Various problems of combinatorial optimization and permutation can be solved with neural network optimization. The problem of estimating the software reliability can be solved with the optimization of failed components to its minimum value. Various solutions of the problem of estimating the software reliability have been given. These solutions are exact and heuristic, but all the exact approach...

In rare situations, stochastic programs can be solved analytically. Otherwise, approximation is necessary to solve stochastic programs with a large or infinite number of scenarios to a desired level of accuracy. This involves statistical sampling or deterministic selection of a finite set of scenarios to obtain a tractable deterministic equivalent problem. Some of these approaches rely on bound...

This paper presents an active-set algorithm for large-scale optimization that occupies the middle ground between sequential quadratic programming (SQP) and sequential linear-quadratic programming (SL-QP) methods. It consists of two phases. The algorithm first minimizes a piecewise linear approximation of the Lagrangian, subject to a linearization of the constraints, to determine a working set. ...

In this paper we consider the capacitated vertex cover problem which is the variant of vertex cover where each node is allowed to cover a limited number of edges. We present an efficient, deterministic, distributed approximation algorithm for the problem. Our algorithm computes a (2 + ǫ)-approximate solution which violates the capacity constraints by a factor of (4 + ǫ) in a polylogarithmic num...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

By definition, web-services composition works on developing merely optimum coordination among a number of available web-services to provide a new composed web-service intended to satisfy some users requirements for which a single web service is not (good) enough. In this article, the formulation of the automatic web-services composition is proposed as several set-cover problems and an approxima...

We investigate server scheduling policies to minimize average user perceived latency in pull-based client-server systems (systems where multiple clients request data from a server) where the server answers requests on a multicast/broadcast channel. We rst show that there is no O(1)-competitive algorithm for this problem. We then give a method to convert any nonclairvoyant unicast scheduling alg...

Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the sequential Monte-Carlo (SMC) algorithm, also known as the particle filter. Nevertheless, this method tends to be inefficient when applied to high-dimensional problems. In this chapter, we present, an...

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by approximating their behavior when applied for k steps. Specifically, our algorithm uses MonteCarlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over k steps. The algorithm then selects k examples tha...