Fast algorithms for the dominating set problem on permutation graphs

AnO(n log logn) (resp.O(n² log² n)) algorithm is presented to solve the minimum cardinality (resp. weight) dominating set problem on permutation graphs, assuming the input is a permutation. The best-known previous algorithm was given by FÄrber and Keil, where they use dynamic programming to get anO(n² (resp.O(n³)) algorithm. Our improvement is based on the following three factors: (1) an observation on the order among the intermediate terms in the dynamic programming, (2) a new construction formula for the intermediate terms, and (3) efficient data structures for manipulating these terms.This research was supported in part by the National Science Foundation under Grant CCR-8905415 to Northwestern University.     

Finding a dominating set on bipartite graphs

Finding a dominating set of minimum cardinality is an NP-hard graph problem, even when the graph is bipartite. In this paper we are interested in solving the problem on graphs having a large independent set. Given a graph G with an independent set of size z, we show that the problem can be solved in time O^∗(²n−z), where n is the number of vertices of G. As a consequence, our algorithm is able to solve the dominating set problem on bipartite graphs in time O^∗(²n/2). Another implication is an algorithm for general graphs whose running time is O(n^1.7088).     

Efficient algorithms for clique problems

The k-clique problem is a cornerstone of NP-completeness and parametrized complexity. When k is a fixed constant, the asymptotically fastest known algorithm for finding a k-clique in an n-node graph runs in O(n0.792k) time (given by Nešet?il and Poljak). However, this algorithm is infamously inapplicable, as it relies on Coppersmith and Winograd's fast matrix multiplication.We present good combinatorial algorithms for solving k-clique problems. These algorithms do not require large constants in their runtime, they can be readily implemented in any reasonable random access model, and are very space-efficient compared to their algebraic counterparts. Our results are the following:

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We give an algorithm for k-clique that runs in O(nk/(εlogn)^k−1) time and O(nε) space, for all ε>0, on graphs with n nodes. This is the first algorithm to take o(nk) time and O(nc) space for c independent of k.

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Let k be even. Define a k-semiclique to be a k-node graph G that can be divided into two disjoint subgraphs U={u₁,…,uk/2} and V={v₁,…,vk/2} such that U and V are cliques, and for all i?j, the graph G contains the edge {ui,vj}. We give an time algorithm for determining if a graph has a k-semiclique. This yields an approximation algorithm for k-clique, in the following sense: if a given graph contains a k-clique, then our algorithm returns a subgraph with at least 3/4 of the edges in a k-clique.

     

Fast distributed algorithms for (weakly) connected dominating sets and linear-size skeletons

Motivated by routing issues in ad hoc networks, we present polylogarithmic-time distributed algorithms for two problems. Given a network, we first show how to compute connected and weakly connected dominating sets whose size is at most times the optimum, Δ being the maximum degree of the input network. This is best-possible if and if the processors are required to run in polynomial-time. We then show how to construct dominating sets that have the above properties, as well as the “low stretch” property that any two adjacent nodes in the network have their dominators at a distance of at most in the output network. (Given a dominating set S, a dominator of a vertex u is any v∈S such that the distance between u and v is at most one.) We also show our time bounds to be essentially optimal.     

On the complexity of preflow-push algorithms for maximum-flow problems

We study the maximum-flow algorithm of Goldberg and Tarjan and show that the largest-label implementation runs inO(n ² m) time. We give a new proof of this fact. We compare our proof with the earlier work by Cheriyan and Maheswari who showed that the largest-label implementation of the preflow-push algorithm of Goldberg and Tarjan runs inO(n ² m) time when implemented with current edges. Our proof that the number of nonsaturating pushes isO(n ² m), does not rely on implementing pushes with current edges, therefore it is true for a much larger family of largest-label implementation of the preflow-push algorithms.Research performed while the author was a Ph.D. student at Cornell University and was partially supported by the Ministry of Education of the Republic of Turkey through the scholarship program 1416.     

