...Advanced linear algebra M. Anthony, M. Harvey MT2118, 2790118 2011 Undergraduate study in Economics, Management, Finance and the Social Sciences This is an extract from a subject guide for an undergraduate course offered as part of the University of London International Programmes in Economics, Management, Finance and the Social Sciences. Materials for these programmes are developed by academics at the London School of Economics and Political Science (LSE). For more information, see: www.londoninternational.ac.uk This guide was prepared for the University of London International Programmes by: Professor M. Anthony, BSc, MA, PhD and Dr M. Harvey, BSc, MSc, PhD, Department of Mathematics, The London School of Economics and Political Science. This is one of a series of subject guides published by the University. We regret that due to pressure of work the authors are unable to enter into any correspondence relating to, or arising from, the guide. If you have any comments on this subject guide, favourable or unfavourable, please use the form at the back of this guide. University of London International Programmes Publications Office Stewart House 32 Russell Square London WC1B 5DN United Kingdom Website: www.londoninternational.ac.uk Published by: University of London © University of London 2006 Reprinted with minor revisions 2011 The University of London asserts copyright over all material in this subject guide except where otherwise indicated....
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... 10 a. Geology and Glaciology 10-11 b. Vibration Analysis 11-12 c. Tensor of Moment of Inertia 12 d. Stress Tensor 12 e. Basic Reproduction Number. 12 6. Conclusion 13 7. References 13 3 Abstract In abstract linear algebra, these concepts are naturally extended to more general situations,...
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...Excel and most other Windows-based programs. In this document the symbol >> represents the Matlab prompt in the command window. Commands following it indicate that you should type these in; Matlab commands or responses are printed in Bookman Font. Pushing the Enter key causes the command to be executed. Matlab has extensive help resources, available under the "Help" tab of the MATLAB window, or type >> help topic. Hint Use the ( key to recall previous commands, which you can then edit or re-execute. Type a letter or part command followed by the ( key, and the last instance of a matching command will be recalled. 1. Basic input and display Matlab stands for MATrix LABoratory, and was originally written to perform matrix algebra[2]. It is very efficient at doing this, as well as having the capabilities of any normal programming language. In most instances,...
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...clear KMLIM TCI2261 2012/2013 Scripts New script Debugging Tools % These are comments ` Variables As you work in MATLAB, you issue commands that create variables and call functions. For example, create a variable named a by typing this statement at the command line: a=1 MATLAB adds variable a to the workspace and displays the result in the Command Window. a= 1 When you do not specify an output variable, MATLAB uses the variable ans, short for answer, to store the results of your calculation. sin(a) ans = 0.8415 If you end a statement with a semicolon, MATLAB performs the computation, but suppresses the display of output in the Command Window. sin(a); At any time you want to know the active variables you can use: KMLIM TCI2261 2012/2013 Arrays MATLAB is an abbreviation for "matrix laboratory."While other programming languages mostly work with numbers one at a time, MATLAB is designed to operate primarily on whole matrices and arrays. All MATLAB variables are multidimensional arrays, no matter what type of data. A matrix is a two-dimensional array often used for linear algebra. Row vector: comma or space separated values between brackets Column vector: semicolon separated values between brackets Matrices Matrix...
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...SCHAUM’S outlines SCHAUM’S outlines Linear Algebra Fourth Edition Seymour Lipschutz, Ph.D. Temple University Marc Lars Lipson, Ph.D. University of Virginia Schaum’s Outline Series New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2009, 2001, 1991, 1968 by The McGraw-Hill Companies, Inc. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. ISBN: 978-0-07-154353-8 MHID: 0-07-154353-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-154352-1, MHID: 0-07-154352-X. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. To contact a representative please e-mail us at bulksales@mcgraw-hill.com. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies,...
