings of WABI 2010, pp. 215--225] when $|P|

In Gandhi et al. [2006], we gave an algorithm for the data migration and non-deterministic open shop scheduling problems in the minimum sum version, that was claimed to achieve a 5.06-approximation. Unfortunately, it was pointed to us by Maxim Sviridenko that the argument contained an unfounded assumption that has eluded all of its readers until now. We detail in this document how this error can be amended. A side effect is an improved approximation ratio of 4.96.

In the Steiner Network problem, we are given a graph G with edge-costs and connectivity requirements r uv between node pairs u,v . The goal is to find a minimum-cost subgraph H of G that contains r uv edge-disjoint paths for all u,v ∈ V . In Prize-Collecting Steiner Network problems, we do not need to satisfy all requirements, but are given a penalty function for violating the connectivity requirements, and the goal is to find a subgraph H that minimizes the cost plus the penalty. The case when r uv ∈ {0,1} is the classic Prize-Collecting Steiner Forest problem. In this article, we present a novel linear programming relaxation for the Prize-Collecting Steiner Network problem, and by rounding it, obtain the first constant-factor approximation algorithm for submodular and monotone nondecreasing penalty functions. In particular, our setting includes all-or-nothing penalty functions, which charge the penalty even if the connectivity requirement is slightly violated; this resolves an open question posed by Nagarajan et al. [2008]. We further generalize our results for element-connectivity and node-connectivity.

We study a very natural local protocol for a file transfer problem. Consider a scenario where several files, which may have varied sizes and get created over a period of time, are to be transferred between pairs of hosts in a distributed environment. Our protocol assumes that while executing the file transfers, an individual host does not use any global knowledge; and simply subdivides its I/O resources equally among all the active file transfers at that host at any point in time. This protocol is motivated by its simplicity of use and its applications to scheduling map-reduce workloads.

We consider the scheduling of biprocessor jobs under sum objective (BPSMSM). Given a collection of unit-length jobs where each job requires the use of two processors, find a schedule such that no two jobs involving the same processor run concurrently. The objective is to minimize the sum of the completion times of the jobs. Equivalently, we would like to find a sum edge coloring of a given multigraph, that is, a partition of its edge set into matchings M 1 ,…, M t minimizing Σ i =1 t i | M i |. This problem is APX-hard, even in the case of bipartite graphs [Marx 2009]. This special case is closely related to the classic open shop scheduling problem. We give a 1.8298-approximation algorithm for BPSMSM improving the previously best ratio known of 2 [Bar-Noy et al. 1998]. The algorithm combines a configuration LP with greedy methods, using nonstandard randomized rounding on the LP fractions. We also give an efficient combinatorial 1.8886-approximation algorithm for the case of simple graphs, which gives an improved 1.79568 + O (log d¯/d¯)-approximation in graphs of large average degree d¯.

Buy-at-bulk network design problems arise in settings where the costs for purchasing or installing equipment exhibit economies of scale. The objective is to build a network of cheapest cost to support a given multicommodity flow demand between node pairs. We present approximation algorithms for buy-at-bulk network design problems with costs on both edges and nodes of an undirected graph. Our main result is the first poly-logarithmic approximation ratio for the non-uniform problem that allows different cost functions on each edge and node; the ratio we achieve is $O(\log^4 h)$, where h is the number of demand pairs. In addition we present an $O(\log h)$ approximation for the single sink problem. Poly-logarithmic ratios for some related problems are also obtained. Our algorithm for the multicommodity problem is obtained via a reduction to the single source problem using the notion of junction trees. We believe that this presents a simple yet useful general technique for network design problems.