Subexponential algorithms for partial cover problems

Partial Cover problems are optimization versions of fundamental and well-studied problems like Vertex Cover and Dominating Set. Here one is interested in covering (or dominating) the maximum number of edges (or vertices) using a given number k of vertices, rather than covering all edges (or vertices). In general graphs, these problems are hard for parameterized complexity classes when parameterized by k. It was recently shown by Amini et al. (2008) [1] that Partial Vertex Cover and Partial Dominating Set are fixed parameter tractable on large classes of sparse graphs, namely H-minor-free graphs, which include planar graphs and graphs of bounded genus. In particular, it was shown that on planar graphs both problems can be solved in time ²O(k)nO(1).During the last decade there has been an extensive study on parameterized subexponential algorithms. In particular, it was shown that the classical Vertex Cover and Dominating Set problems can be solved in subexponential time on H-minor-free graphs. The techniques developed to obtain subexponential algorithms for classical problems do not apply to partial cover problems. It was left as an open problem by Amini et al. (2008) [1] whether there is a subexponential algorithm for Partial Vertex Cover and Partial Dominating Set. In this paper, we answer the question affirmatively by solving both problems in time not only on planar graphs but also on much larger classes of graphs, namely, apex-minor-free graphs. Compared to previously known algorithms for these problems our algorithms are significantly faster and simpler.     

Fast parallel algorithms for graph similarity and matching

This paper addresses the problem of global graph alignment on supercomputer-class clusters. We define the alignment of two graphs, as a mapping of each vertex in the first graph to a unique vertex in the second graph so as to optimize a given similarity-based cost function.¹ Using a state of the art serial algorithm for the computation of vertex similarity scores called Network Similarity Decomposition (NSD), we derive corresponding parallel formulations. Coupling this parallel similarity algorithm with a parallel auction-based bipartite matching technique, we obtain a highly efficient and scalable graph matching pipeline. We validate the performance of our integrated approach on a large parallel platform and on diverse graph instances (including Protein Interaction, Wikipedia and Web networks). Experimental results demonstrate that our algorithms scale to large machine configurations (thousands of cores) and problem instances, enabling the alignment of networks of sizes two orders of magnitude larger than reported in the current literature.     

Tutte type theorems for graphs having a perfect internal matching

Splitters are introduced to capture the meaning of barriers in graphs having a perfect internal matching. The factor-critical property is extended in a natural way to accommodate such graphs, and a characterization of factor-critical graphs is given in the new context. Two Tutte type theorems are presented for open graphs with perfect internal matchings, one on maximal splitters, and the other on maximal inaccessible splitters.     

Fast primal-dual distributed algorithms for scheduling and matching problems

In this paper we give efficient distributed algorithms computing approximate solutions to general scheduling and matching problems. All approximation guarantees are within a constant factor of the optimum. By “efficient”, we mean that the number of communication rounds is poly-logarithmic in the size of the input. In the scheduling problem, we have a bipartite graph with computing agents on one side and resources on the other. Agents that share a resource can communicate in one time step. Each agent has a list of jobs, each with its own length and profit, to be executed on a neighbouring resource within a given time-window. Each job is also associated with a rational number in the range between zero and one (width), specifying the amount of resource required by the job. Resources can execute non preemptively multiple jobs whose total width at any given time is at most one. The goal is to maximize the profit of the jobs that are scheduled. We then adapt our algorithm for scheduling, to solve the weighted b-matching problem, which is the generalization of the weighted matching problem where for each vertex v, at most b(v) edges incident to v, can be included in the matching. For this problem we obtain a randomized distributed algorithm with approximation guarantee of \frac16+e{\frac{1}{6+\epsilon}}, for any ${\epsilon >0 }${\epsilon >0 }. For weighted matching, we devise a deterministic distributed algorithm with the same approximation ratio. To our knowledge, we give the first distributed algorithm for the aforementioned scheduling problem as well as the first deterministic distributed algorithm for weighted matching with poly-logaritmic running time. A very interesting feature of our algorithms is that they are all derived in a systematic manner from primal-dual algorithms.     

Linear self-stabilizing algorithms for the independent and dominating set problems using an unfair distributed scheduler

This paper presents distributed self-stabilizing algorithms for the maximal independent and the minimal dominating set problems. Using an unfair distributed scheduler the algorithms stabilizes in at most max{3n−5,2n} resp. 9n moves. All previously known algorithms required O(n²) moves.     

Enhanced algorithms for Local Search

Let G=(V,E) be a finite graph, and be any function. The Local Search problem consists in finding a local minimum of the function f on G, that is a vertex v such that f(v) is not larger than the value of f on the neighbors of v in G. In this note, we first prove a separation theorem slightly stronger than the one of Gilbert, Hutchinson and Tarjan for graphs of constant genus. This result allows us to enhance a previously known deterministic algorithm for Local Search with query complexity , so that we obtain a deterministic query complexity of , where n is the size of G, d is its maximum degree, and g is its genus. We also give a quantum version of our algorithm, whose query complexity is of . Our deterministic and quantum algorithms have query complexities respectively smaller than the algorithm Randomized Steepest Descent of Aldous and Quantum Steepest Descent of Aaronson for large classes of graphs, including graphs of bounded genus and planar graphs.     

