Symmetric and Skew Symmetric

Slides:



Advertisements
Similar presentations
Vector Spaces A set V is called a vector space over a set K denoted V(K) if is an Abelian group, is a field, and For every element vV and K there exists.
Advertisements

10.4 Complex Vector Spaces.
An Introduction. Any number of the form x + iy where x,y are Real and i=-1, i.e., i 2 = -1 is called a complex number. For example, 7 + i10, -5 -4i are.
Chapter Matrices Matrix Arithmetic
Scientific Computing QR Factorization Part 2 – Algorithm to Find Eigenvalues.
Matrix Definition: An array of numbers in m rows and n colums is called an mxn matrix A square matrix of order n, is an (nxn) matrix.
Matrix Theory Background
Mathematics. Matrices and Determinants-1 Session.
MATRICES. EXAMPLES:
Autar Kaw Humberto Isaza Transforming Numerical Methods Education for STEM Undergraduates.
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Symmetric Matrices and Quadratic Forms
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.
Matrix Algebra THE INVERSE OF A MATRIX © 2012 Pearson Education, Inc.
MATRICES. Matrices A matrix is a rectangular array of objects (usually numbers) arranged in m horizontal rows and n vertical columns. A matrix with m.
Table of Contents Matrices - Inverse of a 2  2 Matrix To find the inverse of a 2  2 matrix, use the following pattern. Let matrix A be given by... Then.
FactorsGraphsComplexityMisc Series and Sequences.
Completing the square Solving quadratic equations 1. Express the followings in completed square form and hence solve the equations x 2 + 4x – 12 = 0 (x.
Mathematics.
 Row and Reduced Row Echelon  Elementary Matrices.
Discrete Mathematics, 1st Edition Kevin Ferland
Gram-Schmidt Orthogonalization
CALCULUS – II Inverse Matrix by Dr. Eman Saad & Dr. Shorouk Ossama.
Matrices & Determinants Chapter: 1 Matrices & Determinants.
1 © 2010 Pearson Education, Inc. All rights reserved © 2010 Pearson Education, Inc. All rights reserved Chapter 9 Matrices and Determinants.
Matrices CHAPTER 8.1 ~ 8.8. Ch _2 Contents  8.1 Matrix Algebra 8.1 Matrix Algebra  8.2 Systems of Linear Algebra Equations 8.2 Systems of Linear.
Algebra 3: Section 5.5 Objectives of this Section Find the Sum and Difference of Two Matrices Find Scalar Multiples of a Matrix Find the Product of Two.
Matrices Matrices For grade 1, undergraduate students For grade 1, undergraduate students Made by Department of Math.,Anqing Teachers college.
Linear Algebra 1.Basic concepts 2.Matrix operations.
Computing Eigen Information for Small Matrices The eigen equation can be rearranged as follows: Ax = x  Ax = I n x  Ax - I n x = 0  (A - I n )x = 0.
5 5.2 © 2012 Pearson Education, Inc. Eigenvalues and Eigenvectors THE CHARACTERISTIC EQUATION.
Chapter 5 Eigenvalues and Eigenvectors 大葉大學 資訊工程系 黃鈴玲 Linear Algebra.
Mathematics.
1 Some Inequalities on Weighted Vertex Degrees, Eigenvalues, and Laplacian Eigenvalues of Weighted Graphs Behzad Torkian Dept. of Mathematical Sciences.
Elementary Linear Algebra Anton & Rorres, 9 th Edition Lecture Set – 07 Chapter 7: Eigenvalues, Eigenvectors.
CSCI 171 Presentation 9 Matrix Theory. Matrix – Rectangular array –i th row, j th column, i,j element –Square matrix, diagonal –Diagonal matrix –Equality.
Chapter 2 … part1 Matrices Linear Algebra S 1. Ch2_2 2.1 Addition, Scalar Multiplication, and Multiplication of Matrices Definition A matrix is a rectangular.
LESSON 79 – EXPONENTIAL FORMS OF COMPLEX NUMBERS HL2 Math - Santowski.
Chapter 2 Determinants. With each square matrix it is possible to associate a real number called the determinant of the matrix. The value of this number.
CHARACTERIZATIONS OF INVERTIBLE MATRICES
By Josh Zimmer Department of Mathematics and Computer Science The set ℤ p = {0,1,...,p-1} forms a finite field. There are p ⁴ possible 2×2 matrices in.
Linear Algebra Chapter 2 Matrices.
2.5 – Determinants and Multiplicative Inverses of Matrices.
7 7.2 © 2016 Pearson Education, Ltd. Symmetric Matrices and Quadratic Forms QUADRATIC FORMS.
Chapter 5 Chapter Content 1. Real Vector Spaces 2. Subspaces 3. Linear Independence 4. Basis and Dimension 5. Row Space, Column Space, and Nullspace 6.
Operations of Functions Given two functions  and g, then for all values of x for which both  (x) and g (x) are defined, the functions  + g,
Matrices CHAPTER 8.9 ~ Ch _2 Contents  8.9 Power of Matrices 8.9 Power of Matrices  8.10 Orthogonal Matrices 8.10 Orthogonal Matrices 
Section 2.4. Section Summary  Sequences. o Examples: Geometric Progression, Arithmetic Progression  Recurrence Relations o Example: Fibonacci Sequence.
Matrices Presentation by : Miss Matkar Pallavi. P.
CS 285- Discrete Mathematics Lecture 11. Section 3.8 Matrices Introduction Matrix Arithmetic Transposes and Power of Matrices Zero – One Matrices Boolean.
13.3 Product of a Scalar and a Matrix.  In matrix algebra, a real number is often called a.  To multiply a matrix by a scalar, you multiply each entry.
Matrix Operations McDougal Littell Algebra 2 Larson, Boswell, Kanold, Stiff Larson, Boswell, Kanold, Stiff Algebra 2: Applications, Equations, Graphs Algebra.
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.
2.1 Matrix Operations 2. Matrix Algebra. j -th column i -th row Diagonal entries Diagonal matrix : a square matrix whose nondiagonal entries are zero.
MATRICES.
Equations Quadratic in form factorable equations
Eigenvalues and Eigenvectors
Section 7.4 Matrix Algebra.
Essential Questions How do we use the Factor Theorem to determine factors of a polynomial? How do we factor the sum and difference of two cubes.
2. Matrix Algebra 2.1 Matrix Operations.
Elementary Matrix Methid For find Inverse
Matrices Elements, Adding and Subtracting
Symmetric Matrices and Quadratic Forms
Equations Quadratic in form factorable equations
The sum of all the internal Angles in a triangle is:
Eigenvalues and Eigenvectors
Linear Algebra: Matrix Eigenvalue Problems – Part 2
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Presentation transcript:

