In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation, referred to as two-step knowledge-aided iterative estimation of signal parameters via rotational invariance techniques (ESPRIT) method (Two-Step KAIESPRIT), which achieves more accurate estimates than those of prior art. The proposed Two-Step KAI-ESPRIT improves the estimation of the covariance matrix...

GCR (Generalized Conjugate Residual) and Omin (Orthomin) are iterative methods for approximating the solution of unsymmetric linear systems. The S-step generalization of these methods has been derived and studied in past work. The S-step methods exhibit improved convergence properties. Also, their data locality and parallel properties are enhanced by forming blocks of s search direction vectors...

The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden la...

This article demonstrates a new approach for nonlinear finite element analysis. The methodology is very suitable and gives very accurate results in linear as well as in nonlinear range of the material behavior. Proposed methodology can be regarded as stress based finite element analysis as it is required to define the stress distribution within the structural body with structural idealization a...

‎Newton method is one of the most famous numerical methods among the line search‎ ‎methods to minimize functions. ‎It is well known that the search direction and step length play important roles ‎in this class of methods to solve optimization problems. ‎In this investigation‎, ‎a new modification of the Newton method to solve ‎unconstrained optimization problems is presented‎. ‎The significant ...

First, you have to add the gmmtbx folder into Matlab’s path. Enter the command: >>addpath(’k:\gmmtbx’); Now, we need to construct the variables of interest, namely the dataset and the instruments. Both have already been saved in the gmmtbx folder and can be loaded into Matlab using the commands: >>x = load(’cbapmvwrdata.dat’); >>z = load(’cbapmvwrinstr.dat’); where x is the dataset 1, and z are...

In this short note we give certain comments and improvements of some three-step iterative methods recently considered by N.A. Mir and T. Zaman (Appl. Math. Comput. (2007) doi: 10.1016/j.amc.2007.03.071).

We consider the problem of finding an approximate minimizer of a general quadratic function subject to a two-norm constraint. The Steihaug-Toint method minimizes the quadratic over a sequence of expanding subspaces until the iterates either converge to an interior point or cross the constraint boundary. The benefit of this approach is that an approximate solution may be obtained with minimal wo...

Recommender systems can change our life a lot and help us select suitable and favorite items much more conveniently and easily. As a consequence, various kinds of algorithms have been proposed in last few years to improve the performance. However, all of them face one critical problem: data sparsity. In this paper, we proposed a two-step recommendation algorithm via iterative local least square...