Memoryless search algorithms in a network with faulty advice

In this paper, we present a randomized algorithm for a mobile agent to search for an item stored at a node t of a network, without prior knowledge of its exact location. Each node of the network has a database that will answer queries of the form “how do I find t?” by responding with the first edge on a shortest path to t. It may happen that some nodes, called liars, give bad advice. We investigate a simple memoryless algorithm which follows the advice with some fixed probability q>1/2 and otherwise chooses a random edge. If the degree of each node and number of liars k are bounded, we show that the expected number of edges traversed by the agent before finding t is bounded from above by O(d+r^k), where d is the distance between the initial and target nodes and . We also show that this expected number of steps can be significantly improved for particular topologies such as the complete graph and the torus.     

Piecemeal Graph Exploration by a Mobile Robot

We study how a mobile robot can learn an unknown environment in a piecemeal manner. The robot's goal is to learn a complete map of its environment, while satisfying the constraint that it must return every so often to its starting position (for refueling, say). The environment is modeled as an arbitrary, undirected graph, which is initially unknown to the robot. We assume that the robot can distinguish vertices and edges that it has already explored. We present a surprisingly efficient algorithm for piecemeal learning an unknown undirected graph G=(V, E) in which the robot explores every vertex and edge in the graph by traversing at most O(E+V^1+o(1)) edges. This nearly linear algorithm improves on the best previous algorithm, in which the robot traverses at most O(E+V²) edges. We also give an application of piecemeal learning to the problem of searching a graph for a “treasure.”     

Distributed edge coloration for bipartite networks

This paper presents a self-stabilizing algorithm to color the edges of a bipartite network such that any two adjacent edges receive distinct colors. The algorithm has the self-stabilizing property; it works without initializing the system. It also works in a de-centralized way without a leader computing a proper coloring for the whole system. Moreover, it finds an optimal edge coloring and its time complexity is O(n ² k + m) moves, where k is the number of edges that are not properly colored in the initial configuration. This is a completely revised and extended version of [15]. This research was supported in part by the National Science Council of the Republic of China under the Contract NSC94-2213-E008-001.     

A Posteriori Error Estimates and an Adaptive Finite Element Method for the Allen–Cahn Equation and the Mean Curvature Flow

This paper develops an a posteriori error estimate of residual type for finite element approximations of the Allen–Cahn equation u_t − Δu+ ε⁻² f(u)=0. It is shown that the error depends on ε⁻¹ only in some low polynomial order, instead of exponential order. Based on the proposed a posteriori error estimator, we construct an adaptive algorithm for computing the Allen–Cahn equation and its sharp interface limit, the mean curvature flow. Numerical experiments are also presented to show the robustness and effectiveness of the proposed error estimator and the adaptive algorithm.     

Superconvergence of Discontinuous Finite Element Solutions for Transient Convection–diffusion Problems

We present a study of the local discontinuous Galerkin method for transient convection–diffusion problems in one dimension. We show that p-degree piecewise polynomial discontinuous finite element solutions of convection-dominated problems are O(Δx ^p+2) superconvergent at Radau points. For diffusion- dominated problems, the solution’s derivative is O(Δx ^p+2) superconvergent at the roots of the derivative of Radau polynomial of degree p+1. Using these results, we construct several asymptotically exact a posteriori finite element error estimates. Computational results reveal that the error estimates are asymptotically exact.This revised version was published online in July 2005 with corrected volume and issue numbers.     

New Fixed-Parameter Algorithms for the Minimum Quartet Inconsistency Problem

Let S be a set of n taxa. Given a parameter k and a set of quartet topologies Q over S such that there is exactly one topology for every subset of four taxa, the parameterized Minimum Quartet Inconsistency (MQI) problem is to decide whether we can find an evolutionary tree inducing a set of quartet topologies that differs from the given set in at most k quartet topologies. The best fixed-parameter algorithm devised so far for the parameterized MQI problem runs in time O(4 ^k n+n ⁴). In this paper, first we present an O(3.0446 ^k n+n ⁴) fixed-parameter algorithm and an O(2.0162 ^k n ³+n ⁵) fixed-parameter algorithm for the parameterized MQI problem. Finally, we give an O ^*((1+ε) ^k ) fixed-parameter algorithm, where ε>0 is an arbitrarily small constant.     

