Linear Algebra in Numerical Methods. Lecture on linear algebra MATLAB/Octave works well with linear algebra
|
|
- David West
- 6 years ago
- Views:
Transcription
1 Linear Algebra in Numerical Methods Lecture on linear algebra MATLAB/Octave works well with linear algebra
2 Linear Algebra A pseudo-algebra that deals with a system of equations and the transformations of those equations (This) Linear algebra is technically not an algebra per definition Algebra (not linear algebra) studies vectors (and vector fields), matrices, tensors (and tensor fields), quaternions, abstract concepts like groups and rings, etc..; this sometimes is referred to as abstract algebra Elementary algebra is the algebra learned in secondary school. BTW, there is a linear algebra that is more akin to algebra, but it is not the linear algebra most people refer to
3 Linear Algebra A pseudo-algebra that deals with a system of equations and the transformations of those equations Fitting and smoothing (in a different lecture) is an application of this algebra Solving the system of equations is an application of this algebra Biggest issue is the inverse of a matrix Normally linear algebra works on solving the following equation problem and it's solution A x= y x= A 1 y
4 Linear Algebra This linear algebra method leads to transform
5 System of equations Linear system of equations Graphical solution (tough for big systems) Gauss method Use method similar to what we used for the simplex method Just a fancy method of solving linear equations by addition and substitution Non-linear system of equations (not linear algebra BTW) System of equations are equal to zero Use root finding method in multiple dimensions Newton-Raphson Set-up in matrix formation and use Gauss elimination Other similar but better methods exist
6 Eigen (own value) Eigenvalues: Characteristic roots (values) of a linear system of equations Eigenvectors: Vectors associated with a linear system of equations Eigenfunction: A function that is operated on by some operator and has associated eigenvalues from this operation is called an eigenfunction (in essence a transformed function that has eigenvalues from a particular operation). Solved using different decomposition methods
7 Decomposition There are a number of matrix decomposition methods Decomposes a matrix into simpler matrices in order to make it easier to do a time-consuming operation Eigen decomposition A matrix can be decomposed into a given a matrix of eigenvectors, V, and a diagonal matrix with eigenvalues on the diagonal, D, from matrix A Given that V is a square matrix then LU decomposition A=V DV 1 Works with square matrix Solves a linear equation QR decomposition Works with rectangular matrix Solves linear equations
8 Decomposition (cont.) Matrix decomposition methods Single Value Decomposition (SVD) Cholesky Decomposition Works with symmetric positive definite matrices Faster then LU decomposition if can be uses Schur Decomposition Works with complex square matrix Hessenberg Decomposition Decomposes into an Unitary matrix (U* ) T = U -1 {for real is the same as orthogonal} and Hessenberg Matrix {special matrix band and upper triangle not zero} For eigenvalues and eigenvectors many applications perform a Hessenberg Decomposition and then a Schur Decomposition
9 Matrix Definitions A matrix can have different operations done to it that are useful in linear algebra Transpose is when the the elements of a matrix are transposed B= A T Given A ij then B= A ji Adjugate (formally adjoint which is a conjugate transpose now) of a matrix is the transpose of the cofactor matrix of a matrix C ij =( 1) i+ j cofactor ( A ij ) adj ( A)=C T =C ji
10 Inverse of Matrix A matrix can have different operations done to it that are useful in linear algebra Inverse of a matrix is the matrix when multiplied by the original matrix equal to the identity matrix I = A 1 A Inverse is the adjugate divided by the determinant A 1 =adj ( A)/ det ( A) The inverse of an triangular matrix is triangular itself and lends itself to an easy equation form given the zeros on the other triangular half lots of cofactor determinants
11 LU Decomposition Reduces the time consuming forward elimination that is in Gauss elimination Decomposes matrix into lower (L) and upper (U) triangular matrices L is used to produce an intermediate vector through elimination U is used to produce the answer with the intermediate vector Many variation to improve this simple description Very useful for matrix inversion (a very time consuming task) LU reduction in computing is a parallelized version of LU decomposition Method is a modified Gaussian elimination called the Doolittle algorithm (except for LUP Crout algorithm)
12 LU Decomposition Applications First to solve a set of linear equations in two steps The advantage is it avoids Gaussian elimination (though to get the LU decomposition a similar process is uses; so this is only good if the A matrix is used multiple times) Problem and solutions follow A x=b L U x=b solve for L y=b that is y=l 1 b finally solve for U x= y that is x=u 1 y
13 LU Decomposition Applications Solves inverse and normally is how computer applications like MATLAB preform an inverse of a matrix, see example Solves determinants quickly, see example A 1 =U 1 L 1 det A =det L det U MATLAB det(a) det(l)*det(p*u) det(p*l)*det(u) MATLAB [l,u,p]=lu(a) inv(u)*inv(l)*p Octave [l,u]=lu(a) inv(u)*inv(l)=inv(a)
