The purpose of this paper is to establish bounds on the rate of convergence of the conjugate gradient algorithm when the underlying matrix is a random positive definite perturbation of a deterministic positive definite matrix. We estimate all finite moments of a natural halting time when the random perturbation is drawn from the Laguerre unitary ensemble in a critical scaling regime explored in...

in this paper, we implemented the modified iteration perturbation method (mipm) for approximating the periodic behavior of a tapered beam. this problem is formulated as a nonlinear ordinary differential equation with linear and nonlinear terms. the solution is quickly convergent and does not need to complicated calculations. comparing the results of the mipm with the exact solution shows that t...

Here, an algorithm is presented for solving the minimum sum-of-squares clustering problems using their difference of convex representations. The proposed algorithm is based on an incremental approach and applies the well known DC algorithm at each iteration. The proposed algorithm is tested and compared with other clustering algorithms using large real world data sets.

in this paper, we will compare a homotopy perturbation algorithm and taylor series expansin method for a system of second kind fredholm integral equations. an application of he’s homotopy perturbation method is applied to solve the system of fredholm integral equations. taylor series expansin method reduce the system of integral equations to a linear system of ordinary differential equation.

We present the first adaptive random directions Newton algorithm under i.i.d., symmetric, uniformly distributed perturbations for a general problem of optimization under noisy observations. We also present a simple gradient search scheme under the aforementioned perturbation random variates. Our Newton algorithm requires generating N perturbation variates and three simulations at each iteration...

As a general framework to determine a collision-free feedback motion strategies, the Random Neighborhood Graph (RNG) approach [19] defines a global navigation function over an approximate representation of the free configuration. In this paper, we improve the RNG approach in several aspects. We present an ANN-accelerated RNG construction algorithm to achieve near logarithmic running time in eac...

The current paper focuses on some analytical techniques to solve the non-linear Duffing oscillator with large nonlinearity. Four different methods have been applied for solution of the equation of motion; the variational iteration method, He’s parameter expanding method, parameterized perturbation method, and the homotopy perturbation method. The results reveal that approxim...

This paper presents a variant of the AdaBoost algorithm for boosting Näıve Bayes text classifier, called AdaBUS, which combines active learning with boosting algorithm. Boosting has been evaluated to effectively improve the accuracy of machine-learning based classifiers. However, Näıve Bayes classifier, which is remarkably successful in practice for text classification problems, is known not to...

In this paper, we propose an arc-search corrector-predictor interior-point method for solving $P_*(kappa)$-linear complementarity problems. The proposed algorithm searches the optimizers along an ellipse that is an approximation of the central path. The algorithm generates a sequence of iterates in the wide neighborhood of central path introduced by Ai and Zhang. The algorithm does not de...