Edge detection and curvature calculation in the visual system

A new model of edge detection in a visual system is presented, where the edge locations are assumed to be at the place of negative peaks of ▿²G * I. Related schemes of detecting edges and calculating the curvature of the edge locus are also presented. It is assumed that the locations of receptive field centers of geniculate cells move with an input image. The arrangement of the receptive fields is given as a solution of an optimization problem. An edge detection algorithm based on the arrangement is presented. The edge detection algorithms are applied to three examples, i.e., Helmholtz irradiation, a face image, and a circular one.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Load Balanced Mapping of Distributed Objects to Minimize Network Communication

This paper introduces a new load balancing and communication minimizing heuristic used in theInverse Remote Procedure Call(IRPC) system. While the paper briefly describes the IRPC system, the focus is on the newIRPCassignment heuristic. The IRPC compiler maps a distributed program to a graph that represents program objects and their dependencies (due to invocations and parameter passing) as nodes and edges, respectively. In the graph, the system preserves conditional and iterative flows, records network transmission and execution costs, and marks nodes that have to reside at specific network sites. The graph is then partitioned by the heuristic to derive a (sub)optimal node assignment to network sites minimizing load balancing and network data transport. The resulting program partition is then reflected in the physical object distribution, and remote and local object communication is transparently implemented. The compiler and run-time system use efficient implementation techniques such as type prediction, inlining, splitting and subprogram passing. The last of these allows remote code to be copied to local data, as an alternative to copying data to the remote site, whenever this will reduce network data transport. The IRPC graph partitioning heuristic operates in timeO(E(logd+l+ logM)), whereMis the number of network sites,Eis the number of communication edges, anddis the maximum degree of a node;lis a parameter of the algorithm, and can vary between 1 andN, whereNis the number of communicating objects. This complexity is more nearly independent ofM, and considerably better in terms ofEandN, than that of previously known related algorithms, such as A*, which employs backtracking and is potentially exponential, or the max-flow/min-cut class of network flow algorithms or heuristics which tend to be at least of Ω(MN²E), and it can be made (by choosinglappropriately) as efficient as even such fast heuristics as heaviest-edge-first, minimal communication, and Kernighan–Lin. In an extensive quantitative evaluation, the heuristic has been demonstrated to perform very well, giving on the average 75% traffic cost reductions for over 95% of the programs when compared to random partitioning, and outperforming in cost reduction and actual execution time the three aforementioned fast heuristics, even with a largel. Thus, to the best of our knowledge, this is the first report of a well-performing assignment heuristic that is bothessentially linearin the number of communication edges, andbetterthan existing, established heuristics of no better complexity.     

An efficient fault-tolerant routing algorithm in bijective connection networks with restricted faulty edges

In this paper, we study fault-tolerant routing in bijective connection networks with restricted faulty edges. First, we prove that the probability that all the incident edges of an arbitrary node become faulty in an n-dimensional bijective connection network, denoted by X_n, is extremely low when n becomes sufficient large. Then, we give an O(n) algorithm to find a fault-free path of length at most n+3⌈log₂∣F∣⌉+1 between any two different nodes in X_n if each node of X_n has at least one fault-free incident edge and the number of faulty edges is not more than 2n−3. In fact, we also for the first time provide an upper bound of the fault diameter of all the bijective connection networks with the restricted faulty edges. Since the family of BC networks contains hypercubes, crossed cubes, Möbius cubes, etc., all the results are appropriate for these cubes.     

Semi-dynamic breadth-first search in digraphs

In this paper we propose dynamic algorithms for maintaining a breadth-first search tree from a given source vertex of a directed graph G in either an incremental or a decremental setting. During a sequence of q edge insertions or a sequence of q edge deletions the total time required is O(m·min{q,n}), where n is the number of vertices of G, and m is the final number of edges of G in the case of insertions or the initial number of edges of G in the case of deletions. This gives O(n) amortized time for each operation if the sequence has length Ω(m). Our algorithms require O(n+m) space. These are the first results in the literature concerning the dynamic maintenance of a breadth-first search tree for directed graphs. As a straightforward application of such algorithms we can maintain a shortest path tree for a directed graph in the case of unit edge weights within the same time bounds. In this case distance queries can be answered in constant time, while shortest path queries can be answered in time linear in the length of the retrieved path.     