14 QR Decomposition Used to solve least squares problems Used to solve linear equations in the same manner as LU decomposition Can be used to get orthonormal basis of a set of vectors Octave [q,r]=qr(a) inv(r)*inv(q)=inv(a)
15 SVD Decomposition Decomposes a matrix into eigenvectors and eigenvalues (in essence really AA T and A T A) Can be done on any type of matrix (non-square, sparse, singular, large, etc.) More then one type of SVD Think of this method as factoring a matrix (normally into three simpler matrices) A=USV T The U and V represents the eigenvectors (AA T and A T A respectively) and S represents the square root of the eigenvalue The basis for Principal Component Analysis Used to reduce a complicated multi-variable data set to its principal components That is factor it using SVD Goal would be to reduce the dimensionality of space Also known as Karhunen-Loeve Transform (KLT), proper orthogonal decomposition (POD), empirical orthogonal functions (meteorology and geophysics), or Hotelling transform (in economics and imaging) with modifications to fit the field
16 SVD Decomposition Decomposes a matrix into eigenvectors and eigenvalues (in essence really AA T and A T A) The basis for Principal Component Analysis Scree test (graph the eigenvalues and keep the larger valued eigenvalue Use only the most important eigenvector and eigenvalues
17 Special Matrices Banded matrices Coefficients banded about the center LU decomposition no good Other methods Sparse matrix Very few coefficients scattered throughout Special methods Fast Handles big matrices Iterative elimination solutions Gauss-Seidel
18 Determinants and Tensors Determinants The sum of the signed permutations of a matrix Can be used to get an inverse of a matrix (and hence can be used to solve a system of equations using the cofactors) Cramer's method: usually taught in linear algebra in general doesn't work for large matrices so value is limited Tensors!!!!! (Won't review as we did this in EGR 1010) Tensor 0 th order: Scalar Tensor 1 st order: Vector Dot product Cross product Tensor 2 nd order and greater: Tensor Direct cross product Stresses, etc.
19 Spaces Vector fields A set of scalars in a region (say all the potentials in a square area) A set of vectors in a region Not necessarily a vector space Vector space A set of vectors with defined operations on them Subspace Could think of this as an object in programming languages Spaces are useful definitions for mathematicians A vector subspace is a subset of the vector space All vector spaces have at least two subspace in itself and an empty set These ideas should be fully developed in a good linear algebra class
20 Eigenmath A x= x Given a matrix A, x is defined as the eigenvector and lambda is the eigenvalues I A =0 I A x=0 Where det I A =0 is the characteristic equation of A. has a set of eigenvector for each eigenvalue that definethe eigenspace of A Note that for a triangular matrix and a diagonal matrix the eigenvalues of A are just the values of the diagonal
21 Linear Transformations Transformations are the basis for systems control descriptions Basically you change space from one space to another space Remember EGR 1010: Fourier transform changes your signal from time space to frequency space Remember EGR 1010: Laplace transform changes your signal from time space to s space A simple transform would be the rotation or reflection transform Say you have a vector that is (0,0 x 1,y 1 ) We can rotate this or reflect it with a very simple matrix using ones and negative ones Try it More complicated descriptions would involve spaces
22 Control Blocks General block for control Example for numerical derivative (think Taylor) Example of derivative (transfer function) General block for summing/multiplying, etc. Example combining block
23 Sampling In the digital world we need to take what would be an analog signal and sample it So if we take f(t) and sample it at equal spaces we will have a set of points f n were n goes from 1 to I (say) When taking these samples we only take a few digits which we round (as opposed to infinite precision which only works in a theoretical world). We refer to this as quantization of the signal. That is the rounding, not the sampling. There are obvious problems to this with regards to error We discussed the error in the numerical analysis portion of the class and will only briefly mention it here
24 Sampling This digitized sample is what we will transform In the numerical methods portion we already did some of these transformations though we didn't express it in the jargon of signals We can smooth or fit, etc. using filters This is akin to transforming the input A typical filter is the nonrecursive filter
25 Sampling Non-recursive filter ( transforming ) Filter coefficients are represented by c k (since these are constants this is a time-invariant filter) Types of nonrecursive filters Finite impulse response (FIR) filter Easier to implement than IIR filter (see later) This is the most general name: names below same thing Transversal filter Tapped delay line filter Moving average filter (common) u n = c k f n k k= Example : u n = 1 5 ( f n 2 + f n 1 + f n + f n+ 1 + f n+ 2 ) Example 2( flat top?): u n = 1 35 ( 3 f n f n f n + 12 f n+ 1 3 f n+ 2 )