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...Regular Matrix | 8 | 2.4.13 Singular Matrix | 8 | Chapter-03: Matrices Operation | 9-15 | 3.1. Properties of matrix operation | 9 | 3.1.1 Properties of subtraction | 9 | 3. 1.2 Properties of Addition | 9 | 3.1.3 Properties of Matrix Multiplication | 10 | 3.1.4 Properties of Scalar Multiplication | 10 | 3.1.5 Properties of the Transpose of a Matrix | 10 | 3.2 Matrix Operation | 11 | 3.2.1 Matrix Equality | 12 | 3.2.2 Matrix Addition | 12 | 3.2.3 Matrix Subtraction | 12 | 3.2.4 Matrix Multiplication | 12 | 3.2.5 Multiplication of Vectors | 14 | 3.3 Inverse of Matrix | 15 | 3.4 Elementary Operations | 15 | Chapter-04: Application of Matrix | 16-21 | 4.1 Application of Matrix | 16 | 4.1.1 Solving Linear Equations | 16 | 4.1.2 Electronics | 16 | 4.1.3 Symmetries and transformations in physics | 17 | 4.1.4 Analysis and geometry | 17 | 4.1.5 Probability theory and statistics | 17 | 4.1.6 Cryptography | 18 | 4.2. Application of Matrices in Real Life | 18 | Chapter-05:Findings and Recommendation | 20-22 | 5.1 Findings | 20...
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...Linear Least Squares Suppose we are given a set of data points {(xi , fi )}, i = 1, . . . , n. These could be measurements from an experiment or obtained simply by evaluating a function at some points. You have seen that we can interpolate these points, i.e., either find a polynomial of degree ≤ (n − 1) which passes through all n points or we can use a continuous piecewise interpolant of the data which is usually a better approach. How, it might be the case that we know that these data points should lie on, for example, a line or a parabola, but due to experimental error they do not. So what we would like to do is find a line (or some other higher degree polynomial) which best represents the data. Of course, we need to make precise what we mean by a “best fit” of the data. As a concrete example suppose we have n points (x1 , f1 ), (x2 , f2 ), ··· (xn , fn ) and we expect them to lie on a straight line but due to experimental error, they don’t. We would like to draw a line and have the line be the best representation of the points. If n = 2 then the line will pass through both points and so the error is zero at each point. However, if we have more than two data points, then we can’t find a line that passes through the three points (unless they happen to be collinear) so we have to find a line which is a good approximation in some sense. Of course we need to define what we mean by a good representation. An obvious approach would be to create an error vector of length n and each...
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...Lecture Notes on Mathematics for Economists Chien-Fu CHOU September 2006 Contents Lecture 1 Lecture 2 Lecture 3 Lecture 4 Lecture 5 Lecture 6 Lecture 7 Lecture 8 Lecture 9 Lecture 10 Static Economic Models and The Concept of Equilibrium Matrix Algebra Vector Space and Linear Transformation Determinant, Inverse Matrix, and Cramer’s rule Differential Calculus and Comparative Statics Comparative Statics – Economic applications Optimization Optimization–multivariate case Optimization with equality constraints and Nonlinear Programming General Equilibrium and Game Theory 1 5 10 16 25 36 44 61 74 89 1 1 Static Economic Models and The Concept of Equilibrium Here we use three elementary examples to illustrate the general structure of an economic model. 1.1 Partial market equilibrium model A partial market equilibrium model is constructed to explain the determination of the price of a certain commodity. The abstract form of the model is as follows. Qd = D(P ; a) Qd : Qs : P: a: Qs = S(P ; a) Qd = Qs , quantity demanded of the commodity quantity supplied to the market market price of the commodity a factor that affects demand and supply D(P ; a): demand function S(P ; a): supply function Equilibrium: A particular state that can be maintained. Equilibrium conditions: Balance of forces prevailing in the model. Substituting the demand and supply functions, we have D(P ; a) = S(P ; a). For a given a, we can solve this last equation to obtain the equilibrium...
