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Fen Fakültesi Matematik Bölümü Y
İZMİR INSTITUTE OF TECHNOLOGY GRADUATE SCHOOL OF ENGINEERING AND SCIENCES DEPARTMENT OF MATHEMATICS CURRICULUM OF THE GRADUATE PROGRAMS M.S. in Mathematics (Thesis) Core Courses MATH 596 Graduate Seminar (0-2) NC AKTS:9 MATH 599 Scientific Research Techniques and Publication Ethics (0-2) NC AKTS:8 MATH 500 M.S. Thesis (0-1) NC AKTS:26 MATH 8XX Special Studies (8-0) NC AKTS:4 In addition, the following courses must be taken. MATH 515 Real Analysis (3-0)3 AKTS:8 MATH 527 Basic Abstract Algebra (3-0)3 AKTS:8 *All M.S. students must register Graduate Seminar course until the beginning of their 4th semester. Total credit (min.) :21 Number of courses with credit (min.) : 7 M.S. in Mathematics (Non-thesis) Core Courses MATH 516 Complex Analysis (3-0)3 AKTS:8 MATH 527 Basic Abstract Algebra (3-0)3 AKTS:8 MATH 533 Ordinary Differential Equations (3-0)3 AKTS:8 MATH 534 Partial Differential Equations (3-0)3 AKTS:8 MATH 573 Modern Geometry I (3-0)3 AKTS:8 MATH 595 Graduation Project (0-2) NC AKTS:5 MATH 596 Graduate Seminar (0-2) NC AKTS:9 MATH 599 Scientific Research Techniques and Publication Ethics (0-2) NC AKTS:8 Total credit (min.) :30 Number of courses with credit (min.) :10 Ph.D. in Mathematics Core Courses MATH 597 Comprehensive Studies (0-2) NC AKTS:9 MATH 598 Graduate Seminar in PhD (0-2) NC AKTS:9 MATH 599 Scientific Research Techniques and Publication Ethics* (0-2) NC AKTS:8 MATH 600 Ph.D. -
RESOURCES in NUMERICAL ANALYSIS Kendall E
RESOURCES IN NUMERICAL ANALYSIS Kendall E. Atkinson University of Iowa Introduction I. General Numerical Analysis A. Introductory Sources B. Advanced Introductory Texts with Broad Coverage C. Books With a Sampling of Introductory Topics D. Major Journals and Serial Publications 1. General Surveys 2. Leading journals with a general coverage in numerical analysis. 3. Other journals with a general coverage in numerical analysis. E. Other Printed Resources F. Online Resources II. Numerical Linear Algebra, Nonlinear Algebra, and Optimization A. Numerical Linear Algebra 1. General references 2. Eigenvalue problems 3. Iterative methods 4. Applications on parallel and vector computers 5. Over-determined linear systems. B. Numerical Solution of Nonlinear Systems 1. Single equations 2. Multivariate problems C. Optimization III. Approximation Theory A. Approximation of Functions 1. General references 2. Algorithms and software 3. Special topics 4. Multivariate approximation theory 5. Wavelets B. Interpolation Theory 1. Multivariable interpolation 2. Spline functions C. Numerical Integration and Differentiation 1. General references 2. Multivariate numerical integration IV. Solving Differential and Integral Equations A. Ordinary Differential Equations B. Partial Differential Equations C. Integral Equations V. Miscellaneous Important References VI. History of Numerical Analysis INTRODUCTION Numerical analysis is the area of mathematics and computer science that creates, analyzes, and implements algorithms for solving numerically the problems of continuous mathematics. Such problems originate generally from real-world applications of algebra, geometry, and calculus, and they involve variables that vary continuously; these problems occur throughout the natural sciences, social sciences, engineering, medicine, and business. During the second half of the twentieth century and continuing up to the present day, digital computers have grown in power and availability. -
A Short Course on Approximation Theory
A Short Course on Approximation Theory N. L. Carothers Department of Mathematics and Statistics Bowling Green State University ii Preface These are notes for a topics course offered at Bowling Green State University on a variety of occasions. The course is typically offered during a somewhat abbreviated six week summer session and, consequently, there is a bit less material here than might be associated with a full semester course offered during the academic year. On the other hand, I have tried to make the notes self-contained by adding a number of short appendices and these might well be used to augment the course. The course title, approximation theory, covers a great deal of mathematical territory. In the present context, the focus is primarily on the approximation of real-valued continuous functions by some simpler class of functions, such as algebraic or trigonometric polynomials. Such issues have attracted the attention of thousands of mathematicians for at least two centuries now. We will have occasion to discuss both venerable and contemporary results, whose origins range anywhere from the dawn of time to the day before yesterday. This easily explains my interest in the subject. For me, reading these notes is like leafing through the family photo album: There are old friends, fondly remembered, fresh new faces, not yet familiar, and enough easily recognizable faces to make me feel right at home. The problems we will encounter are easy to state and easy to understand, and yet their solutions should prove intriguing to virtually anyone interested in mathematics. The techniques involved in these solutions entail nearly every topic covered in the standard undergraduate curriculum. -
IRJET-V3I8330.Pdf
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 08 | Aug-2016 www.irjet.net p-ISSN: 2395-0072 A comprehensive study on different approximation methods of Fractional order system Asif Iqbal and Rakesh Roshon Shekh P.G Scholar, Dept. of Electrical Engineering, NITTTR, Kolkata, West Bengal, India P.G Scholar, Dept. of Electrical Engineering, NITTTR, Kolkata, West Bengal, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Many natural phenomena can be more infinite dimensional (i.e. infinite memory). So for analysis, accurately modeled using non-integer or fractional order controller design, signal processing etc. these infinite order calculus. As the fractional order differentiator is systems are approximated by finite order integer order mathematically equivalent to infinite dimensional filter, it’s system in a proper range of frequencies of practical interest proper integer order approximation is very much important. [1], [2]. In this paper four different approximation methods are given and these four approximation methods are applied on two different examples and the results are compared both in time 1.1 FRACTIONAL CALCULAS: INRODUCTION domain and in frequency domain. Continued Fraction Expansion method is applied on a fractional order plant model Fractional Calculus [3], is a generalization of integer order and the approximated model is converted to its equivalent calculus to non-integer order fundamental operator D t , delta domain model. Then step response of both delta domain l and continuous domain model is shown. where 1 and t is the lower and upper limit of integration and Key Words: Fractional Order Calculus, Delta operator the order of operation is . -
Zonotopal Algebra and Forward Exchange Matroids
Zonotopal algebra and forward exchange matroids Matthias Lenz1,2 Technische Universit¨at Berlin Sekretariat MA 4-2 Straße des 17. Juni 136 10623 Berlin GERMANY Abstract Zonotopal algebra is the study of a family of pairs of dual vector spaces of multivariate polynomials that can be associated with a list of vectors X. It connects objects from combinatorics, geometry, and approximation theory. The origin of zonotopal algebra is the pair (D(X), P(X)), where D(X) denotes the Dahmen–Micchelli space that is spanned by the local pieces of the box spline and P(X) is a space spanned by products of linear forms. The first main result of this paper is the construction of a canonical basis for D(X). We show that it is dual to the canonical basis for P(X) that is already known. The second main result of this paper is the construction of a new family of zonotopal spaces that is far more general than the ones that were recently studied by Ardila–Postnikov, Holtz–Ron, Holtz–Ron–Xu, Li–Ron, and others. We call the underlying combinatorial structure of those spaces forward exchange matroid. A forward exchange matroid is an ordered matroid together with a subset of its set of bases that satisfies a weak version of the basis exchange axiom. Keywords: zonotopal algebra, matroid, Tutte polynomial, hyperplane arrangement, multivariate spline, graded vector space, kernel of differential operators, Hilbert series 2010 MSC: Primary 05A15, 05B35, 13B25, 16S32, 41A15. Secondary: 05C31, arXiv:1204.3869v4 [math.CO] 31 Mar 2016 41A63, 47F05. Email address: [email protected] (Matthias Lenz) 1Present address: D´epartement de math´ematiques, Universit´ede Fribourg, Chemin du Mus´ee 23, 1700 Fribourg, Switzerland 2The author was supported by a Sofia Kovalevskaya Research Prize of Alexander von Humboldt Foundation awarded to Olga Holtz and by a PhD scholarship from the Berlin Mathematical School (BMS) 22nd November 2018 1. -
