1. Refresher: Vector and Matrix Algebra Mike Kirkpatrick Department of Chemical Engineering FAMU-FSU College of Engineering

2. Outline Basics: Basics: Operations on vectors and matricesOperations on vectors and matrices Linear systems of algebraic equations Linear systems of algebraic equations Gauss eliminationGauss elimination Matrix rank, existence of a solutionMatrix rank, existence of a solution Inverse of a matrixInverse of a matrix DeterminantsDeterminants Eigenvalues and Eigenvectors Eigenvalues and Eigenvectors applicationsapplications diagonalizationdiagonalization more more

3. Outline cont’ Special matrix properties Special matrix properties symmetric, skew-symmetric, and orthogonal matricessymmetric, skew-symmetric, and orthogonal matrices Hermitian, skew-Hermitian, and unitary matricesHermitian, skew-Hermitian, and unitary matrices

4. Matrices A matrix is a rectangular array of numbers (or functions). A matrix is a rectangular array of numbers (or functions). The matrix shown above is of size m x n. Note that this designates first the number of rows, then the number of columns. The matrix shown above is of size m x n. Note that this designates first the number of rows, then the number of columns. The elements of a matrix, here represented by the letter ‘a’ with subscripts, can consist of numbers, variables, or functions of variables. The elements of a matrix, here represented by the letter ‘a’ with subscripts, can consist of numbers, variables, or functions of variables.

5. Vectors A vector is simply a matrix with either one row or one column. A matrix with one row is called a row vector, and a matrix with one column is called a column vector. A vector is simply a matrix with either one row or one column. A matrix with one row is called a row vector, and a matrix with one column is called a column vector. Transpose: A row vector can be changed into a column vector and vice-versa by taking the transpose of that vector. e.g.: Transpose: A row vector can be changed into a column vector and vice-versa by taking the transpose of that vector. e.g.:

6. Matrix Addition Matrix addition is only possible between two matrices which have the same size. Matrix addition is only possible between two matrices which have the same size. The operation is done simply by adding the corresponding elements. e.g.: The operation is done simply by adding the corresponding elements. e.g.:

7. Matrix scalar multiplication Multiplication of a matrix or a vector by a scalar is also straightforward: Multiplication of a matrix or a vector by a scalar is also straightforward:

8. Transpose of a matrix Taking the transpose of a matrix is similar to that of a vector: Taking the transpose of a matrix is similar to that of a vector: The diagonal elements in the matrix are unaffected, but the other elements are switched. A matrix which is the same as its own transpose is called symmetric, and one which is the negative of its own transpose is called skew-symmetric. The diagonal elements in the matrix are unaffected, but the other elements are switched. A matrix which is the same as its own transpose is called symmetric, and one which is the negative of its own transpose is called skew-symmetric.

9. Matrix Multiplication The multiplication of a matrix into another matrix not possible for all matrices, and the operation is not commutative: The multiplication of a matrix into another matrix not possible for all matrices, and the operation is not commutative: AB ≠ BA in general In order to multiply two matrices, the first matrix must have the same number of columns as the second matrix has rows. In order to multiply two matrices, the first matrix must have the same number of columns as the second matrix has rows. So, if one wants to solve for C=AB, then the matrix A must have as many columns as the matrix B has rows. So, if one wants to solve for C=AB, then the matrix A must have as many columns as the matrix B has rows. The resulting matrix C will have the same number of rows as did A and the same number of columns as did B. The resulting matrix C will have the same number of rows as did A and the same number of columns as did B.

10. Matrix Multiplication The operation is done as follows: The operation is done as follows: using index notation: for example: for example:

11. Linear systems of equations One of the most important application of matrices is for solving linear systems of equations which appear in many different problems including electrical networks, statistics, and numerical methods for differential equations. One of the most important application of matrices is for solving linear systems of equations which appear in many different problems including electrical networks, statistics, and numerical methods for differential equations. A linear system of equations can be written: A linear system of equations can be written: a 11 x 1 + … + a 1n x n = b 1 a 21 x 1 + … + a 2n x n = b 2 : a m1 x 1 + … + a mn x n = b m This is a system of m equations and n unknowns. This is a system of m equations and n unknowns.

12. The system of equations shown on the previous slide can be written more compactly as a matrix equation: The system of equations shown on the previous slide can be written more compactly as a matrix equation:Ax=b where the matrix A contains all the coefficients of the unknown variables from the LHS, x is the vector of unknowns, and b a vector containing the numbers from the RHS where the matrix A contains all the coefficients of the unknown variables from the LHS, x is the vector of unknowns, and b a vector containing the numbers from the RHS Linear systems cont’

13. Gauss elimination Although these types of problems can be solved easily using a wide number of computational packages, the principle of Gaussian elimination should be understood. Although these types of problems can be solved easily using a wide number of computational packages, the principle of Gaussian elimination should be understood. The principle is to successively eliminate variables from the equations until the system is in ‘triangular’ form, that is, the matrix A will contain all zeros below the diagonal. The principle is to successively eliminate variables from the equations until the system is in ‘triangular’ form, that is, the matrix A will contain all zeros below the diagonal.