Random parallel algorithms for finding exact branchings,perfect matchings,and cycles

In this paper we devise randomized parallel algorithms which given a unary weighted (di)graphG=(V, E)construct in time O(log²¦ V¦) branchings, perfect matchings, and disjoint cycles of weightexactly k belonging toG. These problems have been studied by Papadimitriou and Yannakakis [PY1], by Barahona and Pulleyblank [BP], by Cameriniet al [CGM], and by Mulmuleyet al. [MVV]. Our algorithms improve previous solutions. Moreover, we give an NC² algorithm for computing the absolute value of the pfaffian of a skew-symmetric matrix.Supported in part by MURST 40%.     

Work-efficient BSR-based parallel algorithms for some fundamental problems in graph theory

This paper presents BSR-parallel algorithms for some problems in fundamental graph theory : transitive closure, connected components, spanning tree, bridges and articulation points of a graph and bipartite graph recognition. There already exist constant time algorithms to solve these problems on a mesh with reconfigurable bus system using O(N ⁴) processors. Here we show that these problems can be solved in constant time using only O(N ²) processors on the BSR model (N is the number of vertices of the graph G). Therefore, our algorithms are more work-efficient. These new results suggest that many other problems in graph theory can be solved in constant time using the BSR model.     

Efficient algorithms for the round-trip 1-center and 1-median problems

This paper presents improved algorithms for the round-trip single-facility location problem on a general graph, in which a set A of collection depots is given and the service distance of a customer is defined to be the distance from the server, to the customer, then to a depot, and back to the server. Each customer i is associated with a subset $A^i\subseteq A$ of depots that i can potentially select from and use. When $A^i=A$ for each customer i, the problem is unrestricted; otherwise it is restricted. For the restricted round-trip 1-center problem, we give an $O(mn\lg n)$-time algorithm. For the restricted 1-median problem, we give an $O(mn\lg (|A|/m)+n^2\lg n)$-time algorithm. For the unrestricted 1-median problem, we give an $O(mn+n^2\lg \lg n)$-time algorithm.     

Fast exact string matching algorithms

String matching is the problem of finding all the occurrences of a pattern in a text. We propose a very fast new family of string matching algorithms based on hashing q-grams. The new algorithms are the fastest on many cases, in particular, on small size alphabets.     

Some results on graphs without long induced paths

Many NP-hard graph problems remain difficult on P_k-free graphs for certain values of k. Our goal is to distinguish subclasses of P_k-free graphs where several important graph problems can be solved in polynomial time. In particular, we show that the independent set problem is polynomial-time solvable in the class of (P_k,K_1,n)-free graphs for any positive integers k and n, thereby generalizing several known results.     

Fast adaptive PNN-based thresholding algorithms

Thresholding is a fundamental operation in image processing. Based on the pairwise nearest neighbor technique and the variance criterion, this theme presents two fast adaptive thresholding algorithms. The proposed first algorithm takes O((m−k)mτ) time where k denotes the number of thresholds specified by the user; m denotes the size of the compact image histogram, and the parameter τ has the constraint 1τm. On a set of different real images, experimental results reveal that the proposed first algorithm is faster than the previous three algorithms considerably while having a good feature-preserving capability. The previous three mentioned algorithms need O(m^k) time. Given a specific peak-signal-to-noise ratio (PSNR), we further present the second thresholding algorithm to determine the number of thresholds as few as possible in order to obtain a thresholded image satisfying the given PSNR. The proposed second algorithm takes O((m−k)mτ+γN) time where N and γ denote the image size and the fewest number of thresholds required, respectively. Some experiments are carried out to demonstrate the thresholded images that are encouraging. Since the time complexities required in our proposed two thresholding algorithms are polynomial, they could meet the real-time demand in image preprocessing.     

Fast string matching algorithms for run-length coded strings

Given a run-length coded text of length 2n and a run-length coded pattern of length 2m,m?n commonly, this paper first presents anO(n+m) time sequential algorithm for string matching, then presents anO(1) time parallel algorithm on a two-dimensionalm×n mesh with a reconfigurable bus system.