We study approximation algorithms, integrality gaps, and hardness of approximation of two problems related to cycles of “small” length k in a given (undirected) graph. The instance for these problems consists of a graph $G=(V,E)$ and an integer k. The k-Cycle Transversal problem is to find a minimum edge subset of E that intersects every k-cycle. The k-Cycle-Free Subgraph problem is to find a maximum edge subset of E without k-cycles. Our main result is for the k-Cycle-Free Subgraph problem with even values of k. For any $k=2r$, we give an $\Omega(n^{-\frac{1}{r}+\frac{1}{r(2r-1)}-\varepsilon})$-approximation scheme with running time $(1/\varepsilon)^{O(1/\varepsilon)}\mathsf{poly}(n)$, where $n=|V|$ is the number of vertices in the graph. This improves upon the ratio $\Omega(n^{-1/r})$ that can be deduced from extremal graph theory. In particular, for $k=4$ the improvement is from $\Omega(n^{-1/2})$ to $\Omega(n^{-1/3-\varepsilon})$. Our additional result is for odd k. The 3-Cycle Transversal problem (covering all triangles) was studied by Krivelevich [Discrete Math., 142 (1995), pp. 281–286], who presented an LP-based 2-approximation algorithm. We show that k-Cycle Transversal admits a $(k-1)$-approximation algorithm, which extends to any odd k the result that Krivelevich proved for $k=3$. Based on this, for odd k we give an algorithm for k-Cycle-Free Subgraph with ratio $\frac{k-1}{2k-3}=\frac{1}{2}+\frac{1}{4k-6}$; this improves upon the trivial ratio of $1/2$. For $k=3$, the integrality gap of the underlying LP was posed as an open problem in the work of Krivelevich. We resolve this problem by showing a sequence of graphs with integrality gap approaching 2. In addition, we show that if k-Cycle Transversal admits a $(2-\varepsilon)$-approximation algorithm, then so does the Vertex-Cover problem; thus improving the ratio 2 is unlikely. Similar results are shown for the problem of covering cycles of length $\leq k$ or finding a maximum subgraph without cycles of length $\leq k$ (i.e., with girth $>k$).

In this paper, we initiate the study of designing approximation algorithms for {\sf Fault-Tolerant Group-Steiner} ({\sf FTGS}) problems. The motivation is to protect the well-studied group-Steiner networks from edge or vertex failures. In {\sf Fault-Tolerant Group-Steiner} problems, we are given a graph with edge- (or vertex-) costs, a root vertex, and a collection of subsets of vertices called groups. The objective is to find a minimum-cost subgraph that has two edge- (or vertex-) disjoint paths from each group to the root. We present approximation algorithms and hardness results for several variants of this basic problem, e.g., edge-costs vs. vertex-costs, edge-connectivity vs. vertex-connectivity, and $2$-connecting from each group a single vertex vs. many vertices. Main contributions of our paper include the introduction of very general structural lemmas on connectivity and a charging scheme that may find more applications in the future. Our algorithmic results are supplemented by inapproximability results, which are tight in some cases. Our algorithms employ a variety of techniques. For the edge-connectivity variant, we use a primal-dual based algorithm for covering an {\em uncros\-sable} set-family, while for the vertex-connectivity version, we prove a new graph-theoretic lemma that shows equivalence between obtaining two vertex-disjoint paths from two vertices and $2$-connecting a carefully chosen single vertex. To handle large group-sizes, we use a $p$-Steiner tree algorithm to identify the ``correct'' pair of terminals from each group to be connected to the root. We also use a non-trivial charging scheme to improve the approximation ratio for the most general problem we consider.

We consider the k-Directed Steiner Forest (k-DSF) problem: given a directed graph G = (V, E) with edge costs, a collection D ⊆ V × V of ordered node pairs, and an integer k ≤ |D|, find a min-cost subgraph H of G that contains an st-path for (at least) k pairs (s, t) ∊ D. When k = |D|, we get the Directed Steiner Forest (DSF) problem. The best known approximation ratios for these problems are: for k-DSF by Charikar et al. [2], and O(k1/2+∊) for DSF by Chekuri et al. [3]. For DSF we give an O(n∊.min{n4/5, m2/3})-approximation scheme using a novel LP-relaxation seeking to connect pairs via “cheap” paths. This is the first sub-linear (in terms of n = |V|) approximation ratio for the problem. For k-DSF we give a simple greedy O(k1/2+∊)-approximation scheme, improving the best known ratio by Charikar et al. [2], and (almost) matching, in terms of k, the best ratio known for the undirected variant [11]. Even when used for the particular case DSF, our algorithm favorably compares to the one of [3], which repeatedly solves linear programs, and uses complex time and space consuming transformations.

