Polylogarithmic Approximation Algorithm Research Articles

Polynomial Integrality Gap of Flow LP for Directed Steiner Tree

In the Directed Steiner Tree (DST) problem, we are given a directed graph \(G=(V,E)\) on \(n\) vertices with edge-costs \(c\in{\mathbb{R}}_{\geq 0}^{E}\) , a root vertex \(r\in V\) , and a set \(K\subseteq V\setminus\{r\}\) of \(k\) terminals. The goal is to find a minimum-cost subgraph of \(G\) that contains a path from \(r\) to every terminal \(t\in K\) . DST has been a notorious problem for decades as there is a large gap between the best-known polynomial-time approximation ratio of \(O(k^{\epsilon})\) for any constant \(\epsilon \gt 0\) , and the best quasi-polynomial-time approximation ratio of \(O\left(\frac{\log^{2}k}{\log\log k}\right)\) . Toward understanding this gap, we study the integrality gap of the standard flow linear programming relaxation for the problem. We show that the linear program (LP) has an integrality gap of \(\Omega(n^{0.0418})\) . Previously, the integrality gap of the LP is only known to be \(\Omega\left(\frac{\log^{2}n}{\log\log n}\right)\) [Halperin et al., SODA’03 & SIAM J. Comput.] and \(\Omega(\sqrt{k})\) [Zosin-Khuller, SODA’02] in some instance with \(\sqrt{k}=O\left(\frac{\log n}{\log\log n}\right)\) . Our result gives the first known lower bound on the integrality gap of this standard LP that is polynomial in \(n\) , the number of vertices. Consequently, we rule out the possibility of developing a poly-logarithmic approximation algorithm for the problem based on the flow LP relaxation.

A polylogarithmic approximation algorithm for 2-edge-connected dominating set

In the Connected Dominating Set (CDS) problem we are given a graph G and seek a min-size dominating set S such that the subgraph G[S] of G induced by S is connected. In the 2-Edge-Connected Dominating Set problem G[S] should be 2-edge-connected. We give the first non-trivial approximation algorithm for this problem, with expected approximation ratio O(log2⁡n⋅log⁡log⁡n⋅(log⁡log⁡log⁡n)3). We also show that the Subset Steiner CDS problem is approximation equivalent to the Group Steiner Tree problem.

Approximation algorithms for connected maximum cut and related problems

An instance of the Connected Maximum Cut problem consists of an undirected graph G=(V,E) and the goal is to find a subset of vertices S⊆V that maximizes the number of edges in the cut δ(S) such that the induced graph G[S] is connected. We present the first non-trivial Ω(1log⁡n) approximation algorithm for the Connected Maximum Cut problem in general graphs using novel techniques. We then extend our algorithm to edge weighted case and obtain a poly-logarithmic approximation algorithm. Interestingly, in contrast to the classical Max-Cut problem that can be solved in polynomial time on planar graphs, we show that the Connected Maximum Cut problem remains NP-hard on unweighted, planar graphs. On the positive side, we obtain a polynomial time approximation scheme for the Connected Maximum Cut problem on planar graphs and more generally on bounded genus graphs.

Approximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems

We consider the problem of constructing optimal decision trees: given a collection of tests that can disambiguate between a set of m possible diseases, each test having a cost, and the a priori likelihood of any particular disease, what is a good adaptive strategy to perform these tests to minimize the expected cost to identify the disease? This problem has been studied in several works, with O(log m)-approximations known in the special cases when either costs or probabilities are uniform. In this paper, we settle the approximability of the general problem by giving a tight O(log m)-approximation algorithm. We also consider a substantial generalization, the adaptive traveling salesman problem. Given an underlying metric space, a random subset S of vertices is drawn from a known distribution, but S is initially unknown—we get information about whether any vertex is in S only when it is visited. What is a good adaptive strategy to visit all vertices in the random subset S while minimizing the expected distance traveled? This problem has applications in routing message ferries in ad hoc networks and also models switching costs between tests in the optimal decision tree problem. We give a polylogarithmic approximation algorithm for adaptive TSP, which is nearly best possible due to a connection to the well-known group Steiner tree problem. Finally, we consider the related adaptive traveling repairman problem, where the goal is to compute an adaptive tour minimizing the expected sum of arrival times of vertices in the random subset S; we obtain a polylogarithmic approximation algorithm for this problem as well.

