Class 27: Question 1 TRUE or FALSE: If P is a projection matrix of the form P=A(A T A) -1 A T then P is a symmetric matrix. 1. TRUE 2. FALSE.

Slides:



Advertisements
Similar presentations
Rules of Matrix Arithmetic
Advertisements

Chapter 28 – Part II Matrix Operations. Gaussian elimination Gaussian elimination LU factorization LU factorization Gaussian elimination with partial.
Which augmented matrix represents the following system of equations?
Y.M. Hu, Assistant Professor, Department of Applied Physics, National University of Kaohsiung Matrix – Basic Definitions Chapter 3 Systems of Differential.
6.1 Eigenvalues and Diagonalization. Definitions A is n x n. is an eigenvalue of A if AX = X has non zero solutions X (called eigenvectors) If is an eigenvalue.
Chap. 3 Determinants 3.1 The Determinants of a Matrix
Section 1.7 Diagonal, Triangular, and Symmetric Matrices.
1.7 Diagonal, Triangular, and Symmetric Matrices.
Mathematics. Matrices and Determinants-1 Session.
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Class 25: Question 1 Which of the following vectors is orthogonal to the row space of A?
Section 9.6 Determinants and Inverses Objectives To understand how to find a determinant of a 2x2 matrix. To understand the identity matrix. Do define.
Boot Camp in Linear Algebra Joel Barajas Karla L Caballero University of California Silicon Valley Center October 8th, 2008.
Matrices CS485/685 Computer Vision Dr. George Bebis.
5.1 Orthogonality.
1 Statistical Analysis Professor Lynne Stokes Department of Statistical Science Lecture 5QF Introduction to Vector and Matrix Operations Needed for the.
1.7 Diagonal, Triangular, and Symmetric Matrices 1.
Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka Virginia de Sa (UCSD) Cogsci 108F Linear.
Linear Algebra With Applications by Otto Bretscher. Page The Determinant of any diagonal nxn matrix is the product of its diagonal entries. True.
Orthogonal Matrices and Spectral Representation In Section 4.3 we saw that n  n matrix A was similar to a diagonal matrix if and only if it had n linearly.
Compiled By Raj G. Tiwari
Linear Algebra Review 1 CS479/679 Pattern Recognition Dr. George Bebis.
8.1 Vector spaces A set of vector is said to form a linear vector space V Chapter 8 Matrices and vector spaces.
1 MAC 2103 Module 12 Eigenvalues and Eigenvectors.
1.4 Inverses; Rules of Matrix Arithmetic. Properties of Matrix Operations For real numbers a and b,we always have ab=ba, which is called the commutative.
F UNDAMENTALS OF E NGINEERING A NALYSIS Eng. Hassan S. Migdadi Inverse of Matrix. Gauss-Jordan Elimination Part 1.
Day 1 Eigenvalues and Eigenvectors
Day 1 Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Matrices. A matrix, A, is a rectangular collection of numbers. A matrix with “m” rows and “n” columns is said to have order m x n. Each entry, or element,
Section 1.5 Elementary Matrices and a Method for Finding A −1.
Inverse and Identity Matrices Can only be used for square matrices. (2x2, 3x3, etc.)
13.1 Matrices and Their Sums
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 02 Chapter 2: Determinants.
What is the determinant of What is the determinant of
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 07 Chapter 7: Eigenvalues, Eigenvectors.
Class 26: Question 1 1.An orthogonal basis for A 2.An orthogonal basis for the column space of A 3.An orthogonal basis for the row space of A 4.An orthogonal.
Class 24: Question 1 Which of the following set of vectors is not an orthogonal set?
Review of Linear Algebra Optimization 1/16/08 Recitation Joseph Bradley.
5.1 Eigenvalues and Eigenvectors
Matrices and Determinants
MATRICES Operations with Matrices Properties of Matrix Operations
Similar diagonalization of real symmetric matrix
2.5 – Determinants and Multiplicative Inverses of Matrices.
A function is a rule f that associates with each element in a set A one and only one element in a set B. If f associates the element b with the element.
Section 2.1 Determinants by Cofactor Expansion. THE DETERMINANT Recall from algebra, that the function f (x) = x 2 is a function from the real numbers.
Boot Camp in Linear Algebra TIM 209 Prof. Ram Akella.
Section 6-2: Matrix Multiplication, Inverses and Determinants There are three basic matrix operations. 1.Matrix Addition 2.Scalar Multiplication 3.Matrix.
MATRICES A rectangular arrangement of elements is called matrix. Types of matrices: Null matrix: A matrix whose all elements are zero is called a null.
Final Outline Shang-Hua Teng. Problem 1: Multiple Choices 16 points There might be more than one correct answers; So you should try to mark them all.
Review of Eigenvectors and Eigenvalues from CliffsNotes Online mining-the-Eigenvectors-of-a- Matrix.topicArticleId-20807,articleId-
Linear Algebra With Applications by Otto Bretscher.
CS479/679 Pattern Recognition Dr. George Bebis
Warm-up Problem Use the Laplace transform to solve the IVP.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Rules of Matrix Arithmetic
Matrices and vector spaces
Evaluating Determinants by Row Reduction
Euclidean Inner Product on Rn
CS485/685 Computer Vision Dr. George Bebis
6-4 Symmetric Matrices By毛.
Derivative of scalar forms
MATRICES Operations with Matrices Properties of Matrix Operations
Eigen Decomposition Based on the slides by Mani Thomas
Elementary Linear Algebra Anton & Rorres, 9th Edition
Inverse Matrices and Systems
Subject :- Applied Mathematics
Matrices - Operations INVERSE OF A MATRIX
Linear Algebra: Matrix Eigenvalue Problems – Part 2
Presentation transcript:

Class 27: Question 1 TRUE or FALSE: If P is a projection matrix of the form P=A(A T A) -1 A T then P is a symmetric matrix. 1. TRUE 2. FALSE

Class 27: Answer 1: (A) The question is asking whether P T =P and the answer is yes. P=A(A T A) -1 A T P T =[A(A T A) -1 A T ] T P T =[A T ] T [(A T A) -1 ] T [A] T P T =A[(A T A) T ] -1 A T P T =A[A T A] -1 A T =P

Class 27: Question 2 TRUE or FALSE: If P is a projection matrix of the form P=A(A T A) -1 A T then P is an invertible matrix. 1. TRUE 2. FALSE

Class 26: Answer 2: (B) What’s the determinant of a permutation matrix? Well, if det(A) exists and is not zero then: But since A does not even have to be square (although P always is) P does NOT have to always invertible. The answer is FALSE.

Class 27: Question 3 TRUE or FALSE: If A is a symmetric matrix then A -1 =A T. 1. TRUE 2. FALSE

Class 27: Answer 3: (B) If a matrix is symmetric its transpose equals itself. This does not mean its transpose equals its inverse (that’s true for square orthogonal matrices). There’s nothing that says a symmetric matrix even needs to be invertible, i.e.

Class 27: Question 4

Class 27: Answer 4: (E) All four statements are true. Statement 1 follows from Theorem (Symmetric matrices have real eigenvalues) Statement 2 follows because (A -1 ) T =(A T ) -1 =A -1 which means the inverse matrix A -1 is symmetric, also. Statement 3 follows from Theorem (Eigenvectors corresponding to different eigenvalues of symmetric matrices are orthogonal to each other) Statement 4 follows from Statement 3