We present a 1.8-approximation algorithm for the following NP-hard problem: Given a connected graph G = ( V , E ) and an edge set E on V disjoint to E , find a minimum-size subset of edges F ⊆ E such that ( V , E ∪ F ) is 2-edge-connected. Our result improves and significantly simplifies the approximation algorithm with ratio 1.875 + ε of Nagamochi.

We consider a general class of scheduling problems where a set of conflicting jobs needs to be scheduled (preemptively or nonpreemptively) on a set of machines so as to minimize the weighted sum of completion times. The conflicts among jobs are formed as an arbitrary conflict graph. Building on the framework of Queyranne and Sviridenko [2002b], we present a general technique for reducing the weighted sum of completion-times problem to the classical makespan minimization problem. Using this technique, we improve the best-known results for scheduling conflicting jobs with the min-sum objective, on several fundamental classes of graphs, including line graphs, ( k + 1)-claw-free graphs, and perfect graphs. In particular, we obtain the first constant-factor approximation ratio for nonpreemptive scheduling on interval graphs. We also improve the results of Kim [2003] for scheduling jobs on line graphs and for resource-constrained scheduling.

The natural relaxation for the group Steiner tree problem, as well as for its generalization, the directed Steiner tree problem, is a flow‐based linear programming relaxation. We prove new lower bounds on the integrality ratio of this relaxation. For the group Steiner tree problem, we show that the integrality ratio is $\Omega(\log^2 k)$, where k denotes the number of groups; this holds even for input graphs that are hierarchically well‐separated trees, introduced by Bartal [in Proceedings of the 37th Annual IEEE Symposium on Foundations of Computer Science, 1996, pp. 184–193], in which case this lower bound is tight. This also applies for the directed Steiner tree problem. In terms of the number n of vertices, our results for the directed Steiner problem imply an $\Omega(\frac{\log^2 n}{(\log \log n)^2})$ integrality ratio. For both problems, these are the first lower bounds on the integrality ratio that are superlogarithmic in the input size. This exhibits, for the first time, a relaxation of a natural optimization problem whose integrality ratio is known to be superlogarithmic but subpolynomial. Our results and techniques have been used by Halperin and Krauthgamer [in Proceedings of the 35th Annual ACM Symposium on Theory of Computing, 2003, pp. 585–594] to show comparable inapproximability results, assuming that NP has no quasi‐polynomial Las Vegas algorithms. We also show algorithmically that the integrality ratio for the group Steiner tree problem is much better for certain families of instances, which helps pinpoint the types of instances (parametrized by optimal solutions to their flow‐based relaxations) that appear to be most difficult to approximate.

The achromatic number of a graph $G = (V,E)$ with $|V| = n$ vertices is the largest number k with the following property: the vertices of G can be partitioned into k independent subsets $\{V_i\}_{1 \leq i \leq k}$ such that for every distinct pair of subsets $V_i,V_j$ in the partition, there is at least one edge in E that connects these subsets. We describe a greedy algorithm that computes the achromatic number of a bipartite graph within a factor of $O(n^{4/5})$ of the optimal. Prior to our work, the best known approximation factor for this problem was $n \log\log n /\log n$ as shown by Kortsarz and Krauthgamer [SIAM J. Discrete Math., 14 (2001), pp. 408–422].

We show that for every radio network G = (V, E) and source s ∈ V, there exists a radio broadcast schedule for G of length Rad(G, s) + O(√Rad(G, s) ⋅log2 n) = O(Rad(G, s) + log4 n), where Rad(G, s) is the radius of the radio network G with respect to the source s. This result improves the previously best-known upper bound of O(Rad(G, s) + log5 n) due to Gaber and Mansour [1995].

We consider approximation algorithms for non-uniform buy-at-bulk network design problems. The first non-trivial approximation algorithm for this problem is due to Charikar and Karagiozova (STOC 05); for an instance on h pairs their algorithm has an approximation guarantee of exp(O(radic(log h log log h)))for the uniform-demand case, and log D middot exp(O(radic(log h log log h))) for the general demand case, where D is the total demand. We improve upon this result, by presenting the first poly-logarithmic approximation for this problem. The ratio we obtain is O(log3h middot min{log D, gamma(h2)}) where his the number of pairs and gamma(n) is the worst case distortion in embedding the metric induced by a n vertex graph into a distribution over its spanning trees. Using the best known upper bound on gamma(n) we obtain an O(min{log3h middot log D, log5h log log h}) ratio approximation. We also give poly-logarithmic approximations for some variants of the single-source problem that we need for the multicommodity problem

The data migration problem is to compute an efficient plan for moving data stored on devices in a network from one configuration to another. We consider this problem with the objective of minimizing the sum of completion times of all storage devices. It is modeled by a transfer graph, where vertices represent the storage devices, and the edges indicate the data transfers required between pairs of devices. Each vertex has a nonnegative weight, and each edge has a release time and a processing time. A vertex completes when all the edges incident on it complete; the constraint is that two edges incident on the same vertex cannot be processed simultaneously. The objective is to minimize the sum of weighted completion times of all vertices. Kim ( Journal of Algorithms, 55:42--57, 2005 ) gave a 9-approximation algorithm for the problem when edges have arbitrary processing times and are released at time zero. We improve Kim's result by giving a 5.06-approximation algorithm. We also address the open shop scheduling problem, O | r j | ∑ w j C j , and show that it is a special case of the data migration problem. Queyranne and Sviridenko ( Journal of Scheduling, 5:287-305, 2002 ) gave a 5.83-approximation algorithm for the nonpreemptive version of the open shop problem. They state as an obvious open question whether there exists an algorithm for open shop scheduling that gives a performance guarantee better than 5.83. Our 5.06 algorithm for data migration proves the existence of such an algorithm. Crucial to our improved result is a property of the linear programming relaxation for the problem. Similar linear programs have been used for various other scheduling problems. Our technique may be useful in obtaining improved results for these problems as well.

In the ASYMMETRIC k -CENTER problem, the input is an integer k and a complete digraph over n points together with a distance function obeying the directed triangle inequality. The goal is to choose a set of k points to serve as centers and to assign all the points to the centers, so that the maximum distance of any point from its center is as small as possible.We show that the ASYMMETRIC k -CENTER problem is hard to approximate up to a factor of log * n − O (1) unless NP ⊆ DTIME ( n log log n ). Since an O (log * n )-approximation algorithm is known for this problem, this resolves the asymptotic approximability of ASYMMETRIC k -CENTER. This is the first natural problem whose approximability threshold does not polynomially relate to the known approximation classes. We also resolve the approximability threshold of the metric (symmetric) k -Center problem with costs.

Abstract The Directed Multicut (DM) problem is: given a simple directed graph G = ( V , E ) with positive capacities u e on the edges, and a set K ⊆ V × V of ordered pairs of nodes of G , find a minimum capacity K ‐multicut; C ⊆ E is a K ‐multicut if in G − C there is no ( s , t )‐path for any ( s , t ) ⫅ K . In the uncapacitated case (UDM) the goal is to find a minimum size K ‐multicut. The best approximation ratio known for DM is $O(\min\{\sqrt{n},opt\})$ by Gupta, where n = | V |, and opt is the optimal solution value. All known nontrivial approximation algorithms for the problem solve large linear programs. We give the first combinatorial approximation algorithms for the problem. Our main result is an Õ ( n 2/3 / opt 1/3 )‐approximation algorithm for UDM, which improves the $O(\min\{opt,\sqrt{n}\})$ ‐approximation for opt = Ω( n 1/2+ϵ ). Combined with the article of Gupta, we get that UDM can be approximated within better than $O(\sqrt n)$ , unless $opt={\tilde \Theta}(\sqrt n)$ . We also give a simple and fast O ( n 2/3 )‐approximation algorithm for DM. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 45(4), 214–217 2005

Consider a synchronous network of processors, modeled by directed or undirected graph $G = (V,E)$, in which in each round every processor is allowed to choose one of its neighbors and to send a message to this neighbor. Given a processor $s \in V$ and a subset $T \subseteq V$ of processors, the telephone multicast problem requires computing the shortest schedule (in terms of the number of rounds) that delivers a message from s to all the processors of T. The particular case $T = V$ is called the telephone broadcast problem. These problems have multiple applications in distributed computing. Several approximation algorithms with polylogarithmic ratio, including one with logarithmic ratio, for the undirected variants of these problems are known. However, all these algorithms involve solving large linear programs. Devising a polylogarithmic approximation algorithm for the directed variants of these problems is an open problem, posed by Ravi in [Proceedings of the 35th Annual IEEE Symposium on Foundations of Computer Science, (FOCS '94), 1994, pp. 202-213]. We devise a combinatorial logarithmic approximation algorithm for these problems that applies also for the directed broadcast problem. Our algorithm has significantly smaller running time and seems to reveal more information about the combinatorial structure of the solution than the previous algorithms that are based on linear programming. We also improve the lower bounds on the approximation threshold of these problems. Both problems are known to be 3/2-inapproximable. For the undirected (resp., directed) broadcast problem we show that it is NP-hard (resp., impossible unless $NP \subseteq DTIME(n^{O(\log n)})$) to approximate it within a ratio of $3 - \epsilon$ for any $\epsilon > 0$ (resp., $\Omega(\sqrt{\log n})$).

We present two new approximation algorithms for the problem of finding a k-node connected spanning subgraph (directed or undirected) of minimum cost. The best known approximation guarantees for this problem were $O(\min \{k,\frac{n}{\sqrt{n-k}}\})$ for both directed and undirected graphs, and $O(\ln k)$ for undirected graphs with $n \geq 6k^2$, where n is the number of nodes in the input graph. Our first algorithm has approximation ratio $O(\frac{n}{n-k}\ln^2 k)$, which is $O(\ln^2 k)$ except for very large values of k, namely, $k=n-o(n)$. This algorithm is based on a new result on $\ell$-connected p-critical graphs, which is of independent interest in the context of graph theory. Our second algorithm uses the primal-dual method and has approximation ratio $O(\sqrt{n} \ln k)$ for all values of $n,k$. Combining these two gives an algorithm with approximation ratio $O(\ln k \cdot \min \{\sqrt{k},\frac{n}{n-k} \ln k\})$, which asymptotically improves the best known approximation guarantee for directed graphs for all values of $n,k$, and for undirected graphs for $k>\sqrt{n/6}$. Moreover, this is the first algorithm that has an approximation guarantee better than $\Theta(k)$ for all values of $n,k$. Our approximation ratio also provides an upper bound on the integrality gap of the standard LP-relaxation.

The input for the radio broadcast problem is an undirected n-vertex graph G and a source node s. The goal is to send a message from s to the rest of the vertices in the minimum number of rounds. In a round, a vertex receives the message only if exactly one of its neighbors transmits. The radio broadcast problem admits an $O(\log^2 n)$ approximation [CW-87,KP-04]. [I. Chlamtac and O. Weinstein, in Proceedings of the IEEE INFOCOM, 1987, pp. 874-881; D. Kowalski and A. Pelc, in APPROX-RANDOM, Lecture Notes in Comput. Sci. 3122, Springer, Berlin, 2004, pp. 171-182]. In this paper we consider the additive approximation ratio of the problem. We prove that there exists a constant c so that the problem cannot be approximated within an additive term of $c\log^2 n$, unless $NP\subseteq BTIME(n^{O(\log\log n)})$.

Network design problems, such as generalizations of the Steiner Tree Problem, can be cast as edge-cost-flow problems. An edge-cost flow problem is a min-cost flow problem in which the cost of the flow equals the sum of the costs of the edges carrying positive flow.We prove a hardness result for the Minimum Edge Cost Flow Problem (MECF). Using the one-round two-prover scenario, we prove that MECF does not admit a 2 log 1-ε n -ratio approximation, for every constant ε > 0, unless NP ⊆ DTIME ( n polylogn ).A restricted version of MECF, called Infinite Capacity MECF (ICF), is defined. The ICF problem is defined as follows: (i) all edges have infinite capacity, (ii) there are multiple sources and sinks, where flow can be delivered from every source to every sink, (iii) each source and sink has a supply amount and demand amount, respectively, and (iv) the required total flow is given as part of the input. The goal is to find a minimum edge-cost flow that meets the required total flow while obeying the demands of the sinks and the supplies of the sources. This problem naturally arises in practical scheduling applications, and is equivalent to the special case of single source MECF, with all edges not touching the source or the sink having infinite capacity.The directed ICF generalizes the Covering Steiner Problem in directed and undirected graphs. The undirected version of ICF generalizes several network design problems, such as: Steiner Tree Problem, k -MST, Point-to-point Connection Problem, and the generalized Steiner Tree Problem.An O (log x )-approximation algorithm for undirected ICF is presented. We also present a bi-criteria approximation algorithm for directed ICF. The algorithm for directed ICF finds a flow that delivers half the required flow at a cost that is at most O ( n ε /ε 4 ) times bigger than the cost of an optimal flow. The running time of the algorithm is O ( x 2/ε ċ n 1+1/ε ), where x denotes the required total flow.Randomized approximation algorithms for the Covering Steiner Problem in directed and undirected graphs are presented. The algorithms are based on a randomized reduction to a problem called 1/2-Group Steiner. In undirected graphs, the approximation ratio matches the approximation ratio of Konjevod et al. [2002]. However, our algorithm is much simpler. In directed graphs, the algorithm is the first nontrivial approximation algorithm for the Covering Steiner Problem. Deterministic algorithms are obtained by derandomization.

In the Asymmetric k-Center problem, the input is an integer k and a complete digraph over n points together with a distance function obeying the directed triangle inequality. The goal is to choose a set of k points to serve as centers and to assign all the points to the centers, so that the maximum distance of any point to its center is as small as possible. We show that the Asymmetric k-Center problem is hard to approximate up to a factor of log* n - Θ(1) unless NP ⊆ DTIME(nlog log n). Since an O(log* n)-approximation algorithm is known for this problem, this essentially resolves the approximability of this problem. This is the first natural problem whose approximability threshold does not polynomially relate to the known approximation classes. We also resolve the approximability threshold of the metric k-Center problem with costs.

We present two new approximation algorithms for the problem of finding a k-node connected spanning subgraph (directed or undirected) of minimum cost. The best known ap-n proximation guarantees for this problem were O(min{k, for both directed and undirected graphs, and O(ln k) for undirected graphs with n ≥ 6k 2, where n is the number of nodes in the input graph. Our first algorithm has approximation ratio O ( k n−k ln2 k), which is O(ln 2 k) except for very large values of k, namely, k = n − o(n). This algorithm is based on a new result on ℓ-connected p-critical graphs, which is of independent interest in the context of graph theory. Our second algorithm uses the primal-dual method and has approximation ratio O ( √ n ln k) for all values of n, k. Combining these two gives an algorithm with approximation ratio O(ln k · min { √ k k, ln k}), which asymptotically im-n−k proves the best known approximation guarantee for directed graphs for all values of n, k, and for undirected graphs for k> n/6. Moreover, this is the first algorithm that has an n−k approximation guarantee better than Θ(k) for all values of n, k. Our approximation ratio also provides an upper bound on the integrality gap of the standard LP-relaxation to the problem. As a byproduct, we also get the following result which is of independent interest. To get a faster implementation of our algorithms, we consider the problem of adding a minimumcost edge set to increase the outconnectivity of a directed graph by ∆; a graph is said to be ℓ-outconnected from its node r if it contains ℓ internally disjoint paths from r to any other node. The best known time complexity for the later problem is O(m 3). For the particular case of ∆ = 1, we give a primal-dual algorithm with running time O(m 2). Categories and Subject Descriptors

In the survivable networkdesign problem (SNDP), the goal is to find a minimum-cost spanning subgraph satisfying certain connectivity requirements. We study the vertex-connectivity variant of SNDP in which the input specifies, for each pair of vertices, a required number of vertex-disjoint paths connecting them. We give the first strong lower bound on the approximability of SNDP, showing that the problem admits no efficient $2^{\log^{1-\epsilon} n}$ ratio approximation for any fixed $\epsilon\! >\! 0$, unless $\NP\subseteq \DTIME(n^{\polylog(n)})$. We show hardness of approximation results for some important special cases of SNDP, and we exhibit the first lower bound on the approximability of the related classical NP-hard problem of augmenting the connectivity of a graph using edges from a given set.