High Order Gradient, Curl and Divergence Conforming Spaces, with an Application to NURBS-Based Isogeometric Analysis
High order gradient, curl and divergence conforming spaces, with an application to compatible NURBS-based IsoGeometric Analysis R.R. Hiemstraa, R.H.M. Huijsmansa, M.I.Gerritsmab aDepartment of Marine Technology, Mekelweg 2, 2628CD Delft bDepartment of Aerospace Technology, Kluyverweg 2, 2629HT Delft Abstract Conservation laws, in for example, electromagnetism, solid and fluid mechanics, allow an exact discrete representation in terms of line, surface and volume integrals. We develop high order interpolants, from any basis that is a partition of unity, that satisfy these integral relations exactly, at cell level. The resulting gradient, curl and divergence conforming spaces have the propertythat the conservationlaws become completely independent of the basis functions. This means that the conservation laws are exactly satisfied even on curved meshes. As an example, we develop high ordergradient, curl and divergence conforming spaces from NURBS - non uniform rational B-splines - and thereby generalize the compatible spaces of B-splines developed in [1]. We give several examples of 2D Stokes flow calculations which result, amongst others, in a point wise divergence free velocity field. Keywords: Compatible numerical methods, Mixed methods, NURBS, IsoGeometric Analyis Be careful of the naive view that a physical law is a mathematical relation between previously defined quantities. The situation is, rather, that a certain mathematical structure represents a given physical structure. Burke [2] 1. Introduction In deriving mathematical models for physical theories, we frequently start with analysis on finite dimensional geometric objects, like a control volume and its bounding surfaces. We assign global, ’measurable’, quantities to these different geometric objects and set up balance statements. -
Math 541 - Numerical Analysis Approximation Theory Discrete Least Squares Approximation
Math 541 - Numerical Analysis Approximation Theory Discrete Least Squares Approximation Joseph M. Mahaffy, [email protected] Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research Center San Diego State University San Diego, CA 92182-7720 http://jmahaffy.sdsu.edu Spring 2018 Discrete Least Squares Approximation — Joseph M. Mahaffy, [email protected] (1/97) Outline 1 Approximation Theory: Discrete Least Squares Introduction Discrete Least Squares A Simple, Powerful Approach 2 Application: Cricket Thermometer Polynomial fits Model Selection - BIC and AIC Different Norms Weighted Least Squares 3 Application: U. S. Population Polynomial fits Exponential Models 4 Application: Pharmokinetics Compartment Models Dog Study with Opioid Exponential Peeling Nonlinear least squares fits Discrete Least Squares Approximation — Joseph M. Mahaffy, [email protected] (2/97) Introduction Approximation Theory: Discrete Least Squares Discrete Least Squares A Simple, Powerful Approach Introduction: Matching a Few Parameters to a Lot of Data. Sometimes we get a lot of data, many observations, and want to fit it to a simple model. 8 6 4 2 0 0 1 2 3 4 5 Underlying function f(x) = 1 + x + x^2/25 Measured Data Average Linear Best Fit Quadratic Best Fit PDF-link: code. Discrete Least Squares Approximation — Joseph M. Mahaffy, [email protected] (3/97) Introduction Approximation Theory: Discrete Least Squares Discrete Least Squares A Simple, Powerful Approach Why a Low Dimensional Model? Low dimensional models (e.g. low degree polynomials) are easy to work with, and are quite well behaved (high degree polynomials can be quite oscillatory.) All measurements are noisy, to some degree. -
Arxiv:1106.4415V1 [Math.DG] 22 Jun 2011 R,Rno Udai Form
