Minimizing the size of an identifying or locating-dominating code in a graph is NP-hard

Let G=(V,E) be an undirected graph and C a subset of vertices. If the sets B_r(v)∩C, vV (respectively, vVC), are all nonempty and different, where B_r(v) denotes the set of all points within distance r from v, we call C an r-identifying code (respectively, an r-locating-dominating code). We prove that, given a graph G and an integer k, the decision problem of the existence of an r-identifying code, or of an r-locating-dominating code, of size at most k in G, is NP-complete for any r.     

A multi-tree routing scheme using acyclic orientations

We propose a mathematical model for fault-tolerant routing based on acyclic orientations, or acorns, of the underlying network G=(V,E). The acorn routing model applies routing tables that store the set of parent pointers associated with each out-neighborhood defined by the acorn. Unlike the standard single-parent sink-tree model, which is vulnerable to faults, the acorn model affords a full representation of the entire network and is able to dynamically route around faults. This fault tolerance is achieved when using the acorn model as a multi-tree generator for gathering data at a destination node, as well as an independent tree generator for global point-to-point communication. A fundamental fault-tolerant measure of the model is the capacity of an acorn, i.e., the largest integer k such that each vertex outside the neighborhood N(v) of the destination v has at least k parent pointers. A capacity-k acorn A to destination v is k-vertex fault-tolerant to v. More strongly, we show A supports a k independent sink-tree generator, i.e., the parent pointers of each vertex w V−N(v) can be partitioned into k nonempty classes labeled 1,2,…,k such that any set of sink trees T₁,T₂,…,T_k are pairwise independent, where tree T_i is a sink tree generated by parent pointers labeled i together with the parent pointers into v. We present an linear time optimization algorithm for finding an acorn A of maximum capacity in graphs, based upon a minimax theorem. We also present efficient algorithms that label the parent pointers of capacity-k acorn A, yielding a k-independent sink tree generating scheme.     

Resolvability and the upper dimension of graphs

For an ordered set W = {w₁, w₂,…, w_k} of vertices and a vertex v in a connected graph G, the (metric) representation of v with respect to W is the k-vector r(v | W) = (d(v, w₁), d(v, w₂),…, d(v, w_k)), where d(x, y) represents the distance between the vertices x and y. The set W is a resolving set for G if distinct vertices of G have distinct representations. A new sharp lower bound for the dimension of a graph G in terms of its maximum degree is presented.

A resolving set of minimum cardinality is a basis for G and the number of vertices in a basis is its (metric) dimension dim(G). A resolving set S of G is a minimal resolving set if no proper subset of S is a resolving set. The maximum cardinality of a minimal resolving set is the upper dimension dim⁺(G). The resolving number res(G) of a connected graph G is the minimum k such that every k-set W of vertices of G is also a resolving set of G. Then 1 ≤ dim(G) ≤ dim⁺(G) ≤ res(G) ≤ n − 1 for every nontrivial connected graph G of order n. It is shown that dim⁺(G) = res(G) = n − 1 if and only if G = K_n, while dim⁺(G) = res(G) = 2 if and only if G is a path of order at least 4 or an odd cycle.

The resolving numbers and upper dimensions of some well-known graphs are determined. It is shown that for every pair a, b of integers with 2 ≤ a ≤ b, there exists a connected graph G with dim(G) = dim⁺(G) = a and res(G) = b. Also, for every positive integer N, there exists a connected graph G with res(G) − dim⁺(G) ≥ N and dim⁺(G) − dim(G) ≥ N.     

t-Spanners for metric space searching

The problem of Proximity Searching in Metric Spaces consists in finding the elements of a set which are close to a given query under some similarity criterion. In this paper we present a new methodology to solve this problem, which uses a t-spanner G′(V, E) as the representation of the metric database. A t-spanner is a subgraph G′(V, E) of a graph G(V, A), such that E  A and G′ approximates the shortest path costs over G within a precision factor t.

Our key idea is to regard the t-spanner as an approximation to the complete graph of distances among the objects, and to use it as a compact device to simulate the large matrix of distances required by successful search algorithms such as AESA. The t-spanner properties imply that we can use shortest paths over G′ to estimate any distance with bounded-error factor t.