A Polylogarithmic Approximation Algorithm for Edge-Disjoint Paths with Congestion 2

In the Edge-Disjoint Paths with Congestion problem (EDPwC), we are given an undirected n -vertex graph G , a collection M ={ ( s 1 , t 1 ),… ,( s k , t k ) } of pairs of vertices called demand pairs, and an integer c . The goal is to connect the maximum possible number of the demand pairs by paths, so that the maximum edge congestion - the number of paths sharing any edge - is bounded by c . When the maximum allowed congestion is c = 1, this is the classical Edge-Disjoint Paths problem (EDP). The best current approximation algorithm for EDP achieves an O (√ n )-approximation by rounding the standard multi-commodity flow relaxation of the problem. This matches the Ω (√ n ) lower bound on the integrality gap of this relaxation. We show an O (poly log k )-approximation algorithm for EDPwC with congestion c = 2 by rounding the same multi-commodity flow relaxation. This gives the best possible congestion for a sub-polynomial approximation of EDPwC via this relaxation. Our results are also close to optimal in terms of the number of pairs routed, since EDPwC is known to be hard to approximate to within a factor of ~ Ω ((log n ) 1/( c +1) ) for any constant congestion c . Prior to our work, the best approximation factor for EDPwC with congestion 2 was Õ( n 3/7 ), and the best algorithm achieving a polylogarithmic approximation required congestion 14.

Minimum-Cost Network Design with (Dis)economies of Scale

Given a network, a set of demands, and a cost function $f(\cdot)$, the min-cost network design problem is to route all demands with the objective of minimizing $\sum_e f(\ell_e)$, where $\ell_e$ is the total traffic load under the routing. We focus on cost functions of the form $f(x)= \sigma + x^{\alpha}$ for $x > 0$, with $f(0) = 0$. For $\alpha \le 1$, $f(\cdot)$ is subadditive. This case corresponds to the well-studied buy-at-bulk network design problem and admits polylogarithmic approximation and hardness. In this paper, we focus on the less-studied scenario of $\alpha > 1$ with a positive start-up cost $\sigma > 0$. Now, the cost function $f(\cdot)$ is neither subadditive nor superadditive. It aims to model a range of computing and communication devices for which doubling processing speed more than doubles their power consumption. We begin by discussing why existing routing techniques such as randomized rounding and tree-metric embedding fail to generalize directly. We then present our main contribution, which is a polylogarithmic approximation algorithm. We obtain this result by first deriving a bicriteria approximation for a related capacitated min-cost flow problem that we believe is interesting in its own right. Our approach for this problem builds upon the well-linked decomposition due to Chekuri, Khanna, and Shepherd [Proceedings of ACM STOC, ACM, New York, pp. 183--192], the construction of expanders via matchings due to Khandekar, Rao, and Vazirani [J. ACM, 56 (2009), 19], and edge-disjoint routing in well-connected graphs due to Rao and Zhou [SIAM J. Comput., 39 (2010), pp. 1856--1887]. However, we also develop new techniques that allow us to keep a handle on the total cost, which was not a concern in the aforementioned literature.

Approximation Algorithms and Hardness of the k -Route Cut Problem

We study the k -route cut problem: given an undirected edge-weighted graph G = ( V , E ), a collection {( s 1 , t 1 ), ( s 2 , t 2 ), …, ( s r , t r )} of source-sink pairs, and an integer connectivity requirement k , the goal is to find a minimum-weight subset E ′ of edges to remove, such that the connectivity of every pair ( s i , t i ) falls below k . Specifically, in the edge-connectivity version, EC-kRC, the requirement is that there are at most ( k − 1) edge-disjoint paths connecting s i to t i in G ∖ E ′, while in the vertex-connectivity version, VC-kRC, the same requirement is for vertex-disjoint paths. Prior to our work, poly-logarithmic approximation algorithms have been known for the special case where k ⩽ 3, but no non-trivial approximation algorithms were known for any value k > 3, except in the single-source setting. We show an O ( k log 3/2 r )-approximation algorithm for EC-kRC with uniform edge weights, and several polylogarithmic bi-criteria approximation algorithms for EC-kRC and VC-kRC, where the connectivity requirement k is violated by a constant factor. We complement these upper bounds by proving that VC-kRC is hard to approximate to within a factor of k ϵ for some fixed ϵ > 0. We then turn to study a simpler version of VC-kRC, where only one source-sink pair is present. We give a simple bi-criteria approximation algorithm for this case, and show evidence that even this restricted version of the problem may be hard to approximate. For example, we prove that the single source-sink pair version of VC-kRC has no constant-factor approximation, assuming Feige’s Random κ-AND assumption.

Approximation algorithms for the arc orienteering problem

In this article we present approximation algorithms for the Arc Orienteering Problem (AOP). We propose a polylogarithmic approximation algorithm in directed graphs, while in undirected graphs we give a (6+ϵ+o(1)) and a (4+ϵ)-approximation algorithm for arbitrary instances and instances of unit profit, respectively. Also, an inapproximability result for the AOP is obtained as well as approximation algorithms for the Mixed Orienteering Problem.

