7 7.2 © 2016 Pearson Education, Ltd. Symmetric Matrices and Quadratic Forms QUADRATIC FORMS.

Slides:



Advertisements
Similar presentations
Chapter 4 Euclidean Vector Spaces
Advertisements

Ch 7.7: Fundamental Matrices
Chapter 28 – Part II Matrix Operations. Gaussian elimination Gaussian elimination LU factorization LU factorization Gaussian elimination with partial.
Scientific Computing QR Factorization Part 2 – Algorithm to Find Eigenvalues.
Chapter 6 Eigenvalues and Eigenvectors
Linear Equations in Linear Algebra
8 CHAPTER Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1.
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
6 6.1 © 2012 Pearson Education, Inc. Orthogonality and Least Squares INNER PRODUCT, LENGTH, AND ORTHOGONALITY.
Symmetric Matrices and Quadratic Forms
Symmetric Matrices and Quadratic Forms
Lecture 19 Quadratic Shapes and Symmetric Positive Definite Matrices Shang-Hua Teng.
Ch 7.9: Nonhomogeneous Linear Systems
ENGG2013 Unit 19 The principal axes theorem
Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues
The Terms that You Have to Know! Basis, Linear independent, Orthogonal Column space, Row space, Rank Linear combination Linear transformation Inner product.
Lecture 20 SVD and Its Applications Shang-Hua Teng.
4 4.6 © 2012 Pearson Education, Inc. Vector Spaces RANK.
Linear Equations in Linear Algebra
1 © 2012 Pearson Education, Inc. Matrix Algebra THE INVERSE OF A MATRIX.
Matrix Algebra THE INVERSE OF A MATRIX © 2012 Pearson Education, Inc.
6 6.1 © 2012 Pearson Education, Inc. Orthogonality and Least Squares INNER PRODUCT, LENGTH, AND ORTHOGONALITY.
5 5.1 © 2012 Pearson Education, Inc. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
5.1 Orthogonality.
Boyce/DiPrima 9th ed, Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues Elementary Differential Equations and Boundary Value Problems,
CHAPTER SIX Eigenvalues
Elementary Linear Algebra Anton & Rorres, 9th Edition
4 4.2 © 2012 Pearson Education, Inc. Vector Spaces NULL SPACES, COLUMN SPACES, AND LINEAR TRANSFORMATIONS.
Eigenvalues and Eigenvectors
4 4.4 © 2012 Pearson Education, Inc. Vector Spaces COORDINATE SYSTEMS.
1 1.5 © 2016 Pearson Education, Inc. Linear Equations in Linear Algebra SOLUTION SETS OF LINEAR SYSTEMS.
Linear Equations in Linear Algebra
1 1.7 © 2016 Pearson Education, Inc. Linear Equations in Linear Algebra LINEAR INDEPENDENCE.
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.
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.
Ch11: Normal Modes 1. Review diagonalization and eigenvalue problem 2. Normal modes of Coupled oscillators (3 springs, 2 masses) 3. Case of Weakly coupled.
Chapter 6 Eigenvalues. Example In a certain town, 30 percent of the married women get divorced each year and 20 percent of the single women get married.
5.1 Eigenvectors and Eigenvalues 5. Eigenvalues and Eigenvectors.
CHARACTERIZATIONS OF INVERTIBLE MATRICES
5.1 Eigenvalues and Eigenvectors
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.
1 Chapter 8 – Symmetric Matrices and Quadratic Forms Outline 8.1 Symmetric Matrices 8.2Quardratic Forms 8.3Singular ValuesSymmetric MatricesQuardratic.
5 5.1 © 2016 Pearson Education, Ltd. Eigenvalues and Eigenvectors EIGENVECTORS AND EIGENVALUES.
2 2.2 © 2016 Pearson Education, Ltd. Matrix Algebra THE INVERSE OF A MATRIX.
1 1.4 Linear Equations in Linear Algebra THE MATRIX EQUATION © 2016 Pearson Education, Ltd.
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 
6 6.5 © 2016 Pearson Education, Ltd. Orthogonality and Least Squares LEAST-SQUARES PROBLEMS.
Tutorial 6. Eigenvalues & Eigenvectors Reminder: Eigenvectors A vector x invariant up to a scaling by λ to a multiplication by matrix A is called.
Elementary Linear Algebra Anton & Rorres, 9th Edition
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Boyce/DiPrima 10th ed, Ch 7.7: Fundamental Matrices Elementary Differential Equations and Boundary Value Problems, 10th edition, by William E. Boyce and.
Euclidean Inner Product on Rn
C H A P T E R 3 Vectors in 2-Space and 3-Space
Symmetric Matrices and Quadratic Forms
Eigen Decomposition Based on the slides by Mani Thomas
EIGENVECTORS AND EIGENVALUES
Linear Algebra Lecture 29.
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Linear Algebra: Matrix Eigenvalue Problems – Part 2
Matrix Algebra THE INVERSE OF A MATRIX © 2012 Pearson Education, Inc.
Linear Algebra Lecture 28.
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Symmetric Matrices and Quadratic Forms
Presentation transcript:

7 7.2 © 2016 Pearson Education, Ltd. Symmetric Matrices and Quadratic Forms QUADRATIC FORMS

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS  Example 1: Let. Compute x T Ax for the following matrices. a. b.

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS  Solution: a.. b. There are two entries in A.

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS  The presence of in the quadratic form in Example 1(b) is due to the entries off the diagonal in the matrix A.  In contrast, the quadratic form associated with the diagonal matrix A in Example 1(a) has no x 1 x 2 cross- product term.

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM  Since A is symmetric, Theorem 2 guarantees that there is an orthogonal matrix P such that P T AP is a diagonal matrix D, and the quadratic form in (2) becomes y T Dy.  Example 4: Make a change of variable that transforms the quadratic form into a quadratic form with no cross-product term.  Solution: The matrix of the given quadratic form is

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM  Let  Then and.  A suitable change of variable is,where and.

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM  Then  To illustrate the meaning of the equality of quadratic forms in Example 4, we can compute Q (x) for using the new quadratic form.

Slide © 2016 Pearson Education, Ltd. CHANGE OF VARIBALE IN A QUADRATIC FORM  First, since, so  Hence  This is the value of Q (x) when.

Slide © 2016 Pearson Education, Ltd. THE PRINCIPAL AXIS THEOREM  See the figure below.  Theorem 4: Let A be an symmetric matrix. Then there is an orthogonal change of variable,, that transforms the quadratic form x T Ax into a quadratic form y T Dy with no cross-product term.

Slide © 2016 Pearson Education, Ltd. THE PRINCIPAL AXIS THEOREM

Slide © 2016 Pearson Education, Ltd. A GEOMETRIC VIEW OF PRINCIPAL AXES

Slide © 2016 Pearson Education, Ltd. A GEOMETRIC VIEW OF PRINCIPAL AXES  If A is not a diagonal matrix, the graph of equation (3) is rotated out of standard position, as in the figure below.  Finding the principal axes (determined by the eigenvectors of A) amounts to finding a new coordinate system with respect to which the graph is in standard position.

Slide CLASSIFYING QUADRATIC FORMS  Definition: A quadratic form Q is: a.positive definite if for all, b.negative definite if for all, c.indefinite if Q (x) assumes both positive and negative values.  Also, Q is said to be positive semidefinite if for all x, and negative semidefinite if for all x. © 2016 Pearson Education, Ltd.

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS AND EIGENVALUES  Theorem 5: Let A be an symmetric matrix. Then a quadratic form x T Ax is: a.positive definite if and only if the eigenvalues of A are all positive, b.negative definite if and only if the eigenvalues of A are all negative, or c.indefinite if and only if A has both positive and negative eigenvalues.

Slide QUADRATIC FORMS AND EIGENVALUES  Proof: By the Principal Axes Theorem, there exists an orthogonal change of variable such that (4) where λ 1,…,λ n are the eigenvalues of A.  Since P is invertible, there is a one-to-one correspondence between all nonzero x and all nonzero y. © 2016 Pearson Education, Ltd.

Slide © 2016 Pearson Education, Ltd. QUADRATIC FORMS AND EIGENVALUES  Thus the values of Q (x) for coincide with the values of the expression on the right side of (4), which is controlled by the signs of the eigenvalues λ 1,…,λ n, in three ways described in the theorem.