Discontinuous Galerkin Method for Linear Free-Surface Gravity Waves

In this paper, we discuss a discontinuous Galerkin finite (DG) element method for linear free surface gravity waves. We prove that the algorithm is unconditionally stable and does not require additional smoothing or artificial viscosity terms in the free surface boundary condition to prevent numerical instabilities on a non-uniform mesh. A detailed error analysis of the full time-dependent algorithm is given, showing that the error in the wave height and velocity potential in the L²-norm is in both cases of optimal order and proportional to O(Δt²+h^p+1), without the need for a separate velocity reconstruction, with p the polynomial order, h the mesh size and Δt the time step. The error analysis is confirmed with numerical simulations. In addition, a Fourier analysis of the fully discrete scheme is conducted which shows the dependence of the frequency error and wave dissipation on the time step and mesh size. The algebraic equations for the DG discretization are derived in a way suitable for an unstructured mesh and result in a symmetric positive definite linear system. The algorithm is demonstrated on a number of model problems, including a wave maker, for discretizations with accuracy ranging from second to fourth order.This revised version was published online in July 2005 with corrected volume and issue numbers.     

Inverse 1-center location problems with edge length augmentation on trees

This paper considers the inverse 1-center location problem with edge length augmentation on a tree network T with n + 1 vertices. The goal is to increase the edge lengths at minimum total cost subject to given modification bounds such that a predetermined vertex s becomes an absolute 1-center under the new edge lengths. Using a set of suitably extended AVL-search trees we develop a combinatorial algorithm which solves the inverse 1-center location problem with edge length augmentation in O(n log n) time. Moreover, it is shown that the problem can be solved in O(n) time if all the cost coefficients are equal.     

Improved edge-coloring with three colors

We show an O(1.344ⁿ)=O(2^0.427n) algorithm for edge-coloring an n-vertex graph using three colors. Our algorithm uses polynomial space. This improves over the previous O(2^n/2) algorithm of Beigel and Eppstein [R. Beigel, D. Eppstein, 3-coloring in time O(1.3289n), J. Algorithms 54 (2) (2005) 168–204.]. We apply a very natural approach of generating inclusion–maximal matchings of the graph. The time complexity of our algorithm is estimated using the “measure and conquer” technique.     

Radiocoloring in planar graphs: Complexity and approximations

The Frequency Assignment Problem (FAP) in radio networks is the problem of assigning frequencies to transmitters, by exploiting frequency reuse while keeping signal interference to acceptable levels. The FAP is usually modelled by variations of the graph coloring problem. A Radiocoloring (RC) of a graph G(V,E) is an assignment function such that |Φ(u)-Φ(v)|2, when u,v are neighbors in G, and |Φ(u)-Φ(v)|1 when the distance of u,v in G is two. The number of discrete frequencies and the range of frequencies used are called order and span, respectively. The optimization versions of the Radiocoloring Problem (RCP) are to minimize the span or the order. In this paper we prove that the radiocoloring problem for general graphs is hard to approximate (unless NP=ZPP) within a factor of n^1/2-ε (for any ), where n is the number of vertices of the graph. However, when restricted to some special cases of graphs, the problem becomes easier. We prove that the min span RCP is NP-complete for planar graphs. Next, we provide an O(nΔ) time algorithm (|V|=n) which obtains a radiocoloring of a planar graph G that approximates the minimum order within a ratio which tends to 2 (where Δ the maximum degree of G). Finally, we provide a fully polynomial randomized approximation scheme (fpras) for the number of valid radiocolorings of a planar graph G with λ colors, in the case where λ4Δ+50.     

Restoration by path concatenation: fast recovery of MPLS paths

A new general theory about restoration of network paths is first introduced. The theory pertains to restoration of shortest paths in a network following failure, e.g., we prove that a shortest path in a network after removing k edges is the concatenation of at most k+1 shortest paths in the original network. The theory is then combined with efficient path concatenation techniques in MPLS (multi-protocol label switching), to achieve powerful schemes for restoration in MPLS based networks. We thus transform MPLS into a flexible and robust method for forwarding packets in a network. Finally, the different schemes suggested are evaluated experimentally on three large networks (a large ISP, the AS graph of the Internet, and the full Internet topology). These experiments demonstrate that the restoration schemes perform well in actual topologies. Received: December 2001 / Accepted: July 2002 RID="*" ID="*" This research was supported by a grant from the Ministry of Science, Israel     