26 Sampling Non-recursive filter ( transforming ) A set of coefficients multiply a strip of function points to create one point (u n ) To get the next point (u n+1 or u n-1 ) the coefficients shift and multiply the function points again This is referred to as a convolution u n =c 2 f n 2 c 1 f n 1 c 0 f n c 1 f n 1 c 2 f n 2 u n 1 =c 2 f n 1 c 1 f n c 0 f n 1 c 1 f n 2 c 2 f n 3
27 Sampling These filter can be known as windowing Rectangular function (first example) Bartlett window (Triangular function/window) Hann function (Hanning window) Bartlett-Hann window Hamming function/window Blackman function/window Lanczos function (Sinc window) Gaussian function/window Kaiser window (Bessel function/window) Tukey function/window
28 Sampling These filter can be known as windowing Cosine function/window Connes function/window Kaiser function/window Spencer window (usually used in accounting) Welch window (improvement of Bartlett method)
29 Sampling These filter can be known as windowing Nuttal window Blackman-Harris window Blackman-Nuttal window Poisson window Hann-Poisson window Rife-Vincent window (used for tones music) DPSS (Discrete prolate spheroidals sequences) window (Slepian window)
30 Sampling Recursive filter ( transforming ) Filter coefficients are represented by c k and d k Akin to feedback and feedforward system Types of recursive filters Infinite Impulse Response Filter (IIR) Ladder filter Lattice filter Wave Digital Filter (WDF) All coefficients are physical (spring, mass, damper or inductor, capacitor, resistor) Autoregressive (integrated) moving average filter (ARMA or ARIMA) u n = c k f n k d k u n k k= k = Example :u n =u n [ f n f n 1 ] Trapezoid rule
31 Sampling All the numerical analysis that we did previously can be recast as digital filters, this includes Fitting (least-squares and more) Smoothing Differences and derivatives Integration
32 Aliasing Problems in sampling Aliasing Different frequencies are found in another frequency (aliased) Seen typically in car wheel rotation when we see it slow down, stop, and maybe move backwards depending on your speed Sample a sine wave at different intervals...different frequency appear indistinguishable
33 Aliasing Problems in sampling Aliasing Different frequencies are found in another frequency (aliased) Seen typically in car wheel rotation when we see it slow down, stop, and maybe move backwards depending on your speed Sample a sine wave at different intervals...different frequency appear indistinguishable
EE731 Lecture Notes: Matrix Computations for Signal Processing
EE731 Lecture Notes: Matrix Computations for Signal Processing James P. Reilly c Department of Electrical and Computer Engineering McMaster University September 22, 2005 0 Preface This collection of ten
More informationMathematics. EC / EE / IN / ME / CE. for
Mathematics for EC / EE / IN / ME / CE By www.thegateacademy.com Syllabus Syllabus for Mathematics Linear Algebra: Matrix Algebra, Systems of Linear Equations, Eigenvalues and Eigenvectors. Probability
More informationConceptual Questions for Review
Conceptual Questions for Review Chapter 1 1.1 Which vectors are linear combinations of v = (3, 1) and w = (4, 3)? 1.2 Compare the dot product of v = (3, 1) and w = (4, 3) to the product of their lengths.
More informationNumerical Methods in Matrix Computations
Ake Bjorck Numerical Methods in Matrix Computations Springer Contents 1 Direct Methods for Linear Systems 1 1.1 Elements of Matrix Theory 1 1.1.1 Matrix Algebra 2 1.1.2 Vector Spaces 6 1.1.3 Submatrices
More informationMath 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination
Math 0, Winter 07 Final Exam Review Chapter. Matrices and Gaussian Elimination { x + x =,. Different forms of a system of linear equations. Example: The x + 4x = 4. [ ] [ ] [ ] vector form (or the column
More informationFINITE-DIMENSIONAL LINEAR ALGEBRA
DISCRETE MATHEMATICS AND ITS APPLICATIONS Series Editor KENNETH H ROSEN FINITE-DIMENSIONAL LINEAR ALGEBRA Mark S Gockenbach Michigan Technological University Houghton, USA CRC Press Taylor & Francis Croup
More informationMAC Module 3 Determinants. Learning Objectives. Upon completing this module, you should be able to:
MAC 2 Module Determinants Learning Objectives Upon completing this module, you should be able to:. Determine the minor, cofactor, and adjoint of a matrix. 2. Evaluate the determinant of a matrix by cofactor
More informationMATH 240 Spring, Chapter 1: Linear Equations and Matrices
MATH 240 Spring, 2006 Chapter Summaries for Kolman / Hill, Elementary Linear Algebra, 8th Ed. Sections 1.1 1.6, 2.1 2.2, 3.2 3.8, 4.3 4.5, 5.1 5.3, 5.5, 6.1 6.5, 7.1 7.2, 7.4 DEFINITIONS Chapter 1: Linear
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS SYSTEMS OF EQUATIONS AND MATRICES Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationStatistical and Adaptive Signal Processing
r Statistical and Adaptive Signal Processing Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing Dimitris G. Manolakis Massachusetts Institute of Technology Lincoln Laboratory
More informationPreliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012
Instructions Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012 The exam consists of four problems, each having multiple parts. You should attempt to solve all four problems. 1.