For this sake, several t-spanner construction, updating, and search algorithms are proposed and experimentally evaluated. We show that our technique is competitive against current approaches. For example, in a metric space of documents our search time is only 9% over AESA, yet we need just 4% of its space requirement. Similar results are obtained in other metric spaces.

Finally, we conjecture that the essential metric space property to obtain good t-spanner performance is the existence of clusters of elements, and enough empirical evidence is given to support this claim. This property holds in most real-world metric spaces, so we expect that t-spanners will display good behavior in most practical applications. Furthermore, we show that t-spanners have a great potential for improvements.     

Efficient enumeration of all minimal separators in a graph

This paper presents an efficient algorithm for enumerating all minimal a-b separators separating given non-adjacent vertices a and b in an undirected connected simple graph G = (V, E), Our algorithm requires O(n³R_ab) time, which improves the known result of O(n⁴R_ab) time for solving this problem, where ¦V¦= n and R_ab is the number of minimal a-b separators. The algorithm can be generalized for enumerating all minimal A-B separators that separate non-adjacent vertex sets A, B < V, and it requires O(n²(n − n_A − n_b)R_AB) time in this case, where n_a = ¦A¦, n_B = ¦B¦ and r_AB is the number of all minimal A−B separators. Using the algorithm above as a routine, an efficient algorithm for enumerating all minimal separators of G separating G into at least two connected components is constructed. The algorithm runs in time O(n³R⁺_Σ + n⁴R_Σ), which improves the known result of O(n⁶R_Σ) time, where R_σ is the number of all minimal separators of G and R_ΣR⁺_Σ = ∑_1i, v_j) ER_vivj n − 1)/2 − m)R_Σ. Efficient parallelization of these algorithms is also discussed. It is shown that the first algorithm requires at most O((n/log n)R_ab) time and the second one runs in time O((n/log n)R⁺_Σ+n log nR_Σ) on a CREW PRAM with O(n³) processors.     

Efficient algorithms for shortest distance queries on special classes of polygons

The problem of finding a rectilinear minimum bend path (RMBP) between two designated points inside a rectilinear polygon has applications in robotics and motion planning. In this paper, we present efficient algorithms to solve the query version of the RMBP problem for special classes of rectilinear polygons given their visibility graphs. Specifically, we show that given an unweighted graph G = (V, E), with ¦V¦ = N and ¦E¦ = M, algorithms to preprocess G in linear space and time such that the shortest distance queries — queries asking for the distance between any pair of nodes in the graph — can be answered in constant time and space are presented in this paper. For the case of a chordal graph G, our algorithms give a distance which is at most one away from the actual shortest distance. When G is a K-chordal graph, our algorithm produces an exact shortest distance in O(K) time. We also present a non-trivial parallel implementation of the sequential preprocessing algorithm for the CREW-PRAM model which runs in O(log² N) time using O(N + M) processors. After the preprocessing, we can answer the queries in constant time using a single processor.     

Paired-domination in inflated graphs

The inflation G_I of a graph G with n(G) vertices and m(G) edges is obtained from G by replacing every vertex of degree d of G by a clique K_d. A set S of vertices in a graph G is a paired dominating set of G if every vertex of G is adjacent to some vertex in S and if the subgraph induced by S contains a perfect matching. The paired domination number γ_p(G) is the minimum cardinality of a paired dominating set of G. In this paper, we show that if a graph G has a minimum degree δ(G)2, then n(G)γ_p(G_I)4m(G)/[δ(G)+1], and the equality γ_p(G_I) = n(G) holds if and only if G has a perfect matching. In addition, we present a linear time algorithm to compute a minimum paired-dominating set for an inflation tree.     

Finding the minimum vertex distance between two disjoint convex polygons in linear time

Let V = v₁, v₂, …, v_m and W = w₁, w₂, …, w_n be two linearly separable convex polygons whose vertices are specified by their cartesian coordinates in order. An algorithm with O(m + n) worst-case time complexity is described for finding the minimum euclidean distance between a vertex v₁ in V and a vertex w_j in W. It is also shown that the algorithm is optimal.     

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.     

A linear time algorithm for maximum matchings in convex, bipartite graphs

The problem of determining the maximum matching in a convex bipartite graph, G = (V₁, V₂, E), is considered. It is shown that by using the appropriate data structures, the maximum matching problem can be efficiently transformed into an off-line minimum problem. Since the off-line minimum problem has been shown to be linear, the maximum matching in a convex bipartite graph can be determined in O(|V₁|) time.     