Approximation Algorithms for the Directed k-Tour and k-Stroll Problems

We consider two natural generalizations of the Asymmetric Traveling Salesman problem: the k-Stroll and the k-Tour problems. The input to the k-Stroll problem is a directed n-vertex graph with nonnegative edge lengths, an integer k, as well as two special vertices s and t. The goal is to find a minimum-length s-t walk, containing at least k distinct vertices (including the endpoints s,t). The k-Tour problem can be viewed as a special case of k-Stroll, where s=t. That is, the walk is required to be a tour, containing some pre-specified vertex s. When k=n, the k-Stroll problem becomes equivalent to Asymmetric Traveling Salesman Path, and k-Tour to Asymmetric Traveling Salesman.Our main result is a polylogarithmic approximation algorithm for the k-Stroll problem. Prior to our work, only bicriteria (O(log2 k),3)-approximation algorithms have been known, producing walks whose length is bounded by 3OPT, while the number of vertices visited is Ω(k/log2 k). We also show a simple O(log2 n/loglogn)-approximation algorithm for the k-Tour problem. The best previously known approximation algorithms achieved min(O(log3 k),O(log2 n⋅logk/loglogn)) approximation in polynomial time, and O(log2 k) approximation in quasipolynomial time.

The Directed Orienteering Problem

This paper studies vehicle routing problems on asymmetric metrics. Our starting point is the directed k-TSP problem: given an asymmetric metric (V,d), a root r∈V and a target k≤|V|, compute the minimum length tour that contains r and at least k other vertices. We present a polynomial time $O(\frac{\log^{2} n}{\log\log n}\cdot\log k)$ -approximation algorithm for this problem. We use this algorithm for directed k-TSP to obtain an $O(\frac{\log^{2} n}{\log\log n})$ -approximation algorithm for the directed orienteering problem. This answers positively, the question of poly-logarithmic approximability of directed orienteering, an open problem from Blum et al. (SIAM J. Comput. 37(2):653–670, 2007). The previously best known results were quasi-polynomial time algorithms with approximation guarantees of O(log 2 k) for directed k-TSP, and O(log n) for directed orienteering (Chekuri and Pal in IEEE Symposium on Foundations in Computer Science, pp. 245–253, 2005). Using the algorithm for directed orienteering within the framework of Blum et al. (SIAM J. Comput. 37(2):653–670, 2007) and Bansal et al. (ACM Symposium on Theory of Computing, pp. 166–174, 2004), we also obtain poly-logarithmic approximation algorithms for the directed versions of discounted-reward TSP and vehicle routing problem with time-windows.

Set connectivity problems in undirected graphs and the directed steiner network problem

In the generalized connectivity problem, we are given an edge-weighted graph G = ( V , E ) and a collection D = {( S 1 , T 1 ), …, ( S k , T k )} of distinct demands each demand ( S i , T i ) is a pair of disjoint vertex subsets. We say that a subgraph F of G connects a demand ( S i , T i ) when it contains a path with one endpoint in S i and the other in T i . The goal is to identify a minimum weight subgraph that connects all demands in D . Alon et al. (SODA '04) introduced this problem to study online network formation settings and showed that it captures some well-studied problems such as Steiner forest, facility location with nonmetric costs, tree multicast, and group Steiner tree. Obtaining a nontrivial approximation ratio for generalized connectivity was left as an open problem. We describe the first poly-logarithmic approximation algorithm for generalized connectivity that has a performance guarantee of O (log 2 n log 2 k ). Here, n is the number of vertices in G and k is the number of demands. We also prove that the cut-covering relaxation of this problem has an O (log 3 n log 2 k ) integrality gap. Building upon the results for generalized connectivity, we obtain improved approximation algorithms for two problems that contain generalized connectivity as a special case. For the directed Steiner network problem, we obtain an O ( k 1/2 + ϵ ) approximation which improves on the currently best performance guarantee of Õ ( k 2/3 ) due to Charikar et al. (SODA '98). For the set connector problem, recently introduced by Fukunaga and Nagamochi (IPCO '07), we present a poly-logarithmic approximation; this result improves on the previously known ratio which can be Ω( n ) in the worst case.

Edge Disjoint Paths in Moderately Connected Graphs

We study the edge disjoint paths (EDP) problem in undirected graphs: Given a graph G with n nodes and a set $\mathcal{T}$ of pairs of terminals, connect as many terminal pairs as possible using paths that are mutually edge disjoint. This leads to a variety of classic NP-complete problems, for which approximability is not well understood. We show a polylogarithmic approximation algorithm for the undirected EDP problem in general graphs with a moderate restriction on graph connectivity; we require the global minimum cut of G to be $\Omega(\log^5n)$. Previously, constant or polylogarithmic approximation algorithms were known for trees with parallel edges, expanders, grids, grid-like graphs, and, most recently, even-degree planar graphs. These graphs either have special structure (e.g., they exclude minors) or have large numbers of short disjoint paths. Our algorithm extends previous techniques in that it applies to graphs with high diameters and asymptotically large minors.