More informationMATH 583A REVIEW SESSION #1
MATH 583A REVIEW SESSION #1 BOJAN DURICKOVIC 1. Vector Spaces Very quick review of the basic linear algebra concepts (see any linear algebra textbook): (finite dimensional) vector space (or linear space),
More informationMobile Robotics 1. A Compact Course on Linear Algebra. Giorgio Grisetti
Mobile Robotics 1 A Compact Course on Linear Algebra Giorgio Grisetti SA-1 Vectors Arrays of numbers They represent a point in a n dimensional space 2 Vectors: Scalar Product Scalar-Vector Product Changes
More informationNumerical Linear Algebra
Numerical Linear Algebra The two principal problems in linear algebra are: Linear system Given an n n matrix A and an n-vector b, determine x IR n such that A x = b Eigenvalue problem Given an n n matrix
More informationNumerical Linear Algebra
Numerical Linear Algebra Direct Methods Philippe B. Laval KSU Fall 2017 Philippe B. Laval (KSU) Linear Systems: Direct Solution Methods Fall 2017 1 / 14 Introduction The solution of linear systems is one
More information1 9/5 Matrices, vectors, and their applications
1 9/5 Matrices, vectors, and their applications Algebra: study of objects and operations on them. Linear algebra: object: matrices and vectors. operations: addition, multiplication etc. Algorithms/Geometric
More informationFundamentals of Engineering Analysis (650163)
Philadelphia University Faculty of Engineering Communications and Electronics Engineering Fundamentals of Engineering Analysis (6563) Part Dr. Omar R Daoud Matrices: Introduction DEFINITION A matrix is
More informationGAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511)
GAUSSIAN ELIMINATION AND LU DECOMPOSITION (SUPPLEMENT FOR MA511) D. ARAPURA Gaussian elimination is the go to method for all basic linear classes including this one. We go summarize the main ideas. 1.
More informationMAA507, Power method, QR-method and sparse matrix representation.
,, and representation. February 11, 2014 Lecture 7: Overview, Today we will look at:.. If time: A look at representation and fill in. Why do we need numerical s? I think everyone have seen how time consuming
More informationAdaptive Filtering. Squares. Alexander D. Poularikas. Fundamentals of. Least Mean. with MATLABR. University of Alabama, Huntsville, AL.
Adaptive Filtering Fundamentals of Least Mean Squares with MATLABR Alexander D. Poularikas University of Alabama, Huntsville, AL CRC Press Taylor & Francis Croup Boca Raton London New York CRC Press is
More informationApplied Linear Algebra
Applied Linear Algebra Peter J. Olver School of Mathematics University of Minnesota Minneapolis, MN 55455 olver@math.umn.edu http://www.math.umn.edu/ olver Chehrzad Shakiban Department of Mathematics University
More informationMatrix Factorization and Analysis
Chapter 7 Matrix Factorization and Analysis Matrix factorizations are an important part of the practice and analysis of signal processing. They are at the heart of many signal-processing algorithms. Their
More informationReview problems for MA 54, Fall 2004.
Review problems for MA 54, Fall 2004. Below are the review problems for the final. They are mostly homework problems, or very similar. If you are comfortable doing these problems, you should be fine on
More informationIterative Methods. Splitting Methods
Iterative Methods Splitting Methods 1 Direct Methods Solving Ax = b using direct methods. Gaussian elimination (using LU decomposition) Variants of LU, including Crout and Doolittle Other decomposition
More information5.6. PSEUDOINVERSES 101. A H w.
5.6. PSEUDOINVERSES 0 Corollary 5.6.4. If A is a matrix such that A H A is invertible, then the least-squares solution to Av = w is v = A H A ) A H w. The matrix A H A ) A H is the left inverse of A and
More informationNumerical Linear Algebra
Numerical Linear Algebra By: David McQuilling; Jesus Caban Deng Li Jan.,31,006 CS51 Solving Linear Equations u + v = 8 4u + 9v = 1 A x b 4 9 u v = 8 1 Gaussian Elimination Start with the matrix representation
More informationFinal Review Sheet. B = (1, 1 + 3x, 1 + x 2 ) then 2 + 3x + 6x 2
Final Review Sheet The final will cover Sections Chapters 1,2,3 and 4, as well as sections 5.1-5.4, 6.1-6.2 and 7.1-7.3 from chapters 5,6 and 7. This is essentially all material covered this term. Watch
More information5.7 Cramer's Rule 1. Using Determinants to Solve Systems Assumes the system of two equations in two unknowns
5.7 Cramer's Rule 1. Using Determinants to Solve Systems Assumes the system of two equations in two unknowns (1) possesses the solution and provided that.. The numerators and denominators are recognized
More informationPractical Linear Algebra: A Geometry Toolbox
Practical Linear Algebra: A Geometry Toolbox Third edition Chapter 12: Gauss for Linear Systems Gerald Farin & Dianne Hansford CRC Press, Taylor & Francis Group, An A K Peters Book www.farinhansford.com/books/pla
