Matrices and Determinants

1. Matrices and Determinants

2. Matrices • A matrix is a rectangular arrangement of numbers in rows and columns. Rows run horizontally and columns run vertically. • The dimensions of a matrix are stated “m x n” where ‘m’ is the number of rows and ‘n’ is the number of columns.

3. Equal Matrices • Two matrices are considered equal if they have the same number of rows and columns (the same dimensions) AND all their corresponding elements are exactly the same.

4. Types of Matrices • Rectangular Matrix • Square Matrix • Diagonal Matrix • Scalar Matrix • Identity Matrix • Null Matrix • Row Matrix • Column Matrix • Upper Triangular Matrix • Lower Triangular Matrix • Sub matrix.

5. Matrix Addition • You can add or subtract matrices if they have the same dimensions (same number of rows and columns). • To do this, you add (or subtract) the corresponding numbers (numbers in the same positions).

6. Matrix Addition Example:

7. Properties of Matrix Addition • Matrix addition is commutative i.e. A+B = B+A • Matrix addition is associative i.e. (A+B)+C = A+(B+C) • Matrix addition is distributive w.r.t. scalar K K(A+B) = KA+KB

8. Scalar Multiplication • To do this, multiply each entry in the matrix by the number outside (called the scalar). This is like distributing a number to a polynomial.

9. Scalar Multiplication Example:

10. 2 columns 2 rows Matrix Multiplication • Matrix Multiplication is NOT Commutative! Order matters! • You can multiply matrices only if the number of columns in the first matrix equals the number of rows in the second matrix.

11. Matrix Multiplication • Take the numbers in the first row of matrix #1. Multiply each number by its corresponding number in the first column of matrix #2. Total these products. The result, 11, goes in row 1, column 1 of the answer. Repeat with row 1, column 2; row 1 column 3; row 2, column 1; ...

12. Matrix Multiplication • Notice the dimensions of the matrices and their product. 3 x 2 2 x 3 3 x 3 __ __ __ __

13. Matrix Multiplication • Another example: 3 x 2 2 x 1 3 x 1

14. Properties of Matrix Multiplication • Matrix Multiplication is not commutative, i.e. AB ≠ BA • Matrix Multiplication is associative, i.e. A(BC) = (AB)C • Matrix Multiplication is distributive, i.e. A(B+C) = AB+AC

15. Special Types of Matrices • Idempotent Matrix • Nilpotent Matrix • Involutory Matrix

16. Transpose of Matrix • Let A be any matrix. The matrix obtained by interchanging rows and columns of A is called the transpose of A and is denoted by A’ or AT.

17. Properties of Transpose of Matrices • The transpose of transposed matrix is equal to the matrix itself, i.e. (A’)’ = A. • The transpose of the sum of the two matrices is equal to the transpose of the matrices, i.e. (A+B)’ = A’+B’. • The transpose of the product of two matrices is equal to the product of their transposes in the reverse order, i.e. (AB)’ = B’A’.

18. Matrix Determinants • A Determinant is a real number associated with a matrix. Only SQUARE matrices have a determinant. • The symbol for a determinant can be the phrase “det” in front of a matrix variable, det(A); or vertical bars arounda matrix, |A| or .

19. Determinant of a 2x2 matrix

20. Determinant of a 3x3 matrix Imagine crossing out the first row. And the first column. Now take the double-crossed element. . . And multiply it by the determinant of the remaining 2x2 matrix

21. Determinant of a 3x3 matrix Now keep the first row crossed. Cross out the second column. • Now take the negative of the double-crossed element. • And multiply it by the determinant of the remaining 2x2 matrix. • Add it to the previous result.

22. Determinant of a 3x3 matrix Finally, cross out first row and last column. • Now take the double-crossed element. • Multiply it by the determinant of the remaining 2x2 matrix. • Then add it to the previous piece.