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...high-risk decisions as a manager can be based. One also needs to know how to analyze the research findings. The study of quantitative techniques provides one with the knowledge and skills needed to solve the problems and the challenges of a fast-paced decisionmaking environment. Managers make decisions on a day to day basis and it is necessary for them to be able to analyze the data so as to be able to make optimal decisions. This module has ten lesson which cover matrix algebra, markov analysis, Linear programming, differentiation, applications of differentiation to cost, revenue and profit functions, integral calculus, inventory models, sampling and estimation theory, hypothesis testing and chi-square tests. iii MODULE OBJECTIVES By the end of the course, the student should be able to:- 1. Perform various operations on matrices matrix algebra, 2. Apply the concept of matrices in solving simultaneous equations, input-output analysis and markov analysis, 3. Formulate and solve Linear programming using the graphical and simplex method 4. Differentiate various functions and apply to cost, revenue and profit functions 5. Apply...
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...University Press 0521652278 - Mathematical Methods for Physicists: A Concise Introduction - Tai L. Chow Excerpt More information 1 Vector and tensor analysis Vectors and scalars Vector methods have become standard tools for the physicists. In this chapter we discuss the properties of the vectors and vector ®elds that occur in classical physics. We will do so in a way, and in a notation, that leads to the formation of abstract linear vector spaces in Chapter 5. A physical quantity that is completely speci®ed, in appropriate units, by a single number (called its magnitude) such as volume, mass, and temperature is called a scalar. Scalar quantities are treated as ordinary real numbers. They obey all the regular rules of algebraic addition, subtraction, multiplication, division, and so on. There are also physical quantities which require a magnitude and a direction for their complete speci®cation. These are called vectors if their combination with each other is commutative (that is the order of addition may be changed without aecting the result). Thus not all quantities possessing magnitude and direction are vectors. Angular displacement, for example, may be characterised by magnitude and direction but is not a vector, for the addition of two or more angular displacements is not, in general, commutative (Fig. 1.1). In print, we shall denote vectors by boldface letters (such as A) and use ordinary italic letters (such as A) for their magnitudes; in writing, vectors are usually...
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...the image of the point (3, 2) under this transformation, you need to find the result of the following matrix multiplication So the coordinates of the image are (8, -1). Example 2: A rectangle has coordinates (1, 1), (4, 1), (4, 3) and (1, 3). Find the coordinates of the image of the rectangle under the transformation represented by the matrix . Solution: You could find the image of each vertex in turn by finding , etc. However, it is more efficient to multiply the transformation matrix by a rectangular matrix containing the coordinates of each vertex: . So the image has coordinates (2, 0), (11, -3), (9, -1) and (0, 2). The diagram below shows the object and the image: Any transformation that can be represented by a 2 by 2 matrix, , is called a linear transformation. 1.1 Transforming the unit square The square with coordinates O(0, 0), I(1, 0), J(0, 1) and K(1, 1) is called the unit square. Suppose we consider the image of this square under a general linear transformation as represented by the matrix : . We therefore can notice the following things: * The origin O(0, 0) is mapped to itself; * The image of the point I(1, 0) is (a, c), i.e. the first column of the transformation matrix; * The image of the point J(0, 1) is (b, d), i.e. the second column of the transformation matrix; * The image of the point K(1, 1) is (a + b, c+ d), i.e. the result of finding the sum of the entries in each row of the matrix. Example: Find...
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...MATH 3330 INFORMATION SHEET FOR FINAL EXAM FALL 2011 FINAL EXAM will be in PKH 103 at 2:00-4:30 pm on Tues Dec 13 • See above for date, time and location of FINAL EXAM. Recall from the first-day handout that any student not obtaining a positive score on the FINAL EXAM will not pass this class. • The material covered will be the same as that covered on the homework from the start of the semester through Dec 6 (but not §6.3) inclusive. (Homework is listed at my website: www.uta.edu/math/vancliff/T/F11 .) • My remaining office hours are: 3:30-4:20 pm on Thurs Dec 8 and 3:30-5:30 pm on Mon Dec 12. • This test will be, in part, multiple choice, but you do NOT need to bring a scantron form. There will be several choices of answer per multiple-choice question and, for each, only one answer will be the correct one. You should do rough work on the test or on paper provided by me. No calculator is allowed. No notes or cards are allowed. BRING YOUR MYMAV ID CARD WITH YOU. • When I write a test, I look over the lecture notes and homework which have already been assigned, and use them to model about 85% of the test problems (and most of them are fair game). You should expect between 30 and 40 questions in total. • A good way to review is to go over the homework problems you have not already done & make sure you understand all the homework well by 48 hours prior to the test. You should also look over the past tests/midterms and understand those fully. In addition, this...
