The Further Mathematics network www.fmnetwork.org.uk.

Slides:



Advertisements
Similar presentations
The Further Mathematics network
Advertisements

Chapter 6 Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors (11/17/04) We know that every linear transformation T fixes the 0 vector (in R n, fixes the origin). But are there other subspaces.
Linear Transformations
數位控制(九).
Ch 7.3: Systems of Linear Equations, Linear Independence, Eigenvalues
6 1 Linear Transformations. 6 2 Hopfield Network Questions.
Digital Control Systems Vector-Matrix Analysis. Definitions.
Further Pure 1 Solving Simultaneous equations. Simultaneous equations You have already learnt how to solve simultaneous equations at GCSE and AS level.
Using Inverse Matrices Solving Systems. You can use the inverse of the coefficient matrix to find the solution. 3x + 2y = 7 4x - 5y = 11 Solve the system.
8.1 Vector spaces A set of vector is said to form a linear vector space V Chapter 8 Matrices and vector spaces.
Linear Algebra/Eigenvalues and eigenvectors. One mathematical tool, which has applications not only for Linear Algebra but for differential equations,
PHY 301: MATH AND NUM TECH Chapter 5: Linear Algebra Applications I.Homogeneous Linear Equations II.Non-homogeneous equation III.Eigen value problem.
Row Reduction Method Lesson 6.4.
The Further Mathematics network
Diagonalization Revisted Isabel K. Darcy Mathematics Department Applied Math and Computational Sciences University of Iowa Fig from knotplot.com.
Day 1 Eigenvalues and Eigenvectors
Day 1 Eigenvalues and Eigenvectors
Eigenvalues and Eigenvectors
Chapter 4 Section 4: Inverse and Identity Matrices 1.
Advanced Trig Exam Review Day Three: Matrices. Solving Systems of Equations.
MA10210: ALGEBRA 1B
Domain Range definition: T is a linear transformation, EIGENVECTOR EIGENVALUE.
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.
The Further Mathematics network
Eigenvectors and Linear Transformations Recall the definition of similar matrices: Let A and C be n  n matrices. We say that A is similar to C in case.
Section 5.1 First-Order Systems & Applications
Section 10.3 and Section 9.3 Systems of Equations and Inverses of Matrices.
The Further Mathematics network
KEY THEOREMS KEY IDEASKEY ALGORITHMS LINKED TO EXAMPLES next.
Assignment Questions?? Pg all, 23-26, 35, 46, 48 Handout Questions?
Linear Algebra Chapter 6 Linear Algebra with Applications -Gareth Williams Br. Joel Baumeyer, F.S.C.
2.5 DETERMINANTS AND MULTIPLICATIVE INVERSES OF MATRICES By the end of the section students will evaluate determinants, find inverses of matrices, and.
Section 4.3 Properties of Linear Transformations from R n to R m.
The Further Mathematics network
Review of Eigenvectors and Eigenvalues from CliffsNotes Online mining-the-Eigenvectors-of-a- Matrix.topicArticleId-20807,articleId-
Lecture XXVII. Orthonormal Bases and Projections Suppose that a set of vectors {x 1,…,x r } for a basis for some space S in R m space such that r  m.
Tutorial 6. Eigenvalues & Eigenvectors Reminder: Eigenvectors A vector x invariant up to a scaling by λ to a multiplication by matrix A is called.
College Algebra Chapter 6 Matrices and Determinants and Applications
Use Inverse Matrices to Solve Linear Systems
Lesson 4 – Solving Simultaneous equations
Review of Eigenvectors and Eigenvalues
Warm-up Problem Use the Laplace transform to solve the IVP.
Matrices and Matrix Solutions
Matrices and vector spaces
Review Problems Matrices
Let Maths take you Further…
Elementary Linear Algebra
Let Maths take you Further…
Section 6.4 Multiplicative Inverses of Matices and Matrix Equations
Solving Linear Systems Using Inverse Matrices
FP2 (MEI) Maclaurin Series
Let Maths take you Further…
Find the inverse of the matrix
Notes Over 11.2 Number Compared to Base is Unknown
MATH 374 Lecture 23 Complex Eigenvalues.
Further Matrix Algebra
FP2 (MEI) Hyperbolic functions -Introduction (part 1)
Section 9.4 Multiplicative Inverses of Matices and Matrix Equations
EIGENVECTORS AND EIGENVALUES
MATH 1310 Section 2.8.
Linear Algebra Lecture 29.
Solving simultaneous equations
MATH 1310 Section 2.8.
MATH 1310 Section 2.8.
Matrix arithmetic: the product of two matrices
MATH 1310 Section 2.8.
Eigenvalues and Eigenvectors
Presentation transcript:

the Further Mathematics network

the Further Mathematics network FP2 (MEI) Matrices (part 3) Eigenvalues & eigenvectors, diagonalisation & the Cayley-Hamilton theorem Let Maths take you Further…

Before you start:  You need to be able to find the determinant and the inverse of a 2x2 matrix and 3x3 matrix. You need to be familiar with the work on matrices on FP1, in particular you should know what is meant by an invariant line. Eigenvalues & eigenvectors, diagonalisation & the Cayley-Hamilton theorem

Solving simultaneous equations using matrices, eigenvalues & eigenvectors, Cayley-Hamilton theorem When you have finished… You should: Understand the meaning of eigenvalue and eigenvector, and be able to find these for 2 x 2 or 3 x 3 matrices whenever this is possible. Understand the term characteristic equation of a 2 x 2 or 3 x 3 matrix. Be able to form the matrix of eigenvectors and use this to reduce a matrix to diagonal form. Be able to find use the diagonal form to find powers of a 2 x 2 or 3 x 3 matrix. Understand that every 2 x 2 or 3 x 3 matrix satisfies its own characteristic equation, and be able to use this.

Recap: FP1 invariant lines A transformation represented by a 2 × 2 matrix maps lines to lines The next concept we will consider is which lines map onto themselves under a transformation (represented by a matrix)

Before we start...

Eigenvalues and Eigenvectors of a 2×2 matrix

Eigenvalues and Eigenvectors of 3×3 matrices

Diagonalisation

This is useful for finding powers of matrices

Example

The Cayley Hamilton Theorem

Exam Question

Solving simultaneous equations using matrices, eigenvalues & eigenvectors, Cayley-Hamilton theorem When you have finished… You should: Understand the meaning of eigenvalue and eigenvector, and be able to find these for 2 x 2 or 3 x 3 matrices whenever this is possible. Understand the term characteristic equation of a 2 x 2 or 3 x 3 matrix. Be able to form the matrix of eigenvectors and use this to reduce a matrix to diagonal form. Be able to find use the diagonal form to find powers of a 2 x 2 or 3 x 3 matrix. Understand that every 2 x 2 or 3 x 3 matrix satisfies its own characteristic equation, and be able to use this.

Independent study: Using the MEI online resources complete the study plans for the two sections: Matrices 4 & 5 Do the online multiple choice tests for these sections and submit your answers online.