ANTS: Agents on Networks, Trees, and Subgraphs

Efficient exploration of large networks is a central issue in data mining and network maintenance applications. In most existing work there is a distinction between the active ‘searcher’ which both executes the algorithm and holds the memory and the passive ‘searched graph’ over which the searcher has no control at all. Large dynamic networks like the Internet, where the nodes are powerful computers and the links have narrow bandwidth and are heavily-loaded, call for a different paradigm, in which a noncentralized group of one or more lightweight autonomous agents traverse the network in a completely distributed and parallelizable way. Potential advantages of such a paradigm would be fault tolerance against network and agent failures, and reduced load on the busy nodes due to the small amount of memory and computing resources required by the agent in each node. Algorithms for network covering based on this paradigm could be used in today’s Internet as a support for data mining and network control algorithms. Recently, a vertex ant walk ( ) method has been suggested [I.A. Wagner, M. Lindenbaum, A.M. Bruckstein, Ann. Math. Artificial Intelligence 24 (1998) 211–223] for searching an undirected, connected graph by an a(ge)nt that walks along the edges of the graph, occasionally leaving ‘pheromone’ traces at nodes, and using those traces to guide its exploration. It was shown there that the ant can cover a static graph within time nd, where n is the number of vertices and d the diameter of the graph. In this work we further investigate the performance of the method on dynamic graphs, where edges may appear or disappear during the search process. In particular we prove that (a) if a certain spanning subgraph S is stable during the period of covering, then the method is guaranteed to cover the graph within time nd_s, where d_s is the diameter of S, and (b) if a failure occurs on each edge with probability p, then the expected cover time is bounded from above by nd((logΔ/log(1/p))+((1+p)/(1−p))), where Δ is the maximum vertex degree in the graph. We also show that (c) if G is a static tree then it is covered within time 2n.     

Lax-stability of fully discrete spectral methods via stability regions and pseudo-eigenvalues

In many calculations, spectral discretization in space is coupled with a standard ordinary differential equation formula in time. To analyze the stability of such a combination, one would like simply to test whether the eigenvalues of the spatial discretization operator (appropriately scaled by the time step k) lie in the stability region for the o.d.e. formula, but it is well known that this kind of analysis is in general invalid. In the present paper we rehabilitate the use of stability regions by proving that a discrete linear multistep ‘method of lines’ approximation to a partial differential equation is Lax-stable, within a small algebraic factor, if and only if all of the -pseudo-eigenvalues of the spatial discretization operator lie within O() of the stability region as → 0. An -pseudo-eigenvalue of a matrix A is any number that is an eigenvalue of some matrix A + E with E ; our arguments make use of resolvents and are closely related to the Kreiss matrix theorem. As an application of our general result, we show that an explicit N-point Chebyshev collocation approximation of u_t = −xu_x on [−1, 1] is Lax-stable if and only if the time step satisfies k = O(N⁻²), although eigenvalue analysis would suggest a much weaker restriction of the form k CN⁻¹.     

Distribution of black nodes at various levels in a linear quadtree

The distribution of black leaf nodes at each level of a linear quadtree is of significant interest in the context of estimation of time and space complexities of linear quadtree based algorithms. The maximum number of black nodes of a given level that can be fitted in a square grid of size 2ⁿ × 2ⁿ can readily be estimated from the ratio of areas. We show that the actual value of the maximum number of nodes of a level is much less than the maximum obtained from the ratio of the areas. This is due to the fact that the number of nodes possible at a level k, 0≤k≤n − 1, should consider the sum of areas occupied by the actual number of nodes present at levels k + 1, k + 2, …, n − 1.     

New results on oscillation for delay differential equations with piecewise constant argument

We introduce a new technique to obtain some new oscillation criteria for the oscillating coefficients delay differential equation with piecewise constant argument of the form x′(t) + a(t)x(t) + b(t)x({t − k}) = 0, where a(t) and b(t) are right continuous functions on [−k, ∞), k is a positive integer, and [·] denotes the greatest integer function. Our results improve and generalize the known results in the literature. Some examples are also given to demonstrate the advantage of our results.     