More informationMTH Linear Algebra. Study Guide. Dr. Tony Yee Department of Mathematics and Information Technology The Hong Kong Institute of Education
MTH 3 Linear Algebra Study Guide Dr. Tony Yee Department of Mathematics and Information Technology The Hong Kong Institute of Education June 3, ii Contents Table of Contents iii Matrix Algebra. Real Life
More informationA Review of Matrix Analysis
Matrix Notation Part Matrix Operations Matrices are simply rectangular arrays of quantities Each quantity in the array is called an element of the matrix and an element can be either a numerical value
More informationSection 4.5 Eigenvalues of Symmetric Tridiagonal Matrices
Section 4.5 Eigenvalues of Symmetric Tridiagonal Matrices Key Terms Symmetric matrix Tridiagonal matrix Orthogonal matrix QR-factorization Rotation matrices (plane rotations) Eigenvalues We will now complete
More informationG1110 & 852G1 Numerical Linear Algebra
The University of Sussex Department of Mathematics G & 85G Numerical Linear Algebra Lecture Notes Autumn Term Kerstin Hesse (w aw S w a w w (w aw H(wa = (w aw + w Figure : Geometric explanation of the
More informationMath 307 Learning Goals
Math 307 Learning Goals May 14, 2018 Chapter 1 Linear Equations 1.1 Solving Linear Equations Write a system of linear equations using matrix notation. Use Gaussian elimination to bring a system of linear
More informationLinear Algebra in Actuarial Science: Slides to the lecture
Linear Algebra in Actuarial Science: Slides to the lecture Fall Semester 2010/2011 Linear Algebra is a Tool-Box Linear Equation Systems Discretization of differential equations: solving linear equations
More information1 Matrices and Systems of Linear Equations. a 1n a 2n
March 31, 2013 16-1 16. Systems of Linear Equations 1 Matrices and Systems of Linear Equations An m n matrix is an array A = (a ij ) of the form a 11 a 21 a m1 a 1n a 2n... a mn where each a ij is a real
More informationLinear Least-Squares Data Fitting
CHAPTER 6 Linear Least-Squares Data Fitting 61 Introduction Recall that in chapter 3 we were discussing linear systems of equations, written in shorthand in the form Ax = b In chapter 3, we just considered
More informationStatistical Geometry Processing Winter Semester 2011/2012
Statistical Geometry Processing Winter Semester 2011/2012 Linear Algebra, Function Spaces & Inverse Problems Vector and Function Spaces 3 Vectors vectors are arrows in space classically: 2 or 3 dim. Euclidian
More informationToday s class. Linear Algebraic Equations LU Decomposition. Numerical Methods, Fall 2011 Lecture 8. Prof. Jinbo Bi CSE, UConn
Today s class Linear Algebraic Equations LU Decomposition 1 Linear Algebraic Equations Gaussian Elimination works well for solving linear systems of the form: AX = B What if you have to solve the linear
More information1 Number Systems and Errors 1
Contents 1 Number Systems and Errors 1 1.1 Introduction................................ 1 1.2 Number Representation and Base of Numbers............. 1 1.2.1 Normalized Floating-point Representation...........
More informationLinear Algebra Primer
Linear Algebra Primer David Doria daviddoria@gmail.com Wednesday 3 rd December, 2008 Contents Why is it called Linear Algebra? 4 2 What is a Matrix? 4 2. Input and Output.....................................
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 1: Course Overview & Matrix-Vector Multiplication Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 20 Outline 1 Course
More informationMA3025 Course Prerequisites
MA3025 Course Prerequisites MA 3025 (4-1) MA3025 (4-1) Logic and Discrete Mathematics: Provides a rigorous foundation in logic and elementary discrete mathematics. Topics from logic include modeling English
More informationLinear Algebra Highlights
Linear Algebra Highlights Chapter 1 A linear equation in n variables is of the form a 1 x 1 + a 2 x 2 + + a n x n. We can have m equations in n variables, a system of linear equations, which we want to
More informationIntroduction to Mathematical Programming
Introduction to Mathematical Programming Ming Zhong Lecture 6 September 12, 2018 Ming Zhong (JHU) AMS Fall 2018 1 / 20 Table of Contents 1 Ming Zhong (JHU) AMS Fall 2018 2 / 20 Solving Linear Systems A
More information1 Singular Value Decomposition and Principal Component
Singular Value Decomposition and Principal Component Analysis In these lectures we discuss the SVD and the PCA, two of the most widely used tools in machine learning. Principal Component Analysis (PCA)
More informationLINEAR ALGEBRA: NUMERICAL METHODS. Version: August 12,
LINEAR ALGEBRA: NUMERICAL METHODS. Version: August 12, 2000 74 6 Summary Here we summarize the most important information about theoretical and numerical linear algebra. MORALS OF THE STORY: I. Theoretically
More informationLinear Algebra & Geometry why is linear algebra useful in computer vision?