Approximating Buy-at-Bulk and Shallow-Light k-Steiner Trees

We study two related network design problems with two cost functions. In the buy-at-bulk k-Steiner tree problem we are given a graph G(V,E) with a set of terminals T⊆V including a particular vertex s called the root, and an integer k≤|T|. There are two cost functions on the edges of G, a buy cost b:E→ℝ+ and a distance cost r:E→ℝ+. The goal is to find a subtree H of G rooted at s with at least k terminals so that the cost ∑e∈H b(e)+∑t∈T−s dist(t,s) is minimized, where dist(t,s) is the distance from t to s in H with respect to the r cost. We present an O(log 4 n)-approximation algorithm for the buy-at-bulk k-Steiner tree problem. The second and closely related one is bicriteria approximation algorithm for Shallow-light k-Steiner trees. In the shallow-light k-Steiner tree problem we are given a graph G with edge costs b(e) and distance costs r(e), and an integer k. Our goal is to find a minimum cost (under b-cost) k-Steiner tree such that the diameter under r-cost is at most some given bound D. We develop an (O(log n),O(log 3 n))-approximation algorithm for a relaxed version of Shallow-light k-Steiner tree where the solution has at least $\frac{k}{8}$terminals. Using this we obtain an (O(log 2 n),O(log 4 n))-approximation algorithm for the shallow-light k-Steiner tree and an O(log 4 n)-approximation algorithm for the buy-at-bulk k-Steiner tree problem. Our results are recently used to give the first polylogarithmic approximation algorithm for the non-uniform multicommodity buy-at-bulk problem (Chekuri, C., et al. in Proceedings of 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06), pp. 677–686, 2006).

A greedy approximation algorithm for the group Steiner problem

In the group Steiner problem we are given an edge-weighted graph G = ( V , E , w ) and m subsets of vertices { g i } i = 1 m . Each subset g i is called a group and the vertices in ⋃ i g i are called terminals. It is required to find a minimum weight tree that contains at least one terminal from every group. We present a poly-logarithmic ratio approximation for this problem when the input graph is a tree. Our algorithm is a recursive greedy algorithm adapted from the greedy algorithm for the directed Steiner tree problem [Approximating the weight of shallow Steiner trees, Discrete Appl. Math. 93 (1999) 265–285, Approximation algorithms for directed Steiner problems, J. Algorithms 33 (1999) 73–91]. This is in contrast to earlier algorithms that are based on rounding a linear programming based relaxation for the problem [A polylogarithmic approximation algorithm for the Group Steiner tree problem, J. Algorithms 37 (2000) 66–84, preliminary version in Proceedings of SODA, 1998 pp. 253–259, On directed Steiner trees, Proceedings of SODA, 2002, pp. 59–63]. We answer in positive a question posed in [A polylogarithmic approximation algorithm for the Group Steiner tree problem, J. Algorithms 37 (2000) 66–84, preliminary version in Proceedings of SODA, 1998 pp. 253–259] on whether there exist good approximation algorithms for the group Steiner problem that are not based on rounding linear programs. For every fixed constant ε > 0 , our algorithm gives an O ( ( log ∑ i | g i | ) 1 + ε · log m ) approximation in polynomial time. Approximation algorithms for trees can be extended to arbitrary undirected graphs by probabilistically approximating the graph by a tree. This results in an additional multiplicative factor of O ( log | V | ) in the approximation ratio, where | V | is the number of vertices in the graph. The approximation ratio of our algorithm on trees is slightly worse than the ratio of O ( log ( max i | g i | ) · log m ) provided by the LP based approaches.

A Combinatorial Logarithmic Approximation Algorithm for the Directed Telephone Broadcast Problem

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., $\Omega(\sqrt{\log n})$).

Approximation algorithms for the covering Steiner problem

AbstractThe covering Steiner problem is a generalization of both the k‐MST and the group Steiner problems: given an edge‐weighted graph, with subsets of vertices called the groups, and a nonnegative integer value (called the requirement) for each group, the problem is to find a minimum‐weight tree spanning at least the required number of vertices of every group. When all requirements are equal to 1, this becomes the group Steiner problem, while if there is only one group which contains all vertices of the graph the problem reduces to k‐MST with k equal to the requirement of this unique group. We discuss two different (but equivalent) linear relaxations of the problem for the case when the given graph is a tree and construct polylogarithmic approximation algorithms based on randomized LP rounding of these relaxations. By using a probabilistic approximation of general metrics by tree metrics due to Bartal, our algorithms also solve the covering Steiner problem on general graphs with a further polylogarithmic worsening in the approximation ratio. © 2002 Wiley Periodicals, Inc. Random Struct. Alg., 20:465–483, 2002