14. A very simple example: A very simple example: -x + 2y = 4 3x + 4y =38 first, divide the second equation by -2, then add to the first equation to eliminate y; the resulting system is: -x + 2y = 4 -2.5x = -15x = 6 -2.5x = -15x = 6 y = 5 Gauss elimination cont’

15. Matrix rank The rank of a matrix is simply the number of independent row vectors in that matrix. The rank of a matrix is simply the number of independent row vectors in that matrix. The transpose of a matrix has the same rank as the original matrix. The transpose of a matrix has the same rank as the original matrix. To find the rank of a matrix by hand, use Gauss elimination and the linearly dependant row vectors will fall out, leaving only the linearly independent vectors, the number of which is the rank. To find the rank of a matrix by hand, use Gauss elimination and the linearly dependant row vectors will fall out, leaving only the linearly independent vectors, the number of which is the rank.

16. Matrix inverse The inverse of the matrix A is denoted as A -1 The inverse of the matrix A is denoted as A -1 By definition, AA -1 = A -1 A = I, where I is the identity matrix. By definition, AA -1 = A -1 A = I, where I is the identity matrix. Theorem: The inverse of an n x n matrix A exists if and only if the rank A = n. Theorem: The inverse of an n x n matrix A exists if and only if the rank A = n. Gauss-Jordan elimination can be used to find the inverse of a matrix by hand. Gauss-Jordan elimination can be used to find the inverse of a matrix by hand.

17. Determinants Determinants are useful in eigenvalue problems and differential equations. Determinants are useful in eigenvalue problems and differential equations. Can be found only for square matrices. Can be found only for square matrices. Simple example: 2 nd order determinant Simple example: 2 nd order determinant

18. 3 rd order determinant The determinant of a 3 X 3 matrix is found as follows: The determinant of a 3 X 3 matrix is found as follows: The terms on the RHS can be evaluated as shown for a 2 nd order determinant. The terms on the RHS can be evaluated as shown for a 2 nd order determinant.

19. Some theorems for determinants Cramer’s: If the determinant of a system of n equations with n unknowns is nonzero, that system has precisely one solution. Cramer’s: If the determinant of a system of n equations with n unknowns is nonzero, that system has precisely one solution. det(AB)=det(BA)=det(A)det(B) det(AB)=det(BA)=det(A)det(B)

20. Eigenvalues and Eigenvectors Let A be an nxn matrix and consider the vector equation: Let A be an nxn matrix and consider the vector equation: Ax = x A value of for which this equation has a solution x≠0 is called an eigenvalue of the matrix A. A value of for which this equation has a solution x≠0 is called an eigenvalue of the matrix A. The corresponding solutions x are called the eigenvectors of the matrix A. The corresponding solutions x are called the eigenvectors of the matrix A.

21. Solving for eigenvalues Ax=x Ax - x = 0 (A- I)x = 0 This is a homogeneous linear system, homogeneous meaning that the RHS are all zeros. This is a homogeneous linear system, homogeneous meaning that the RHS are all zeros. For such a system, a theorem states that a solution exists given that det(A-I)=0. For such a system, a theorem states that a solution exists given that det(A-I)=0. The eigenvalues are found by solving the above equation. The eigenvalues are found by solving the above equation.

22. Solving for eigenvalues cont’ Simple example: find the eigenvalues for the matrix: Simple example: find the eigenvalues for the matrix: Eigenvalues are given by the equation det(A-I) = 0: Eigenvalues are given by the equation det(A-I) = 0: So, the roots of the last equation are -1 and -6. These are the eigenvalues of matrix A. So, the roots of the last equation are -1 and -6. These are the eigenvalues of matrix A.

23. Eigenvectors For each eigenvalue,, there is a corresponding eigenvector, x. For each eigenvalue,, there is a corresponding eigenvector, x. This vector can be found by substituting one of the eigenvalues back into the original equation: Ax = x : for the example:-5x 1 + 2x 2 = x 1 This vector can be found by substituting one of the eigenvalues back into the original equation: Ax = x : for the example:-5x 1 + 2x 2 = x 1 2x 1 – 2x 2 = x 2 2x 1 – 2x 2 = x 2 Using =-1, we get x 2 = 2x 1, and by arbitrarily choosing x 1 = 1, the eigenvector corresponding to =-1 is: Using =-1, we get x 2 = 2x 1, and by arbitrarily choosing x 1 = 1, the eigenvector corresponding to =-1 is: and similarly,

24. Special matrices A matrix is called symmetric if: A matrix is called symmetric if: A T = A A skew-symmetric matrix is one for which: A skew-symmetric matrix is one for which: A T = -A A T = -A An orthogonal matrix is one whose transpose is also its inverse: An orthogonal matrix is one whose transpose is also its inverse: A T = A -1 A T = A -1

25. Complex matrices If a matrix contains complex (imaginary) elements, it is often useful to take its complex conjugate. The notation used for the complex conjugate of a matrix A is:  If a matrix contains complex (imaginary) elements, it is often useful to take its complex conjugate. The notation used for the complex conjugate of a matrix A is:  Some special complex matrices are as follows: Some special complex matrices are as follows: Hermitian:  T = A Skew-Hermitian:  T = -A Unitary:  T = A -1