Parallel Algorithms for the Edge-Coloring and Edge-Coloring Update Problems

LetG(V,E) be a simple undirected graph with a maximum vertex degree Δ(G) (or Δ for short), |V| =nand |E| =m. An edge-coloring ofGis an assignment to each edge inGa color such that all edges sharing a common vertex have different colors. The minimum number of colors needed is denoted by χ′(G) (called thechromatic index). For a simple graphG, it is known that Δ ≤ χ′(G) ≤ Δ + 1. This paper studies two edge-coloring problems. The first problem is to perform edge-coloring for an existing edge-colored graphGwith Δ + 1 colors stemming from the addition of a new vertex intoG. The proposed parallel algorithm for this problem runs inO(Δ^3/2log³Δ + Δ logn) time usingO(max{nΔ, Δ³}) processors. The second problem is to color the edges of a given uncolored graphGwith Δ + 1 colors. For this problem, our first parallel algorithm requiresO(Δ^5.5log³Δ logn+ Δ⁵log⁴n) time andO(max{n²Δ,nΔ³}) processors, which is a slight improvement on the algorithm by H. J. Karloff and D. B. Shmoys [J. Algorithms8 (1987), 39–52]. Their algorithm costsO(Δ⁶log⁴n) time andO(n²Δ) processors if we use the fastest known algorithm for finding maximal independent sets by M. Goldberg and T. Spencer [SIAM J. Discrete Math.2 (1989), 322–328]. Our second algorithm requiresO(Δ^4.5log³Δ logn+ Δ⁴log⁴n) time andO(max{n²,nΔ³}) processors. Finally, we present our third algorithm by incorporating the second algorithm as a subroutine. This algorithm requiresO(Δ^3.5log³Δ logn+ Δ³log⁴n) time andO(max{n²log Δ,nΔ³}) processors, which improves, by anO(Δ^2.5) factor in time, on Karloff and Shmoys' algorithm. All of these algorithms run in the COMMON CRCW PRAM model.     

Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink

Uneven energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Such unbalanced energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of prolonging network lifetime in large-scale wireless sensor networks where a mobile sink gathers data periodically along the predefined path and each sensor node uploads its data to the mobile sink over a multi-hop communication path. By using greedy policy and dynamic programming, we propose a heuristic topology control algorithm with time complexity O(n(m + n log n)), where n and m are the number of nodes and edges in the network, respectively, and further discuss how to refine our algorithm to satisfy practical requirements such as distributed computing and transmission timeliness. Theoretical analysis and experimental results show that our algorithm is superior to several earlier algorithms for extending network lifetime.     

A practical algorithm for making filled graphs minimal

For an arbitrary filled graph G⁺ of a given original graph G, we consider the problem of removing fill edges from G⁺ in order to obtain a graph M that is both a minimal filled graph of G and a subgraph of G⁺. For G⁺ with f fill edges and e original edges, we give a simple O(f(e+f)) algorithm which solves the problem and computes a corresponding minimal elimination ordering of G. We report on experiments with an implementation of our algorithm, where we test graphs G corresponding to some real sparse matrix applications and apply well-known and widely used ordering heuristics to find G⁺. Our findings show the amount of fill that is commonly removed by a minimalization for each of these heuristics, and also indicate that the runtime of our algorithm on these practical graphs is better than the presented worst-case bound.     

Some counterexamples in robust stability theory

This note deals with the problem of determining if a linear system whose characteristics polynomial depends multilinearly on n independent uncertain real parameters Δ_i, I = 1,…,n, is robustly stable. It is shown by example that a polynomial in n variables may have a unique real root, and that this observation disposes of several natural conjectures in robust stability theory. In particular, we show that, in a certain sense, there are no ‘edge’ or ‘m-dimensional face’ Kharitonov-like theorems for the general multilinear case. The result holds even when restricted to that subset of multilinear functions which can be written in the form f(Δ₁,…, Δ_n) = det(I + diag(Δ₁,…,Δ_n)M) for some complex matrix M.     

The Swap Edges of a Multiple-Sources Routing Tree

Let T be a spanning tree of a graph G and S⊂V(G) be a set of sources. The routing cost of T is the total distance from all sources to all vertices. For an edge e of T, the swap edge of e is the edge f minimizing the routing cost of the tree formed by replacing e with f. Given an undirected graph G and a spanning tree T of G, we investigate the problem of finding the swap edge for every tree edge. In this paper, we propose an O(mlog n+n ²)-time algorithm for the case of two sources and an O(mn)-time algorithm for the case of more than two sources, where m and n are the numbers of edges and vertices of G, respectively.