Superstrings have many applications in data compression and genetics. However the decision version of the shortest superstring problem is NP-complete. In this paper we examine the complexity of approximating a shortest superstring. There are two basic measures of the approximations: the compression ratio and the approximation ratio. The well known and practical approximation algorithm is the se...

We present a randomized distributed approximation algorithm for the metric uncapacitated facility location problem. The algorithm is executed on a bipartite graph in the CONGEST model yielding a (1.861+ ε) approximation factor, where ε is an arbitrary small positive constant. It needs O(n3/4 log1+ε(n)) communication rounds with high probability (n denoting the number of facilities and clients)....

Thinning algnrithms can be classified into two general types: sequential and parallel alprithms. Most of them peel off the object boundaries until the objects have been reduced to thin lines. The process is performed iteratively and needs a number of iterations (approximately equal to half of the maximum line width of the object). Several boundary based algorithms which belong to the sequential...

In this paper we present two parallel versions of bisection method to compute the spectrum of symmetric Toeplitz matrices. Both parallel algorithms have been implemented and analysed on a virtual shared memory multiprocessor using a portable message-passing environment. The algorithms very efficiently parallelize the sequential method, and the application of a dynamic strategy to distribute the...

This paper addresses the optimisation of particle filtering methods aka Sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using Simultaneous Perturbation Stochastic Approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.

This paper addresses the optimisation of particle filtering methods aka Sequential Monte Carlo (SMC) methods using stochastic approximation. First, the SMC algorithm is parameterised smoothly by a parameter. Second, optimisation of an average cost function is performed using Simultaneous Perturbation Stochastic Approximation (SPSA). Simulations demonstrate the efficiency of our algorithm.

An iterative algorithm for computing the principal nth root of a positive deenite matrix is presented. The algorithm is based on the Gauss-Legendre approximation of a deenite integral. We p resent a parallelization in which we use as many processors as the order of the approximation. An analysis of the error introduced at each step of the iteration indicates that the algorithm converges more ra...

Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself. This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. We propose the Softstar algorithm, a softened heuristic-guided search technique for the maximum en...

A fast algorithm for the approximation of a low rank LU decomposition is presented. In order to achieve a low complexity, the algorithm uses sparse random projections combined with FFTbased random projections. The asymptotic approximation error of the algorithm is analyzed and a theoretical error bound is presented. Finally, numerical examples illustrate that for a similar approximation error, ...

‎In this paper‎, ‎we propose an algorithm to obtain an approximation set of the (weakly) nondominated points of nonsmooth multiobjective optimization problems with equality and inequality constraints‎. ‎We use an extension of the Wolfe duality to construct the separating hyperplane in Benson's outer algorithm for multiobjective programming problems with subdifferentiable functions‎. ‎We also fo...