DEPARTMENT OF MATHEMATICS MATHEMATICS - I

1. DEPARTMENT OF MATHEMATICSMATHEMATICS - I MATRICES I year I sem Dr.Khasim Ali PROFESSOR

2. Matrices – definitions Matrix notation Equal matrices Addition and subtraction of matrices Multiplication of matrices Transpose of a matrix Special matrices Determinant of a square matrix Inverse of a square matrix Solution of a set of linear equations Eigenvalues and eigenvectors

3. Matrices – definitions A matrix is a set of real or complex numbers (called elements) arranged in rows and columns to form a rectangular array. A matrix having m rows and n columns is called an m×n matrix. For example: is a 2 × 3 matrix.

4. Matrices – definitions Row matrix A row matrix consists of a single row. For example: Column matrix A column matrix consists of a single column. For example:

5. Matrix notation Where there is no ambiguity a matrix can be represented by a single general element in brackets or by a capital letter in bold type.

6. Equal matrices Two matrices are equal if corresponding elements throughout are equal.

7. Addition and subtraction of matrices Two matrices are added (or subtracted) by adding (or subtracting) corresponding elements. For example:

8. Multiplication of matrices Scalar multiplication Multiplication of two matrices

9. Multiplication of matrices Scalar multiplication To multiply a matrix by a single number (a scalar), each individual element of the matrix is multiplied by that number. For example: That is:

10. Multiplication of matrices Multiplication of two matrices Two matrices can only be multiplied when the number of columns in the first matrix equals the number of rows in the second matrix. The ijth element of the product matrix is obtained by multiplying each element in the ith row of the first matrix by the corresponding element in the jth column of the second matrix and the element products added. For example:

11. Multiplication of matrices Multiplication of two matrices

12. Multiplication of matrices Multiplication of two matrices

13. Transpose of a matrix If a new matrix is formed by interchanging rows and columns the new matrix is called the transpose of the original matrix. For example, if:

14. Special matrices Square matrix Diagonal matrix Unit matrix Null matrix

15. Special matrices Square matrix A square matrix is of order m×m. A square matrix is symmetric if . For example: A square matrix is skew-symmetric if . For example:

16. Special matrices Diagonal matrix A diagonal matrix is a square matrix with all elements zero except those on the leading diagonal. For example:

17. Special matrices Unit matrix A unit matrix is a diagonal matrix with all elements on the leading diagonal being equal to unity. For example: The product of matrix A and the unit matrix is the matrix A:

18. Special matrices Null matrix A null matrix is one whose elements are all zero. Notice that But that if we cannot deduce that or

19. Determinant of a square matrix Singular matrix Cofactors Adjoint of a square matrix

20. Determinant of a square matrix Singular matrix Every square matrix has its associated determinant. For example, the determinant of The determinant of a matrix is equal to the determinant of its transpose. A matrix whose determinant is zero is called a singular matrix.

21. Determinant of a square matrix Cofactors Each element of a square matrix has a minor which is the value of the determinant obtained from the matrix after eliminating the ith row and jth column to which the element is common. The cofactor of element is then given as the minor of multiplied by

22. Determinant of a square matrix Adjoint of a square matrix Let square matrix C be constructed from the square matrix A where the elements of C are the respective cofactors of the elements of A so that if: Then the transpose of C is called the adjoint of A, denoted by adjA.

23. Inverse of a square matrix If each element of the adjoint of a square matrix A is divided by the determinant of A then the resulting matrix is called the inverse of A, denoted by A-1. Note: if detA = 0 then the inverse does not exist

24. Inverse of a square matrix Product of a square matrix and its inverse The product of a square matrix and its inverse is the unit matrix:

25. Solution of a set of linear equations The set of n simultaneous linear equations in n unknowns can be written in matrix form as:

26. Solution of a set of linear equations Since: The solution is then:

27. Solution of a set of linear equations Gaussian elimination method for solving a set of linear equations Given: Create the augmented matrix B, where:

28. Solution of a set of linear equations Gaussian elimination method for solving a set of linear equations Eliminate the elements other than a11 from the first column by subtracting a21/a11 times the first row from the second row, a31/a11 times the first row from the third row, etc. This gives a new matrix of the form:

29. Solution of a set of linear equations Gaussian elimination method for solving a set of linear equations This process is repeated to eliminate the ci2 from the third and subsequent rows until a matrix of the following form is arrived at:

30. Solution of a set of linear equations Gaussian elimination method for solving a set of linear equations From this augmented matrix we revert to the product:

31. Solution of a set of linear equations Gaussian elimination method for solving a set of linear equations From this product the solution is derived by working backwards from the bottom starting with:

32. Eigenvalues and eigenvectors Equations of the form: where A is a square matrix and is a number (scalar) have non-trivial solutions (x0) for x called eigenvectors or characteristic vectors of A. The corresponding values of are called eigenvalues, characteristic values or latent roots of A.

33. Eigenvalues and eigenvectors Expressed as a set of separate equations: That is:

34. Eigenvalues and eigenvectors These can be rewritten as: That is: Which means that, for non-trivial solutions:

35. Eigenvalues and eigenvectors Eigenvalues To find the eigenvalues of: solve the characteristic equation: That is: This gives eigenvalues

36. Eigenvalues and eigenvectors Eigenvectors To find the eigenvectors of solve the equation For the eigenvalues = 1 and = 5

37. Learning outcomes • Define a matrix • Understand what is meant by the equality of two matrices • Add and subtract two matrices • Multiply a matrix by a scalar and multiply two matrices together • Obtain the transpose of a matrix • Recognize the special types of matrix • Obtain the determinant, cofactors and adjoint of a square matrix • Obtain the inverse of a non-singular matrix • Use matrices to solve a set of linear equations using inverse matrices • Use the Gaussian elimination method to solve a set of linear equations • Evaluate eigenvalues and eigenvectors