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............3, 7 cofactor ........................4 coordinate vector ..........9 Cramer's rule................1 determinant...............2, 5 diagonal matrix .............6 diagonalizable...............8 dimension .....................6 dot product ...................8 eigenbasis ....................7 eigenspace...................7 eigenvalue ....................7 eigenvector...................7 geometric multiplicity....7 identity matrix ...............4 image ...........................6 inner product................9 inverse matrix...............5 inverse transformation..4 invertible.......................4 isomorphism.................4 kernal ...........................6 Laplace expansion by minors .....................8 linear independence.....6 linear transformation.....4 lower triangular.............6 norm .......................... 10 nullity............................ 8 orthogonal ................ 7, 9 orthogonal diagonalization ................................ 8 orthogonal projection.... 7 orthonormal.................. 7 orthonormal basis ........ 7 pivot columns............... 7 quadratic form.............. 9 rank.............................. 3 reduced row echelon form ................................ 3 reflection ...................... 8 row operations ............. 3 rref ................................3 similarity .......................8 simultaneous equations 1 singular.........................8 skew-symmetric............6 span .............................6...
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...2001 Submitted by Arun Avudainaygam LINEAR AND ADAPTIVE LINEAR MULTIUSER DETECTION IN CDMA SYSTEMS Project Website: http://arun-10.tripod.com/mud/mud.html SECTION 0 Introduction Multiuser detection is a technology that spawned in the early 80’s. It has now developed into an important, full-fledged field in multi-access communications. Multiuser Detection (MUD) is the intelligent estimation/demodulation of transmitted bits in the presence of Multiple Access Interference (MAI). MAI occurs in multi-access communication systems (CDMA/ TDMA/ FDMA) where simultaneously occurring digital streams of information interfere with each other. Conventional detectors based on the matched filter just treat the MAI as additive white gaussian noise (AWGN). However, unlike AWGN, MAI has a nice correlative structure that is quantified by the cross-correlation matrix of the signature sequences. Hence, detectors that take into account this correlation would perform better than the conventional matched filter-bank. MUD is basically the design of signal processing algorithms that run in the black box shown in figure 0.1. These algorithms take into account the correlative structure of the MAI. 0.1 Overview of the project This project investigates a couple of different approaches to linear multiuser detection in CDMA systems. Linear MUDs are detectors that operate linearly on the received signal statistic i.e., they perform only linear transformations on the received statistic....
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...TUTOR CLASS Content: Covered all chapter 3, 4, 5. No: * Linear Approximation * Linear programming * Leontief model Chapter 3: INTRODUCTION TO LINEAR EQUATIONS ANDMATRICES (Gaussian elimination) 1. Solve the following system x1-4x2+x3=5-2x1-5x2+3x3=33x1+2x2-x3=1 Using: A, Gauss elimination method B, Cramer’s rule. (chapter 4) Chapter 4: EIGENVECTORS AND EIGENVALUES (Determinants, Cramer’s rule, Matrix Inverse, Eigenvectors, Eigenvalues) 2. Let A b the matrix defined by A=3 4 -1 04 -1 0 3-1 1 2 3 A, Applying the row operation 4 R4+R2→R2 to A, what is the resulting matrix B we get? B, Use row operations to transform A to the form C where C=0 a b c0 d e f0 g h i-1 1 2 3 And where the letters a, b, c, d, e, f, g, h, g and I are the numbers that you have to find out (depending on your row operations). Find a relation between the determinant of A and determinant of C. C, Evaluate the determinant of A, detA, by using the properties of determinants and cofactor expansions along rows and columns of your choice. 3. ( Matrix Inverse) Recall that if a matrix A is invertible, then the unique solution of the linear system of equations AX= b is given by X= A-1b Given the linear system of equations x+2y-9z =1-2x-4y+19z=0-y+2z =1 A, Find the inverse of the matrix A of coefficient of the linear system above, where A= 1 2 -9-2 -4 190...
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