Abstract

In this paper we use a new method for solving a second order partial differential equations. We transform this equations to a system of the integral equations and then to an optimization problem and after ...

In this paper, a new nonlinear neural network for solving the shortest path problem is presented. The our nonlinear neural networks are able to generate optimal solutions to the shortest path problem. The performance of ...

In this paper we use steepest descent method for solving zero-one nonlinear programming problem. Using penalty function we transform this problem to an unconstrained optimization problem and then by steepest descent method ...

The paper deals with convex parametric programming problems. In this paper convex parametric programming transform to a neural network model and then we solve neural network model with one of numerical methods. Finally, ...

In this paper, two new neural network models for solving the maximum flow problem are

presented. The maximum flow problem in networks is formulated as a special type of linear

programming problem and it is ...

This paper presenting case studies in modern large scale constrained optimization,

the purpose of which is to illustrate how recent advances in algorithms and modelling

languages have made it easy to solve ...

In this paper we introduce second-order cone programming. Then we convert fractional programming into secondorder cone programming and apply interior point method for solving second-order cone programming. Hence by solving ...

Abstract: In this paper, we concentrate on linear programming problems in which both the right-hand side and the technological coefficients are fuzzy numbers.We consider here only the case of fuzzy numbers with linear ...

Abstract: The support vector machine (SVM) is one of the most powerful methods in the field of statistica learning theory for constructing a mathematical model in pattern classification. This paper presents a new support ...