A note on exact distance labeling

We show that the vertices of an edge-weighted undirected graph can be labeled with labels of size O(n) such that the exact distance between any two vertices can be inferred from their labels alone in time. This improves the previous best exact distance labeling scheme that also requires O(n)-sized labels but time to compute the distance. Our scheme is almost optimal as exact distance labeling is known to require labels of length Ω(n).     

Probabilistic integer sorting

We introduce a probabilistic sequential algorithm for stable sorting n uniformly distributed keys in an arbitrary range. The algorithm runs in linear time and sorts all but a very small fraction of the input sequences; the best previously known bound was . An EREW PRAM extension of this sequential algorithm sorts in O((n/p+lgp)lgn/lg(n/p+lgn)) time using p?n processors under the same probabilistic conditions. For a CRCW PRAM we improve upon the probabilistic bound of obtained by Rajasekaran and Sen to derive a bound. Additionally, we present experimental results for the sequential algorithm that establish the practicality of our method.     

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.     

Optimal per-edge processing times in the semi-streaming model

We present semi-streaming algorithms for basic graph problems that have optimal per-edge processing times and therefore surpass all previous semi-streaming algorithms for these tasks. The semi-streaming model, which is appropriate when dealing with massive graphs, forbids random access to the input and restricts the memory to bits.Particularly, the formerly best per-edge processing times for finding the connected components and a bipartition are O(α(n)), for determining k-vertex and k-edge connectivity O(k²n) and O(n⋅logn) respectively for any constant k and for computing a minimum spanning forest O(logn). All these time bounds we reduce to O(1).Every presented algorithm determines a solution asymptotically as fast as the best corresponding algorithm up to date in the classical RAM model, which therefore cannot convert the advantage of unlimited memory and random access into superior computing times for these problems.     

Alphabetic coding with exponential costs

This note considers an alphabetic binary tree formulation in a family of nonlinear problems. An application of this family occurs when a random outcome needs to be determined via alphabetically ordered search within a stochastic time window. Rather than finding a decision tree minimizing , this variant involves minimizing for a given a∈(0,1). Herein a dynamic programming algorithm finds the optimal solution in O(n³) time and O(n²) space; methods traditionally used to improve the speed of optimizations in related problems, such as the Hu-Tucker procedure, fail for this problem. This note thus also introduces two algorithms which can find a suboptimal solution in linear time (for one) or O(nlogn) time (for the other), with associated redundancy bounds guaranteeing their coding efficiency.     

Spectral radius minimization for optimal average consensus and output feedback stabilization

In this paper, we consider two problems which can be posed as spectral radius minimization problems. Firstly, we consider the fastest average agreement problem on multi-agent networks adopting a linear information exchange protocol. Mathematically, this problem can be cast as finding an optimal such that x(k+1)=Wx(k), , and W∈S(E). Here, is the value possessed by the agents at the kth time step, is an all-one vector and S(E) is the set of real matrices in with zeros at the same positions specified by a network graph G(V,E), where V is the set of agents and E is the set of communication links between agents. The optimal W is such that the spectral radius is minimized. To this end, we consider two numerical solution schemes: one using the qth-order spectral norm (2-norm) minimization (q-SNM) and the other gradient sampling (GS), inspired by the methods proposed in [Burke, J., Lewis, A., & Overton, M. (2002). Two numerical methods for optimizing matrix stability. Linear Algebra and its Applications, 351-352, 117-145; Xiao, L., & Boyd, S. (2004). Fast linear iterations for distributed averaging. Systems & Control Letters, 53(1), 65-78]. In this context, we theoretically show that when E is symmetric, i.e. no information flow from the ith to the jth agent implies no information flow from the jth to the ith agent, the solution from the 1-SNM method can be chosen to be symmetric and is a local minimum of the function . Numerically, we show that the q-SNM method performs much better than the GS method when E is not symmetric. Secondly, we consider the famous static output feedback stabilization problem, which is considered to be a hard problem (some think NP-hard): for a given linear system (A,B,C), find a stabilizing control gain K such that all the real parts of the eigenvalues of A+BKC are strictly negative. In spite of its computational complexity, we show numerically that q-SNM successfully yields stabilizing controllers for several benchmark problems with little effort.     

Finding frequent items over sliding windows with constant update time

In this paper, we consider the problem of finding ?-approximate frequent items over a sliding window of size N. A recent work by Lee and Ting (2006) [7] solves the problem by giving an algorithm that supports query and update time, and uses space. Their query time and memory usage are essentially optimal, but the update time is not. We give a new algorithm that supports O(1) update time with high probability while maintaining the query time and memory usage as .     

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.     

Improved approximation bounds for the minimum rainbow subgraph problem

In this paper we consider the Minimum Rainbow Subgraph problem (MRS): Given a graph G with n vertices whose edges are coloured with p colours, find a subgraph F⊆G of minimum order and with p edges such that F contains each colour exactly once.We present a polynomial time -approximation algorithm for the MRS problem for an arbitrary small positive ?. This improves the previously best known approximation ratio of . We also prove the MRS problem to be NP-hard and APX-hard for graphs with maximum degree 2. Finally we present an algorithm to find an optimal solution in running time O(²(p+2plog₂Δ)nO(1)).     

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.     

Eigen-distribution on random assignments for game trees

In this Letter, we investigate a special distribution, called eigen-distribution, on random assignments for a class of game trees . There are two cases, where the assignments to leaves are independently distributed (ID) and correlated distributed (CD). In the ID case, we prove that the distributional probability ? belongs to , and ? is a strictly increasing function on rounds k∈[1,∞). In the CD case, we propose a reverse assigning technique (RAT) to form two particular sets of assignments, 1-set and 0-set, then show that the E¹-distribution (namely, a particular distribution on the assignments of 1-set such that all the deterministic algorithms have the same complexity) is the unique eigen-distribution for in the global distribution.