(MATH) Consider a synchronous network of processors, modeled by directed or undirected graph G = (V,E), in which on each round every processor is allowed to choose one of its neighbors and to send him a message. Given a processor s ε V, and a subset T ⊆ V of processors, the telephone multicast problem requires to compute the shortest schedule (in terms of the number of rounds) that delivers a message from s to all the processors of T. The particular case T = V is called telephone broadcast problem.These problems have multiple applications in distributed computing. Several approximation algorithms with polylogarithmic ratio, including one with logarithmic ratio, for the undirected variants of these problems are known. However, all these algorithms involve solving large linear programs. Devising a polylogarithmic approximation algorithm for the directed variants of these problems is anopen problem, posed in [15].We devise a combinatorial logarithmic approximation algorithm for these problems, that applies also for the directed broadcast problem. Our algorithm has significantly smaller running time, and seems to reveal more information about the combinatorial structure of the solution, than the previous algorithms, that are based on linear programming.(MATH) We also improve the lower bounds on the approximation threshold of these problems. Both problems are known to be 3/2-inapproximable. For the undirected (resp., directed) broadcast problem we show that it is NP-hard (resp., impossible unless $NP ⊇ DTIME(nO(log n))) to approximate it within a ratio of 3 —ε for any ε ρ 0 (resp., ω(\sqrt log n)).Finally, we study the radio broadcast problem. Its setting is similar to the telephone broadcast problem, but in every round every processor may either send a message to all its neighbors or may not send it at all. A processor is informed in a certain round if and only if it receives a message from precisely one neighbor.(MATH) This problem was known to admit O(log2 n)-approximation algorithm, but no hardness of approximation was known. In this paper we show that the problem is ω(log n)-inapproximable unless NP ⊆ BPTIME(nlog log n}).

A set of vertices in a graph is a dominating set if every vertex outside the set has a neighbor in the set. The domatic number problem is that of partitioning the vertices of a graph into the maximum number of disjoint dominating sets. Let n denote the number of vertices, $\delta$ the minimum degree, and $\Delta$ the maximum degree. We show that every graph has a domatic partition with $(1 - o(1))(\delta + 1)/\ln n$ dominating sets and, moreover, that such a domatic partition can be found in polynomial-time. This implies a $(1 + o(1))\ln n$-approximation algorithm for domatic number, since the domatic number is always at most $\delta + 1$. We also show this to be essentially best possible. Namely, extending the approximation hardness of set cover by combining multiprover protocols with zero-knowledge techniques, we show that for every $\epsilon > 0$, a $(1 - \epsilon)\ln n$-approximation implies that $NP \subseteq DTIME(n^{O(\log\log n)})$. This makes domatic number the first natural maximization problem (known to the authors) that is provably approximable to within polylogarithmic factors but no better. We also show that every graph has a domatic partition with $(1 - o(1))(\delta + 1)/\ln \Delta$ dominating sets, where the "o(1)" term goes to zero as $\Delta$ increases. This can be turned into an efficient algorithm that produces a domatic partition of $\Omega(\delta/\ln \Delta)$ sets.

