University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 Ten lessons I wish I had been taught Ten lessons I wish I had been taught 1 11 3107 10.22108/msci.2016.3107 FA Alireza Abdollahi University of Isfahan. Journal Article 2013 06 02 This paper is a translation of the the following paper into Persian: [Gian-Carlo Rota, Ten lessons I wish I had been taught, Notices of the American Mathematical Society, 44 (1997) no. 1 22–25.] The main difference with the original text is the adding of some pictures for each name-mentioned person in the above paper. This paper is a translation of the the following paper into Persian: [Gian-Carlo Rota, Ten lessons I wish I had been taught, Notices of the American Mathematical Society, 44 (1997) no. 1 22–25.] The main difference with the original text is the adding of some pictures for each name-mentioned person in the above paper. https://math-sci.ui.ac.ir/article_3107_4691da45efd9effab7b5c1e81a111155.pdf

University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 A study of filter method for solving non-linear problems A study of filter method for solving non-linear problems 13 25 3437 10.22108/msci.2016.3437 FA Najmeh Hoseini Department of Mathematics, University of Isfahan Journal Article 2013 06 24 One of methods for solving nonlinear programming problems that has been used for years is Penalty method. In this paper we introduce a new concept that is Filter, then we expressed algorithm for solving nonlinear constrained programming problems so that the Penalty function is not used. If we uses the algorithm of the Filter instead of the Penalty function, them some of the problems of the penalty method are solved and also the global convergence will be implied. One of methods for solving nonlinear programming problems that has been used for years is Penalty method. In this paper we introduce a new concept that is Filter, then we expressed algorithm for solving nonlinear constrained programming problems so that the Penalty function is not used. If we uses the algorithm of the Filter instead of the Penalty function, them some of the problems of the penalty method are solved and also the global convergence will be implied.

https://math-sci.ui.ac.ir/article_3437_d71a230ce9c21954c3728bf8b63e8f4a.pdf

University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 Aaj Chandra Bose and block designs Aaj Chandra Bose and block designs 27 36 3646 10.22108/msci.2016.3646 FA Elhame Azangoyanfard Department of Mathematics, University of Isfahan Journal Article 2013 08 06 In this paper we first look at the scientific biography of Raj Chandra Bose and next we review some samples of his scientific works. In this paper we first look at the scientific biography of Raj Chandra Bose and next we review some samples of his scientific works. https://math-sci.ui.ac.ir/article_3646_f78be9c9704ac11513a0200ea0b831c5.pdf

University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 A review on integer programming optimization problems A review on integer programming optimization problems 37 46 3660 10.22108/msci.2016.3660 FA Rassool Hosseini Department of Mathematics, University of Isfahan Journal Article 2013 08 24 The Integer Programming is one special kind of optimization problemes. In this problemes one or more decision variables must be integer. In many of real problems decimal values are not accepted. In this paper we will introduce integer programming and their applications. The Integer Programming is one special kind of optimization problemes. In this problemes one or more decision variables must be integer. In many of real problems decimal values are not accepted. In this paper we will introduce integer programming and their applications. https://math-sci.ui.ac.ir/article_3660_356f1c0f2bb800ac45700be458f4de26.pdf

University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 A method to compute the determinant of a tri-diagonal matrix A method to compute the determinant of a tri-diagonal matrix 47 57 3765 10.22108/msci.2016.3765 FA Mona Shahsavari Department of Basic Science Engineering, University of Tehran Journal Article 2013 08 20 In this paper we first explain a recursive method and then two algorithms to find determinant of a particular state of tri diagonal $ n\times n$ matrices, so that by using them we can obtain determinant without calculation it in the usual manner and in some cases in easier manner. In the first approach by the aid of tri diagonal matrices of smaller sizes, the determinant of the matrix can be calculated recursively. In the first algorithm we place series of $2\times2$ blocks on the main diagonal of the matrix, during the process which has been explained in the article, we get the determinant of that matrix. In second algorithm by the aid of two tables in which their elements obtained by special algorithms, we calculate determinant of the matrix. In this paper we first explain a recursive method and then two algorithms to find determinant of a particular state of tri diagonal $ n\times n$ matrices, so that by using them we can obtain determinant without calculation it in the usual manner and in some cases in easier manner. In the first approach by the aid of tri diagonal matrices of smaller sizes, the determinant of the matrix can be calculated recursively. In the first algorithm we place series of $2\times2$ blocks on the main diagonal of the matrix, during the process which has been explained in the article, we get the determinant of that matrix. In second algorithm by the aid of two tables in which their elements obtained by special algorithms, we calculate determinant of the matrix.

https://math-sci.ui.ac.ir/article_3765_a212a893a904702a6943ed4cf377b385.pdf

University of Isfahan Mathematics and Society 2345-6493 1 1 2016 05 21 Augmented lagrangian method and implementations in signal processing Augmented lagrangian method and implementations in signal processing 59 66 3798 10.22108/msci.2016.3798 FA Somayeh Ahmadi Bani Department of Mathematics, University of Isfahan Journal Article 2013 10 14 In this article we study Augmented Lagrangian method which is an algorithm for solving constrained optimization problems. Then we compare this algorithm with penalty method. Some examples of software that use the augmented lagrangian method are presented. We introduce Total Variation Denoising and Compressed Sensing that are used in signal processing. Also, we express some implementations of compressed sensing in industry and technology. In this article we study Augmented Lagrangian method which is an algorithm for solving constrained optimization problems. Then we compare this algorithm with penalty method. Some examples of software that use the augmented lagrangian method are presented. We introduce Total Variation Denoising and Compressed Sensing that are used in signal processing. Also, we express some implementations of compressed sensing in industry and technology.

https://math-sci.ui.ac.ir/article_3798_a486d6e7545fee189c6d727e2c6480b5.pdf