A time-optimal solution for the path cover problem on cographs

We show that the notoriously difficult problem of finding and reporting the smallest number of vertex-disjoint paths that cover the vertices of a graph can be solved time- and work-optimally for cographs. Our result implies that for this class of graphs the task of finding a Hamiltonian path can be solved time- and work-optimally in parallel.

It was open for more than 10 years to find a time- and work-optimal parallel solution for this important problem. Our contribution is to offer an optimal solution to this important problem. We begin by showing that any algorithm that solves an instance of size n of the problem must take Ω(log n) time on the CREW, even if an infinite number of processors are available. We then go on to show that this time lower bound is tight by devising an EREW algorithm that, given an n-vertex cograph G represented by its cotree, finds and reports all the paths in a minimum path cover in O(log n) time using n/log n processors.     

Blended Hermite interpolants

In (Röschel, l997) B-spline technique was used for blending of Lagrange interpolants. In this paper we generalize this idea replacing Lagrange by Hermite interpolants. The generated subspline b(t) interpolates the Hermite input data consisting of parameter values t_i and corresponding derivatives a_i,j, j=0,…,_i−1, and is called blended Hermite interpolant (BHI). It has local control, is connected in affinely invariant way with the input and consists of integral (polynomial) segments of degree 2·k−1, where k−1max{_i}−1 denotes the degree of the B-spline basis functions used for the blending. This method automatically generates one of the possible interpolating subsplines of class C^k−1 with the advantage that no additional input data is necessary.     

On the k-path partition of graphs

The k-path partition problem is to partition a graph into the minimum number of paths, so that none of them has length more than k, for a given positive integer k. The problem is a generalization of the Hamiltonian path problem and the problem of partitioning a graph into the minimum number of paths. The k-path partition problem remains NP-complete on the class of chordal bipartite graphs if k is part of the input, and we show that it is NP-complete on the class of comparability graphs even for k=3. On the positive side, we present a polynomial-time solution for the problem, with any k, on bipartite permutation graphs, which form a subclass of chordal bipartite graphs.     

Contrast-optimal k out of n secret sharing schemes in visual cryptography

Visual cryptography and (k,n)-visual secret sharing schemes were introduced by Naor and Shamir (Advances in Cryptology — Eurocrypt 94, Springer, Berlin, 1995, pp. 1–12). A sender wishing to transmit a secret message distributes n transparencies amongst n recipients, where the transparencies contain seemingly random pictures. A (k,n)-scheme achieves the following situation: If any k recipients stack their transparencies together, then a secret message is revealed visually. On the other hand, if only k−1 recipients stack their transparencies, or analyze them by any other means, they are not able to obtain any information about the secret message. The important parameters of a scheme are its contrast, i.e., the clarity with which the message becomes visible, and the number of subpixels needed to encode one pixel of the original picture. Naor and Shamir constructed (k,k)-schemes with contrast 2^−(k−1). By an intricate result from Linial (Combinatorica 10 (1990) 349–365), they were also able to prove the optimality of these schemes. They also proved that for all fixed kn, there are (k,n)-schemes with contrast . For k=2,3,4 the contrast is approximately and . In this paper, we show that by solving a simple linear program, one is able to compute exactly the best contrast achievable in any (k,n)-scheme. The solution of the linear program also provides a representation of a corresponding scheme. For small k as well as for k=n, we are able to analytically solve the linear program. For k=2,3,4, we obtain that the optimal contrast is at least and . For k=n, we obtain a very simple proof of the optimality of Naor's and Shamir's (k,k)-schemes. In the case k=2, we are able to use a different approach via coding theory which allows us to prove an optimal tradeoff between the contrast and the number of subpixels.     

Threshold counters with increments and decrements

A threshold counter is a shared data structure that assumes integer values. It provides two operations: changes the current counter value from v to v+1, while returns the value v/w, where v is the current counter value and w is a fixed constant. Thus, the operation returns the “approximate” value of the counter to within the constant w. Threshold counters have many potential uses, including software barrier synchronization. Threshold networks are a class of distributed data structures that can be used to construct highly-concurrent, low-contention implementations of shared threshold counters. In this paper, we give the first proof that any threshold network construction of a threshold counter can be extended to support a operation that changes the counter value from v to v−1.