The achromatic number problem is to legally color the vertices of an input graph with the maximum number of colors, denoted $\psi^*$, so that every two color classes share at least one edge. This problem is known to be NP-hard. For general graphs we give an algorithm that approximates the achromatic number within a ratio of $O(n\cdot \log\log n/\log n)$. This improves over the previously known approximation ratio of $O(n/\sqrt{\log n})$, due to Chaudhary and Vishwanathan [{\it Proceedings of the Eighth Annual ACM-SIAM Symposium on Discrete Algorithms}, New Orleans, LA, 1997, pp. 558--563]. For graphs of girth at least 5 we give an algorithm with an approximation ratio $O(\min\{n^{1/3},\sqrt{\psi^*}\})$. This improves over an approximation ratio $O(\sqrt{\psi^*})=O(n^{3/8})$ for the more restricted case of graphs with girth at least 6, due to Krysta and Lory{ś [Proceedings of the Seventh Annual European Symposium on Algorithms, Lecture Notes in Comput. Sci. 1643, Springer-Verlag, Berlin, 1999, pp.402--413]. We also give the first hardness result for approximating the achromatic number. We show that for every fixed $\epsilon > 0$ there is no $2-\epsilon$ approximation algorithm, unless P=NP.

A set of vertices in a graph is a dominating set if every vertex outside the set has aneighbor in the set. The domatic number problem is that of partitioning the vertices of a graph into the maximum number of disjoint dominating sets. Let n denote the number ofvertices, ffi the minimum degree, and \\Delta the maximum degree.We show that every graph has a domatic partition with (1-o(1))(ffi + 1) / ln n dominatingsets, and moreover, that such a domatic partition can be found in polynomial time. This implies a (1 + o(1)) ln n approximation algorithm for domatic number, since the domaticnumber is always at most ffi + 1. We also show this to be essentially best possible. Namely,extending the approximation hardness of set cover by combining multi-prover protocols with zero-knowledge techniques, we show that for every ffl> 0, a (1- ffl) ln n-approximation impliesthat N P ` DT IM E(nO(log log n)). This makes domatic number the first natural maximiza-tion problem (known to the authors) that is provably approximable to within polylogarithmic factors but no better.We also show that every graph has a domatic partition with (1-o(1))(ffi + 1) / ln \\Delta dominating sets, where the " o(1) " term goes to zero as \\Delta increases. This can be turned intoan efficient algorithm that produces a domatic partition of \\Omega ( ffi / ln \\Delta) sets.

A k-spanner of a connected (undirected unweighted) graph G=(V,E) is a subgraph G' consisting of all the vertices of V and a subset of the edges, with the additional property that the distance between any two vertices in G' is larger than that distance in G by no more than a factor of k. This paper is concerned with approximating the problem of finding a 2-spanner in a given graph, with minimum maximum degree. We first show that the problem is at least as hard to approximate as set cover. Then a randomized approximation algorithm is provided for this problem, with approximation ratio of $\tilde O(\Delta^{1/4})$. We then present a probabilistic algorithm that is more efficient for sparse graphs. Our algorithms are converted into deterministic ones using derandomization.

This paper deals with the problem of broadcasting in minimum time in the telephone and message-passing models. Approximation algorithms are developed for arbitrary graphs as well as for several restricted graph classes. In particular, an $O( \sqrt{n} )$-additive approximation algorithm is given for broadcasting in general graphs, and an $O( \log n/\log \log n )$ (multiplicative) ratio approximation is given for broadcasting in the open-path model. This also results in an algorithm for broadcasting on random graphs (in the telephone and message-passing models) that yields an $O( \log n/\log \log n )$ approximation with high probability. In addition, the paper presents a broadcast algorithm for graph families with small separators (such as chordal, k-outerplanar, bounded-face planar, and series-parallel graphs), with approximation ratio proportional to the separator size times $\log n$. Finally, an efficient approximation algorithm is presented for the class of graphs representable as trees of cliques.

This paper concerns the problem of computing the densest k-vertex subgraph of a given graph, namely, the subgraph with the most edges, or with the highest edges-to-vertices ratio. A sequence of approximation algorithms is developed for the problem, with each step yielding a better ratio at the cost of a more complicated solution. The approximation ratio of our final algorithm is O/spl tilde/(n/sup 0.3885/). We also present a method for converting an approximation algorithm for an unweighted graph problem (from a specific class of maximization problems) into one for the corresponding weighted problem, and apply it to the densest subgraph problem.>