Linear Algebra & Geometry why is linear algebra useful in computer vision? References: -Any book on linear algebra! -[HZ] chapters 2, 4 Some of the slides in this lecture are courtesy to Prof. Octavia
More informationGATE Engineering Mathematics SAMPLE STUDY MATERIAL. Postal Correspondence Course GATE. Engineering. Mathematics GATE ENGINEERING MATHEMATICS
SAMPLE STUDY MATERIAL Postal Correspondence Course GATE Engineering Mathematics GATE ENGINEERING MATHEMATICS ENGINEERING MATHEMATICS GATE Syllabus CIVIL ENGINEERING CE CHEMICAL ENGINEERING CH MECHANICAL
More informationLecture 2: Linear Algebra Review
EE 227A: Convex Optimization and Applications January 19 Lecture 2: Linear Algebra Review Lecturer: Mert Pilanci Reading assignment: Appendix C of BV. Sections 2-6 of the web textbook 1 2.1 Vectors 2.1.1
More information(Refer Slide Time: )
Digital Signal Processing Prof. S. C. Dutta Roy Department of Electrical Engineering Indian Institute of Technology, Delhi FIR Lattice Synthesis Lecture - 32 This is the 32nd lecture and our topic for
More informationB553 Lecture 5: Matrix Algebra Review
B553 Lecture 5: Matrix Algebra Review Kris Hauser January 19, 2012 We have seen in prior lectures how vectors represent points in R n and gradients of functions. Matrices represent linear transformations
More information1 Matrices and Systems of Linear Equations
March 3, 203 6-6. Systems of Linear Equations Matrices and Systems of Linear Equations An m n matrix is an array A = a ij of the form a a n a 2 a 2n... a m a mn where each a ij is a real or complex number.
More informationEC5555 Economics Masters Refresher Course in Mathematics September 2014
EC5555 Economics Masters Refresher Course in Mathematics September 4 Lecture Matri Inversion and Linear Equations Ramakanta Patra Learning objectives. Matri inversion Matri inversion and linear equations
More informationNumerical Methods - Numerical Linear Algebra
Numerical Methods - Numerical Linear Algebra Y. K. Goh Universiti Tunku Abdul Rahman 2013 Y. K. Goh (UTAR) Numerical Methods - Numerical Linear Algebra I 2013 1 / 62 Outline 1 Motivation 2 Solving Linear
More informationMath 307 Learning Goals. March 23, 2010
Math 307 Learning Goals March 23, 2010 Course Description The course presents core concepts of linear algebra by focusing on applications in Science and Engineering. Examples of applications from recent
More informationLINEAR ALGEBRA KNOWLEDGE SURVEY
LINEAR ALGEBRA KNOWLEDGE SURVEY Instructions: This is a Knowledge Survey. For this assignment, I am only interested in your level of confidence about your ability to do the tasks on the following pages.
More informationFoundations of Matrix Analysis
1 Foundations of Matrix Analysis In this chapter we recall the basic elements of linear algebra which will be employed in the remainder of the text For most of the proofs as well as for the details, the
More informationMATHEMATICS FOR COMPUTER VISION WEEK 2 LINEAR SYSTEMS. Dr Fabio Cuzzolin MSc in Computer Vision Oxford Brookes University Year
1 MATHEMATICS FOR COMPUTER VISION WEEK 2 LINEAR SYSTEMS Dr Fabio Cuzzolin MSc in Computer Vision Oxford Brookes University Year 2013-14 OUTLINE OF WEEK 2 Linear Systems and solutions Systems of linear
More informationLecture 11. Linear systems: Cholesky method. Eigensystems: Terminology. Jacobi transformations QR transformation
Lecture Cholesky method QR decomposition Terminology Linear systems: Eigensystems: Jacobi transformations QR transformation Cholesky method: For a symmetric positive definite matrix, one can do an LU decomposition
More informationHands-on Matrix Algebra Using R
Preface vii 1. R Preliminaries 1 1.1 Matrix Defined, Deeper Understanding Using Software.. 1 1.2 Introduction, Why R?.................... 2 1.3 Obtaining R.......................... 4 1.4 Reference Manuals
More informationIn this section again we shall assume that the matrix A is m m, real and symmetric.
84 3. The QR algorithm without shifts See Chapter 28 of the textbook In this section again we shall assume that the matrix A is m m, real and symmetric. 3.1. Simultaneous Iterations algorithm Suppose we
More informationSolution of Linear systems
Solution of Linear systems Direct Methods Indirect Methods -Elimination Methods -Inverse of a matrix -Cramer s Rule -LU Decomposition Iterative Methods 2 A x = y Works better for coefficient matrices with
More informationComputational Data Analysis!
