Theory of Approximation and Applications
Volume:14 Issue: 1, Winter and Spring 2020

The purpose of this study was to analyze the role of company monitoring factors on cost stickiness. The spatial realm was the companies listed on the Tehran Stock Exchange and the time realm was during 2014-2020 and 115 companies have selected by the systematic elimination method as a statistical sample. To collect data, reference to financial statements, explanatory notes and stock exchange monthly journals and to describe and summarize data, the descriptive and inferential statistics have been used. In data analysis, F-Leimer test, Hausman test and Jarque-Bera test were used to confirm and reject the hypotheses (Eviews software). The results showed that the company's supervisory factors affect the degree of cost stickiness by relying on the role of accounting-based performance which the results obtained in this study are consistent with the documents mentioned in the theoretical framework of research and financial literature.

AbstractThis paper manages to develop a chance constrained fractional modeling of the waste management taking systems sustainability indices into consideration. Due to the fact that there are some approaches for dealing with the city waste management, but the use of the one that has big positive impacts on the economic and social dimensions of sustainability and least impacts on the environmental side of the problem might of the most concern. To deal with this issue, related city engineers and managers are asked to present their ideas on this problem by filling a questionnaire. Author takes these view point into consideration and then by using fuzzy TOPSIS as a decision making tool for ranking the strategies proposed to city engineers and then those strategies with the highest ranking and implementable in our city was employed for continuation of this study. Once appropriate strategies with sustainability consideration are determined, a stochastic programming model is proposed and a linearization technique was recommended to solve that. A sample case problem is used for presentation purposes.

Supplier selection plays an essential role in organizations due to the cost of raw material constitutes the main cost of the final product. Thus, we develop a new approach to solve the multiple criteria supplier selection problem. The proposed method considers the effects of weights in the final solution. An illustrative example is presented to show the capabilities of our approach.

In this manuscript, we consider the interpolative contractions mappings via simulation func-tions in the setting of complete metric space. We also express an illustrative example to show the validity of our presented results.

In this paper, based on viscosity technique with perturbation, we introduce a new non-linear viscosity algorithm for finding a element of the set of fixed points of nonexpansivemulti-valued mappings in a Hilbert space. We derive a strong convergence theorem for thisnew algorithm under appropriate assumptions. Moreover, in support of our results, somenumerical examples (using Matlab software) are also presented.

IIn this research work, we have shown that it is possible to use fuzzy transform method (FTM) for the estimate solution of fractional system differential equations (FSDEs). In numerical methods, in order to estimate a function on a particular interval, only a restricted number of points are employed. However, what makes the F-transform preferable to other methods is that it makes use of all points in this interval. A number of clear and specific examples have been enumerated for the purpose of illustrating the simplicity and efficiency of the suggested method.

In recent years, sequential optimality conditions are frequently used for convergence of iterative methods to solve nonlinear constrained optimization problems. The sequential optimality conditions do not require any of the constraint qualications. In this paper, We present the necessary sequential complementary approximate Karush Kuhn Tucker (CAKKT) condition for a point to be a solution of a nonlinear optimization problem. The nonlinear optimization problem is associated with the variational inequality problem. We also extend the complementary approximate Karush Kuhn Tucker condition from scalar optimization problem to multiobjective optimization problem and associated with the vector variational inequality problem. Further, we prove that with some extra conditions of convexity and affinity, complementary approximate Karush Kuhn Tucker conditions are sufficient for the variational inequality problem and vector variational inequality problem. Finally, we verify our results via illustrative examples. An example shows that a point which is a solution of variational inequality problem is also a CAKKT point.

A generalized Bethe tree is a rooted unweighted tree in whichthe vertices in each of its levels have equal degree. In this paperwe derive an explicit formula for the characteristic polynomialsof the adjacency and Laplacian matrices of unweighted rootedtree which obtained from the union of the generalized Bethe treesjoined at their respective root vertices by using of rooted productof graphs.

This paper, about the solution of fuzzy Volterra integral equation of fuzzy Volterra integral equation of second kind (F-VIE2) using spectral method is discussed. The parametric form of fuzzy driving term is applied for F-VIE2. Then three cases for (F-VIE2) are searched to solve them. This classifications are considered based on the sign of interval. The Gauss-Legendre points and Legendre weights for arithmetics in spectral method are used to solve (F-VIE2). Finally two examples are got to illustrate more.

The aim of this paper is to find eigenvalues of square matrix A based on the Artificial Bee Colony algorithm and the Bisection method(BIABC ). At first, we obtain initial interval [a,b] that included all eigenvalues based on Gerschgorin's theorem, and then by using Artificial Bee Colony algorithm(ABC) at this interval to generate initial value for each eigenvalue. The bisection method improves them until the best values of eigenvalues with arbitrary accuracy will be achieved. This enables us to find eigenvalues with arbitrary accuracy without computing the derivative of the characteristic polynomial of the given matrix. We illustrate the proposed method with Some numerical examples.

For {φ_n(x)}, x ε [0,1] an orthonormalsystem of uniformly bounded functions, ||φ_n||_{∞}≤ M

Today, deep learning has attracted attention in various scientific and non-scientific fields. Deep learning is a branch of machine learning that simulates the human brain for various applications like recognizing voice, face, handwriting, identifying kinship, image processing, and etc. In deep learning, a set of representation algorithms is used to model high-level abstract concepts through learning at different levels and layers. Deep learning has become popular due to its capabilities like automatic feature extraction, high extendibility, and wide application in different fields. In this paper, it is tried to describe different deep learning models and architectures, how they are trained, and the required hardware and software structures.