This paper is concerned with Minimum Spanning Tree problem, a fundamental problem of Network modeling. Here we have proposed a novel approach to determine minimum spanning tree of an undirected connected network, which is also demonstrated with numerical example.

Most of the current methods for solving linear fractional programming (LFP) problems depend on the simplex type method. In this paper, we present a new approach for solving linear fractional programming problem in which the objective function is a linear fractional function, while constraint functions are in the form of linear inequalities. This approach does not depend on the simplex type method. Here first we transform this LFP problem into linear programming (LP) problem and hence solve this problem algebraically using the concept of duality. Two simple examples to illustrate our algorithm are given. And also we compare this approach with other available methods for solving LFP problems.

In this paper, we study the interior point algorithm for solving linear programming (LP) problem developed by Narendra Karmarkar. As interior point algorithm for LP problem involves tremendous calculations, it is quite impossible to do so by hand calculation. To fulfill the requirement we develop computer code in MATLAB for LP which is based on this algorithm procedure. To illustrate the purpose, we formulate a real life sizeable large-scale linear program for diet problem and solve it using our computer code for interior point algorithm in MATLAB. Dhaka Univ. J. Sci. 65(1): 41-47, 2017 (January)

The goal of the research work is to examine the improvement of numerical solution of VdP equation. The well-known VdP equation is governed by the second order nonlinear ODE and then solved numerically using the classical Runge-Kutta (RK) method, RK-Fehlberg method of order five, Verner method of order eight and Cash-Karp method of order six with nonlinear shooting technique. In this work, numerical simulations have been carried out using NVdP code which is written in MATHEMATICA. Also, the accuracy and efficiency of the solution of VdP equation using different RK methods with nonlinear shooting technique has been investigated. For analysis of accuracy, the approximate exact solution obtained by perturbation method is used for the comparison. It is observed that all the different RK methods give accurate result of the VdP equation. But the classical RK method shows slightly better performance than the other single step techniques. Dhaka Univ. J. Sci. 66(1): 43-47, 2018 (January)

Iris Recognition is regarded as the most reliable and accurate biometric identification system available. In Iris Recognition, a person is identified by the iris region of the eye using image processing, pattern matching and the concept of neural networks. A typical Iris Recognition system involves three steps, Iris pre-processing, Iris feature extraction and Iris Classification. Most of the researchers use Daugman’s integro-differential operator and Daugman’s rubber sheet model for pre-processing. A number of feature extraction methods can be used to achieve a reasonable recognition rate. In our work we have used Supervised Regularized Multidimensional Scaling proposed recently for feature extraction that is used directly on iris image regarded as high dimensional vector. The method uses radial basis function to select some images as centres and then projects higher dimensional vectors into a lower dimensional space using an Iterative majorization algorithm. The projection is done in such a way that data of same class projects together and also it selects the most effective features that leads to better recognition rate. This approach excludes the pre-processing that saves computation time. We have compared our approach with Principal Component Analysis and implemented on a benchmarking data MMU iris data. K-Nearest Neighbor classifier is used for the classification. Numerical experiments show that Supervised Regularized Multidimensional Scaling successfully achieves better recognition and outperforms some other approaches such as Principal Component Analysis with and without pre-processing of iris images.