12.714 Computational Data Analysis! Alan Chave (alan@whoi.edu)! Thomas Herring (tah@mit.edu),! http://geoweb.mit.edu/~tah/12.714! Concentration Problem:! Today s class! Signals that are near time and band
More informationANSWERS (5 points) Let A be a 2 2 matrix such that A =. Compute A. 2
MATH 7- Final Exam Sample Problems Spring 7 ANSWERS ) ) ). 5 points) Let A be a matrix such that A =. Compute A. ) A = A ) = ) = ). 5 points) State ) the definition of norm, ) the Cauchy-Schwartz inequality
More informationIntroduction to Mobile Robotics Compact Course on Linear Algebra. Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz
Introduction to Mobile Robotics Compact Course on Linear Algebra Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz Vectors Arrays of numbers Vectors represent a point in a n dimensional space
More informationMain matrix factorizations
Main matrix factorizations A P L U P permutation matrix, L lower triangular, U upper triangular Key use: Solve square linear system Ax b. A Q R Q unitary, R upper triangular Key use: Solve square or overdetrmined
More informationGlossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB
Glossary of Linear Algebra Terms Basis (for a subspace) A linearly independent set of vectors that spans the space Basic Variable A variable in a linear system that corresponds to a pivot column in the
More informationLinear Algebra: Matrix Eigenvalue Problems
CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given
More informationReview of matrices. Let m, n IN. A rectangle of numbers written like A =
Review of matrices Let m, n IN. A rectangle of numbers written like a 11 a 12... a 1n a 21 a 22... a 2n A =...... a m1 a m2... a mn where each a ij IR is called a matrix with m rows and n columns or an
More informationANSWERS. E k E 2 E 1 A = B
MATH 7- Final Exam Spring ANSWERS Essay Questions points Define an Elementary Matrix Display the fundamental matrix multiply equation which summarizes a sequence of swap, combination and multiply operations,
More informationMath 4A Notes. Written by Victoria Kala Last updated June 11, 2017
Math 4A Notes Written by Victoria Kala vtkala@math.ucsb.edu Last updated June 11, 2017 Systems of Linear Equations A linear equation is an equation that can be written in the form a 1 x 1 + a 2 x 2 +...
More informationAPPLIED NUMERICAL LINEAR ALGEBRA
APPLIED NUMERICAL LINEAR ALGEBRA James W. Demmel University of California Berkeley, California Society for Industrial and Applied Mathematics Philadelphia Contents Preface 1 Introduction 1 1.1 Basic Notation
More informationLinear Algebra Review
Chapter 1 Linear Algebra Review It is assumed that you have had a course in linear algebra, and are familiar with matrix multiplication, eigenvectors, etc. I will review some of these terms here, but quite
More informationNext topics: Solving systems of linear equations
Next topics: Solving systems of linear equations 1 Gaussian elimination (today) 2 Gaussian elimination with partial pivoting (Week 9) 3 The method of LU-decomposition (Week 10) 4 Iterative techniques:
More informationNUMERICAL COMPUTATION IN SCIENCE AND ENGINEERING
NUMERICAL COMPUTATION IN SCIENCE AND ENGINEERING C. Pozrikidis University of California, San Diego New York Oxford OXFORD UNIVERSITY PRESS 1998 CONTENTS Preface ix Pseudocode Language Commands xi 1 Numerical
More informationTEACHING NUMERICAL LINEAR ALGEBRA AT THE UNDERGRADUATE LEVEL by Biswa Nath Datta Department of Mathematical Sciences Northern Illinois University
TEACHING NUMERICAL LINEAR ALGEBRA AT THE UNDERGRADUATE LEVEL by Biswa Nath Datta Department of Mathematical Sciences Northern Illinois University DeKalb, IL 60115 E-mail: dattab@math.niu.edu What is Numerical
More informationCourse Name: Digital Signal Processing Course Code: EE 605A Credit: 3
Course Name: Digital Signal Processing Course Code: EE 605A Credit: 3 Prerequisites: Sl. No. Subject Description Level of Study 01 Mathematics Fourier Transform, Laplace Transform 1 st Sem, 2 nd Sem 02
More informationGeometric Modeling Summer Semester 2010 Mathematical Tools (1)
Geometric Modeling Summer Semester 2010 Mathematical Tools (1) Recap: Linear Algebra Today... Topics: Mathematical Background Linear algebra Analysis & differential geometry Numerical techniques Geometric
More informationMatrix decompositions
Matrix decompositions Zdeněk Dvořák May 19, 2015 Lemma 1 (Schur decomposition). If A is a symmetric real matrix, then there exists an orthogonal matrix Q and a diagonal matrix D such that A = QDQ T. The
More informationMATRICES. knowledge on matrices Knowledge on matrix operations. Matrix as a tool of solving linear equations with two or three unknowns.
MATRICES After studying this chapter you will acquire the skills in knowledge on matrices Knowledge on matrix operations. Matrix as a tool of solving linear equations with two or three unknowns. List of
More informationLinear Algebra. Matrices Operations. Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0.
Matrices Operations Linear Algebra Consider, for example, a system of equations such as x + 2y z + 4w = 0, 3x 4y + 2z 6w = 0, x 3y 2z + w = 0 The rectangular array 1 2 1 4 3 4 2 6 1 3 2 1 in which the
More informationQuiz ) Locate your 1 st order neighbors. 1) Simplify. Name Hometown. Name Hometown. Name Hometown.