23. Computation • Method of Cofactors • Also known as the expansion of minors

24. Determinant of a 2 x 2 matrix is difference in products of diagonal elements. Method of Minors

25. General form for 2 x 2 matrix Then, ad - bc

26. What about larger matrices? • Use method of cofactors • Need to define a new term, “minor” • Minor of an element aij is the determinant of the matrix formed by deleting the ith row and jth column

27. Example Minor of a12 = 2 is determinant of the 2 x 2 matrix obtained by deleting the 1st row and 2nd column

28. Minor of a12 is

29. Minor of a13 = 3 is

30. Cofactors • Definition • The cofactor of aij = (-1)i+j x minor • Evaluate cofactors for first three elements of the 3 x 3 matrix

31. Pattern of signs

32. Matrix of Cofactors

33. Determinant obtained by expanding along any row or column of matrix of cofactors Determinant of A given by

34. Determinant of A Determinant of A = -15

35. Determinants of 4 x 4 matrices • Computational energy increases as order of matrix increases • Use pivotal condensation (computer algorithm)

36. Key Properties of Determinant • Determinant of matrix and its transpose are equal. • If any two adjacent rows(columns) of a determinant are interchanged, the value of the determinant changes only in sign. • If any two rows or two columns of a determinant are identical or are multiple of each other, then the value of the determinant is zero. • If all the elements of any row or column of a determinant are zero, then the value of the determinant is zero.

37. If all the elements of any row (or column) of a determinant are multiplied by a quantity (K), the value of the determinant is multiplied by the same quantity. • If each element of a row (or column) of a determinant is sum of two elements, the determinant can be expressed as the sum of two determinants of the same order. • The addition of a constant multiple of one row (or column) to another row (or column) leave the determinant unchanged. • The determinant of the product of two matrices of the same order is equal to the product of individual determinants.

38. Adjoint of a Matrix • If A is any square matrix, then the adjoint of A is defined as the transpose of the matrix obtained by replacing the element of A by their corresponding co-factors. • Adj.A = Transpose of the cofactor matrix

39. Inverse Matrix • Inverse of square matrix A is a matrix A-1 that satisfies the following equation • AA-1 = A-1A = I

40. Steps to success in Matrix Inversion • If the determinant = 0, the inverse does not exist if the matrix is singular. • Replace each element of matrix A, by it’s minor • Create the matrix of cofactors • Transpose the matrix of cofactors • Forms the adjoint • Divide each element of the adjoint by the determinant of A.

41. Matrix Inversion • Pre multiplying both sides of the last equation by A-1, and using the result that A-1A=I, • we can get • This is one way to invert matrix A!!!

42. Matrix Inversion • Example

43. Properties of Inverse Matrices • If A and B are non-singular matrices of the same order, then (AB)-1 = B-1.A-1 • The inverse of the transpose of a matrix is equal to the transpose of the inverse of that matrix, i.e. (A’)-1 = (A-1)’ • The inverse of the inverse of a matrix is the matrix itself i.e. (A-1)-1 = A

44. Cramer’s Rule Given an equation system Ax=d where A is n x n. |A1| is a new determinant were we replace the first column of |A| by the column vector d but keep all the other columns intact

45. Cramer’s Rule The expansion of the |A1| by its first column (the d column) will yield the expression because the elements dinow take the place of elements aij.

46. Cramer’s Rule In general, This is the statement of Cramers’Rule

47. Cramer’s Rule Find the solution of

48. Cramer’s Rule Find the solution of the equation system: ♫ Work this out!!!!

49. Cramer’s Rule Solutions: Note that |A| ≠ 0 is necessary condition for the application of Cramer’s Rule. Cramer’s rule is based upon the concept of the inverse matrix, even though in practice it bypasses the process of matrix inversion.

50. Rank of a Matrix The number ‘r’ is called the rank of the matrix A if • There exists at atleast one non-zero minor of order r of A • Every minor of order (r+1) of A is zero. The rank of a matrix A is denoted by p(A).