MultiLinear Discriminant Analysis (MDA) is a powerful dimension reduction method specifically formulated to deal with tensor data. Precisely, the goal of MDA  is to find mode-specific projections that optimally separate tensor data from different classes. However, to solve this task, standard MDA methods use alternating optimization heuristics involving the computation of a succession of tensor-matrix products. Such approaches are most of the time difficult to solve and not natural, highligthing the difficulty to formulate this problem in fully tensor form. In this paper, we propose to solve multiLinear discriminant analysis (MDA) by using the concept of transform domain (TD) recently proposed in [15]. We show here that moving MDA to this specific transform domain make its resolution easier and more natural. More precisely, each frontal face of the transformed tensor is processed independently to build a separate optimization sub-problems easier to solve. Next, the obtained solutions are converted into projective tensors by inverse transform. By considering a large number of experiments, we show the effectiveness of our approach with respect to existing MDA methods.

Material balance around process has been conducted by using mixer output equations. A method based on Mason"s theory has been proposed to develop mixer output equations.

In this paper we find solution of Linear interval equation in dual form based on the generalized procedure of interval extension which is called interval extended zero method.Moreover, proposed method has significant advantage, is that it substantially decreases the excess width effect.

In this paper, normal equations for singular fuzzy Linear systems is proposed. The principal application of the normal equations is to systems of equations that are inconsistent. The results are explained by solving some numerical examples.

In this paper we  nd all solutions of four kinds of the Diophantine equations x2  Vtxy 􀀀 y2  x = 0 and x2  Vtxy 􀀀 y2  y = 0; for an odd number t, and, x2  Vtxy + y2 􀀀 x = 0 and x2  Vtxy + y2 􀀀 y = 0; for an even number t, where Vn is a generalized Lucas number. This paper continues and extends a previous work of Bahramian and Daghigh.

This paper aims to extend a Krylov subspace technique based on an in-complete orthogonalization of Krylov tensors (as a multidimensional exten-sion of the common Krylov vectors) to solve generalized Sylvester tensor equations via the Einstein product. First, we obtain the tensor form of the quasi-GMRES method, and then we lead to the direct variant of the proposed algorithm. This approach has the great advantage that it uses previous data in each iteration and has a low computational cost. More-over, an upper bound for the residual norm of the approximate solution is found. Finally, several experimental problems are given to show the acceptable accuracy and efficiency of the presented method.