Quiz 1) Simplify 9999 999 9999 998 9999 998 2) Locate your 1 st order neighbors Name Hometown Me Name Hometown Name Hometown Name Hometown Solving Linear Algebraic Equa3ons Basic Concepts Here only real
More informationElementary Linear Algebra
Matrices J MUSCAT Elementary Linear Algebra Matrices Definition Dr J Muscat 2002 A matrix is a rectangular array of numbers, arranged in rows and columns a a 2 a 3 a n a 2 a 22 a 23 a 2n A = a m a mn We
More informationLinear Algebraic Equations
Linear Algebraic Equations 1 Fundamentals Consider the set of linear algebraic equations n a ij x i b i represented by Ax b j with [A b ] [A b] and (1a) r(a) rank of A (1b) Then Axb has a solution iff
More informationNumerical Linear Algebra
Chapter 3 Numerical Linear Algebra We review some techniques used to solve Ax = b where A is an n n matrix, and x and b are n 1 vectors (column vectors). We then review eigenvalues and eigenvectors and
More informationComputational Methods CMSC/AMSC/MAPL 460. Eigenvalues and Eigenvectors. Ramani Duraiswami, Dept. of Computer Science
Computational Methods CMSC/AMSC/MAPL 460 Eigenvalues and Eigenvectors Ramani Duraiswami, Dept. of Computer Science Eigen Values of a Matrix Recap: A N N matrix A has an eigenvector x (non-zero) with corresponding
More informationQueens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane.
Queens College, CUNY, Department of Computer Science Numerical Methods CSCI 361 / 761 Spring 2018 Instructor: Dr. Sateesh Mane c Sateesh R. Mane 2018 8 Lecture 8 8.1 Matrices July 22, 2018 We shall study
More informationWe use the overhead arrow to denote a column vector, i.e., a number with a direction. For example, in three-space, we write
1 MATH FACTS 11 Vectors 111 Definition We use the overhead arrow to denote a column vector, ie, a number with a direction For example, in three-space, we write The elements of a vector have a graphical
More informationMaths for Signals and Systems Linear Algebra in Engineering
Maths for Signals and Systems Linear Algebra in Engineering Lectures 13 15, Tuesday 8 th and Friday 11 th November 016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE
More informationLecture 3: QR-Factorization
Lecture 3: QR-Factorization This lecture introduces the Gram Schmidt orthonormalization process and the associated QR-factorization of matrices It also outlines some applications of this factorization
More informationEngineering Computation
Engineering Computation Systems of Linear Equations_1 1 Learning Objectives for Lecture 1. Motivate Study of Systems of Equations and particularly Systems of Linear Equations. Review steps of Gaussian
More informationNotes on Eigenvalues, Singular Values and QR
Notes on Eigenvalues, Singular Values and QR Michael Overton, Numerical Computing, Spring 2017 March 30, 2017 1 Eigenvalues Everyone who has studied linear algebra knows the definition: given a square
More informationKarhunen-Loève Transform KLT. JanKees van der Poel D.Sc. Student, Mechanical Engineering
Karhunen-Loève Transform KLT JanKees van der Poel D.Sc. Student, Mechanical Engineering Karhunen-Loève Transform Has many names cited in literature: Karhunen-Loève Transform (KLT); Karhunen-Loève Decomposition
More information. =. a i1 x 1 + a i2 x 2 + a in x n = b i. a 11 a 12 a 1n a 21 a 22 a 1n. i1 a i2 a in
Vectors and Matrices Continued Remember that our goal is to write a system of algebraic equations as a matrix equation. Suppose we have the n linear algebraic equations a x + a 2 x 2 + a n x n = b a 2
More informationBackground Mathematics (2/2) 1. David Barber
Background Mathematics (2/2) 1 David Barber University College London Modified by Samson Cheung (sccheung@ieee.org) 1 These slides accompany the book Bayesian Reasoning and Machine Learning. The book and
More informationENGI 9420 Lecture Notes 2 - Matrix Algebra Page Matrix operations can render the solution of a linear system much more efficient.
ENGI 940 Lecture Notes - Matrix Algebra Page.0. Matrix Algebra A linear system of m equations in n unknowns, a x + a x + + a x b (where the a ij and i n n a x + a x + + a x b n n a x + a x + + a x b m
More informationMATHEMATICS. Course Syllabus. Section A: Linear Algebra. Subject Code: MA. Course Structure. Ordinary Differential Equations
MATHEMATICS Subject Code: MA Course Structure Sections/Units Section A Section B Section C Linear Algebra Complex Analysis Real Analysis Topics Section D Section E Section F Section G Section H Section
More informationMATRICES ARE SIMILAR TO TRIANGULAR MATRICES
MATRICES ARE SIMILAR TO TRIANGULAR MATRICES 1 Complex matrices Recall that the complex numbers are given by a + ib where a and b are real and i is the imaginary unity, ie, i 2 = 1 In what we describe below,
More information22.3. Repeated Eigenvalues and Symmetric Matrices. Introduction. Prerequisites. Learning Outcomes
Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one
More information