JORDAN STRUCTURES IN MATHEMATICS AND PHYSICS Radu IORDANESCU˘ 1 Institute of Mathematics of the Romanian Academy P.O.Box 1-764 014700 Bucharest, Romania E-mail: [email protected] FOREWORD The aim of this paper is to offer an overview of the most important applications of Jordan structures inside mathematics and also to physics, up- dated references being included. For a more detailed treatment of this topic see - especially - the recent book Iord˘anescu [364w], where sugestions for further developments are given through many open problems, comments and remarks pointed out throughout the text. Nowadays, mathematics becomes more and more nonassociative (see 1 § below), and my prediction is that in few years nonassociativity will govern mathematics and applied sciences. MSC 2010: 16T25, 17B60, 17C40, 17C50, 17C65, 17C90, 17D92, 35Q51, 35Q53, 44A12, 51A35, 51C05, 53C35, 81T05, 81T30, 92D10. Keywords: Jordan algebra, Jordan triple system, Jordan pair, JB-, ∗ ∗ ∗ arXiv:1106.4415v1 [math.DG] 22 Jun 2011 JB -, JBW-, JBW -, JH -algebra, Ricatti equation, Riemann space, symmet- ric space, R-space, octonion plane, projective plane, Barbilian space, Tzitzeica equation, quantum group, B¨acklund-Darboux transformation, Hopf algebra, Yang-Baxter equation, KP equation, Sato Grassmann manifold, genetic alge- bra, random quadratic form. 1The author was partially supported from the contract PN-II-ID-PCE 1188 517/2009. 2 CONTENTS 1. Jordan structures ................................. ....................2 § 2. Algebraic varieties (or manifolds) defined by Jordan pairs ............11 § 3. Jordan structures in analysis ....................... ..................19 § 4. Jordan structures in differential geometry . ...............39 § 5. Jordan algebras in ring geometries . ................59 § 6. Jordan algebras in mathematical biology and mathematical statistics .66 § 7. -
Feynman Quantization
3 FEYNMAN QUANTIZATION An introduction to path-integral techniques Introduction. By Richard Feynman (–), who—after a distinguished undergraduate career at MIT—had come in as a graduate student to Princeton, was deeply involved in a collaborative effort with John Wheeler (his thesis advisor) to shake the foundations of field theory. Though motivated by problems fundamental to quantum field theory, as it was then conceived, their work was entirely classical,1 and it advanced ideas so radicalas to resist all then-existing quantization techniques:2 new insight into the quantization process itself appeared to be called for. So it was that (at a beer party) Feynman asked Herbert Jehle (formerly a student of Schr¨odinger in Berlin, now a visitor at Princeton) whether he had ever encountered a quantum mechanical application of the “Principle of Least Action.” Jehle directed Feynman’s attention to an obscure paper by P. A. M. Dirac3 and to a brief passage in §32 of Dirac’s Principles of Quantum Mechanics 1 John Archibald Wheeler & Richard Phillips Feynman, “Interaction with the absorber as the mechanism of radiation,” Reviews of Modern Physics 17, 157 (1945); “Classical electrodynamics in terms of direct interparticle action,” Reviews of Modern Physics 21, 425 (1949). Those were (respectively) Part III and Part II of a projected series of papers, the other parts of which were never published. 2 See page 128 in J. Gleick, Genius: The Life & Science of Richard Feynman () for a popular account of the historical circumstances. 3 “The Lagrangian in quantum mechanics,” Physicalische Zeitschrift der Sowjetunion 3, 64 (1933). The paper is reprinted in J. -
Speech by Honorary Degree Recipient
Speech by Honorary Degree Recipient Dear Colleagues and Friends, Ladies and Gentlemen: Today, I am so honored to present in this prestigious stage to receive the Honorary Doctorate of the Saint Petersburg State University. Since my childhood, I have known that Saint Petersburg University is a world-class university associated by many famous scientists, such as Ivan Pavlov, Dmitri Mendeleev, Mikhail Lomonosov, Lev Landau, Alexander Popov, to name just a few. In particular, many dedicated their glorious lives in the same field of scientific research and studies which I have been devoting to: Leonhard Euler, Andrey Markov, Pafnuty Chebyshev, Aleksandr Lyapunov, and recently Grigori Perelman, not to mention many others in different