Symmetric and Skew Symmetric Matrices Theorem and Proof

Symmetric and Skew – Symmetric Matrix A square matrix A is called a symmetric matrix, if AT = A. A square matrix A is called a skew- symmetric matrix, if AT = - A. Any square matrix can be expressed as the sum of a symmetric and a skew- symmetric matrix.

Theorem 1 For any square matrix A with real number entries, A + A ′ is a symmetric matrix and A – A ′ is a skew symmetric matrix.

Proof Let B = A + A ′, then B′ =(A + A′)′ =A′ + (A′ )′ (as (A + B) ′ = A ′ + B ′ ) =A′ + A (as (A ′) ′ = A) =A + A′ (as A + B = B + A) =B Therefore B = A + A′ is a symmetric matrix Now let C = A – A′ C′ = (A – A′ )′ = A ′ – (A′)′ (Why?) =A′ – A (Why?) = – (A – A ′) = – C Therefore C = A – A′ is a skew symmetric matrix.

Theorem 2 Any square matrix can be expressed as the sum of a symmetric and a skew symmetric matrix.

Proof Let A be a square matrix, then we can writen as From the Theorem 1, we know that (A + A ′ ) is a symmetric matrix and (A – A ′) is a skew symmetric matrix. Since for any matrix A, ( kA)′ = kA′, it follows that is symmetric matrix and is skew symmetric matrix. Thus, any square matrix can be expressed as the sum of a symmetric and a skew symmetric matrix.

Example Show that A= is a skew-symmetric matrix. Solution : As AT = - A, A is a skew – symmetric matrix

Example Express the matrix as the sum of a symmetric and a skew- symmetric matrix. Solution :

Solution Cont.

Solution Cont. Therefore, P is symmetric and Q is skew- symmetric . Further, P+Q = A Hence, A can be expressed as the sum of a symmetric and a skew -symmetric matrix.

ASSESSMENT (Symmetric and Skew Symmetric Matrices)

Express the matrix as the sum of a Question 1: Express the matrix as the sum of a symmetric and askew symmetric matrix. Question 2: Express the following matrices as the sum of a symmetric and a skew symmetric matrix: (II) (I) (III)

For the matrix , verify that (i) (A + A′) is a symmetric matrix Question 3: For the matrix , verify that (i) (A + A′) is a symmetric matrix (ii) (A – A ′) is a skew symmetric matrix Question 4: