Ferdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101Quasi-permutation Representations of Borel and Parabolic Subgroups of Steinberg's triality groups2440610.22067/ijnao.v2i1.637ENM.GhorbanyIran University of Science and Tecnology, MazandaranJournal Article19700101If G is a finite linear group of degree n, that is, a finite group of automor-phisms of an n-dimensional complex vector space, or equivalently, a finite group of non-singular matrices of order n with complex coefficients, we shall say that G is a quasi-permutation group if the trace of every element of G is a non-negative rational integer. By a quasi-permutation matrix we mean a square matrix over the complex field C with non-negative integral trace.
Thus every permutation matrix over C is a quasi-permutation matrix. For a given finite group G, let c(G) denote the minimal degree of a faithful rep-resentation of G by quasi-permutation matrices over the complex numbers and let r(G) denote the minimal degree of a faithful rational valued complex character of G. The purpose of this paper is to calculate c(G) and r(G) for
the Borel and parabolic subgroups of Steinberg's triality groups.https://ijnao.um.ac.ir/article_24406_92aaa71effee65cb8ea54addc4b368fb.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101Properties of groups with points2440710.22067/ijnao.v2i1.734ENV.I.SenashovE.N.TakovlevaJournal Article19700101In this paper, we consider groups with points which were introduced by V.P. Shunkov in 1990. In Novikov-Adian's group, Adian's periodic products of finite groups without involutions and Olshansky's periodic monsters every non-unit element is a point. There exist groups without points. In this article we shall prove some properties of the groups with points.https://ijnao.um.ac.ir/article_24407_96ad78d60fad4b3bbef76e69f0b51663.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101A sufficient condition for null controllability of nonlinear control systems2440810.22067/ijnao.v2i1.2217ENA.HeydariA.V.KamyadJournal Article19700101Classical control methods such as Pontryagin Maximum Principle and Bang-Bang Principle and other methods are not usually useful for solving opti-mal control systems (OCS) specially optimal control of nonlinear systems (OCNS). In this paper, we introduce a new approach for solving OCNS by using some combination of atomic measures. We define a criterion for controllability of lumped nonlinear control systems and when the system is nearly null controllable, we determine controls and states. Finally we use this criterion to solve some numerical examples.https://ijnao.um.ac.ir/article_24408_2fc2a9143641b059127548712de16289.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101The variational iteration method for solving linear and nonlinear Schrodinger equations2440910.22067/ijnao.v2i1.877ENB.JazbiM.MoiniJournal Article19700101In this paper, the variational iteration method proposed by Ji-Huan He is applied to solve both linear and nonlinear Schrodinger equations. The main property of the method is in its flexibility and ability to solve linear and nonlinear equations accurately and conveniently. In this method, general lagrange multipliers are introduced to construct correction functionals to the problems. The multipliers in the functionals can be identified optimally via the variational theory. Numerical results show that this method can readily be implemented with excellent accuracy to linear and nonlinear Schrodinger equations. This technique can be extended to higher dimensions linear and nonlinear Schrodinger equations without a series difficulty.https://ijnao.um.ac.ir/article_24409_1021656ce46d81e8a169aa7e41ab89e1.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101Asymptotic normality of the truncation probability estimator for truncated dependent data2441010.22067/ijnao.v2i1.639ENS.JomhooriV.FakoorH. A.AzarnooshJournal Article19700101In some long term studies, a series of dependent and possibly truncated life-times may be observed. Suppose that the lifetimes have a common marginal distribution function. In left-truncation model, one observes data (Xi,Ti)
only, when Ti ≤ Xi. Under some regularity conditions, we provide a strong representation of the ßn estimator of ß = P(Ti ≤ Xi), in the form of an average of random variables plus a remainder term. This representation en-ables us to obtain the asymptotic normality for ßn .https://ijnao.um.ac.ir/article_24410_cc1c4dab5a3df245a5b66f088d6da479.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101Asymptotic fisher information in order statistics of geometric distribution2441110.22067/ijnao.v2i1.664ENM.RoozbehS. M. M.TabatabaeyJournal Article19700101In this paper, the geometric distribution is considered. The means, variances, and covariances of its order statistics are derived. The Fisher information in any set of order statistics in any distribution can be represented as a sum of Fisher information in at most two order statistics. It is shown that, for the geometric distribution, it can be further simplified to a sum of Fisher information in a single order statistic. Then, we derived the asymptotic Fisher information in any set of order statistics.https://ijnao.um.ac.ir/article_24411_9ae84cb1bb03d6fc9fbb1177d0e30e74.pdfFerdowsi University of MashhadIranian Journal of Numerical Analysis and Optimization2423-69772120090101Estimation of P[Y < X] for generalized exponential distribution in presence of outlier2441210.22067/ijnao.v2i1.645ENP.NasiriM.Jabbari NooghabiJournal Article19700101This paper deals with the estimation of P(Y < X), where Y has generalized exponential distribution with parameters a and A and X has mixture gen-eralized exponential distribution (or marginal distribution of X\, X2,…. Xn,
in presence of one outlier with parameters ß1 and ß2 ) such that X and Y are independent, when the scale parameter (A) is known the maximum like-lihood estimator of R = P(Y < X) is derived. Analysis of a simulated data
set has also been presented for illustrative purposes.https://ijnao.um.ac.ir/article_24412_4db2a660fdb6f34e9086c7f4c440104b.pdf