fields such as political sciences, literature, history, economics, arts, and so on. Being an Honorary Doctorate of the Saint Petersburg State University, I have become a member of the University, of which I am extremely proud. I have been to the beautiful and historical city of Saint Petersburg five times since 1997, to work with my respected Russian scientists and engineers in organizing international academic conferences and conducting joint scientific research. I sincerely appreciate the recognition of the Saint Petersburg State University for my scientific contributions and endeavors to developing scientific cooperations between Russia and the People’s Republic of China. I would like to take this opportunity to thank the University for the honor, and thank all professors, staff members and students for their support and encouragement. Being an Honorary Doctorate of the Saint Petersburg State University, I have become a member of the University, which made me anxious to contribute more to the University and to the already well-established relationship between Russia and China in the future. -
CS70: Jean Walrand: Lecture 30
CS70: Jean Walrand: Lecture 30. Bounds: An Overview Andrey Markov Markov & Chebyshev; A brief review Andrey Markov is best known for his work on stochastic processes. A primary subject of his research later became known as Markov I Bounds: chains and Markov processes. 1. Markov Pafnuty Chebyshev was one of his teachers. 2. Chebyshev Markov was an atheist. In 1912 he protested Leo Tolstoy’s excommunication from the I A brief review Russian Orthodox Church by requesting his I A mini-quizz own excommunication. The Church complied with his request. Monotonicity Markov’s inequality A picture The inequality is named after Andrey Markov, although it appeared earlier in Let X be a RV. We write X 0 if all the possible values of X are the work of Pafnuty Chebyshev. It should be (and is sometimes) called ≥ Chebyshev’s first inequality. nonnegative. That is, Pr[X 0] = 1. ≥ Theorem Markov’s Inequality It is obvious that X 0 E[X] 0. Assume f : ℜ [0,∞) is nondecreasing. Then, ≥ ⇒ ≥ → We write X Y if X Y 0. E[f (X)] ≥ − ≥ Pr[X a] , for all a such that f (a) > 0. Then, ≥ ≤ f (a) Proof: X Y X Y 0 E[X] E[Y ] = E[X Y ] 0. ≥ ⇒ − ≥ ⇒ − − ≥ Observe that f (X) Hence, 1 X a . { ≥ } ≤ f (a) X Y E[X] E[Y ]. ≥ ⇒ ≥ Indeed, if X < a, the inequality reads 0 f (X)/f (a), which ≤ We say that expectation is monotone. holds since f ( ) 0. Also, if X a, it reads 1 f (X)/f (a), which · ≥ ≥ ≤ (Which means monotonic, but not monotonous!) holds since f ( ) is nondecreasing. -
Calculations on Randomness Effects on the Business Cycle Of
Calculations on Randomness Effects on the Business Cycle of Corporations through Markov Chains Christian Holm [email protected] under the direction of Mr. Gaultier Lambert Department of Mathematics Royal Institute of Technology Research Academy for Young Scientists Juli 10, 2013 Abstract Markov chains are used to describe random processes and can be represented by both a matrix and a graph. Markov chains are commonly used to describe economical correlations. In this study we create a Markov chain model in order to describe the competition between two hypothetical corporations. We use observations of this model in order to discuss how market randomness affects the corporations financial health. This is done by testing the model for different market parameters and analysing the effect of these on our model. Furthermore we analyse how our model can be applied on the real market, as to further understand the market. Contents 1 Introduction 1 2 Notations and Definitions 2 2.1 Introduction to Probability Theory . 2 2.2 Introduction to Graph and Matrix Representation . 3 2.3 Discrete Time Markov Chain on V ..................... 6 3 General Theory and Examples 7 3.1 Hidden Markov Models in Financial Systems . 7 4 Application of Markov Chains on two Corporations in Competition 9 4.1 Model . 9 4.2 Simulations . 12 4.3 Results . 13 4.4 Discussion . 16 Acknowledgements 18 A Equation 10 20 B Matlab Code 20 C Matlab Code, Different β:s 21 1 Introduction The study of Markov chains began in the early 1900s, when Russian mathematician Andrey Markov first formalised the theory of Markov processes [1].