AMA1061_2012_Lec1_Sep17 - Outline Introduction Determinants Basic Properties Cramers rule for linear equations Determinants of higher order AMA1061

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Unformatted text preview: Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order AMA1061 FOUNDATION MATHEMATICS DEPARTMENT OF APPLIED MATHEMATICS Lecturer: Dr. Catherine LIU Contact: 2766 6931 (O); Office Venue: HJ616 Consultation Hours: 2:45pm-4:45pm, Wednesday 17/09/2012 AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 1 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Lecture 1 MATRICES AND DETERMINANTS References: Section 9.1 & 9.2 & Chapter 8 Chung, K. C. A Short Course in Calculus and Matrices, 2nd Ed., McGraw Hill AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 2 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Outline 1 2 3 4 5 Introduction to Matrices Determinants Basic properties of determinants Cramer’s rule for linear equations Determinants of higher order. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 3 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Definition of Matrix A matrix is a rectangular array of scalars of the form ⎡ ⎤ a11 a12 ⋅ ⋅ ⋅ a1n ⎢ a21 a22 ⋅ ⋅ ⋅ a2n ⎥ ⎢ ⎥ ⎢. . .⎥ . .⎦ ⎣. . . . am1 am2 ⋅ ⋅ ⋅ amn (1) This matrix is called an m × n matrix . It consists of m rows and n columns, the i -th row (or simply row i ) being the 1 × n matrix [ai 1 ai 2 ⋅ ⋅ ⋅ ain ] and the j -th column (or column j ) the m × 1 matrix ⎡ ⎤ a1j ⎢ a2j ⎥ ⎢ ⎥ ⎢ . ⎥. .⎦ ⎣. anj Remark: The entry aij which belongs to row i and column j is called the (i , j )-entry of the matrix. The matrix (1) can be written in abbreviated forms such as [aij ]m×n or [aij ] or [A] or simply A. The 1 × 1 matrix [ ] is just the scalar AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants . 17/09/2012 4 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Definition of Matrix Example The following are examples of matrices. A= 4.5 1.4 2.3 5.9 , ⎡ ⎤ 5 1 B = ⎣ 2 −9 ⎦ , 3 2 ⎡⎤ 2 c = ⎣6⎦ . 5 The first, denoted by A, is a 2 × 2 matrix. The second, denoted by B , is a 3 × 2 matrix. The third, denoted by c is a 3 × 1 matrix. If the entries of the matrix A above are denoted by aij , then a11 = 4.5, a12 = 1.4, a21 = 2.3, a22 = 5.9. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 5 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Transposition If A = [aij ] is m × n, then its transpose is the n × m matrix, denoted by AT , obtained by interchanging the rows and columns of A, i.e. AT = [bij ] where bij = aji for all i = 1, . . . , n; j = 1, . . . , m. Example If A, B and c are the matrices given in the previous example, then AT = 4.5 2.3 1.4 5.9 AMA1061 (By Catherine Liu) , BT = 5 23 1 −9 2 , Lecture 1 Matrices and Determinants cT = [ 2 6 5 ]. 17/09/2012 6 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Vectors An m × 1 matrix is called a column m-vector or an m-vector or a column-vector or simply a vector . Column-vectors are denoted by bold-faced lower-case letters like a, x in printed form and is hand-written as a , x , etc. A ∼∼ 1 × n matrix is called a row-vector . As row-vectors are transposes of column-vectors, they are denoted by aT , xT , etc. and are written as a T , x T , ∼∼ etc. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 7 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Equality of matrices Two m × n matrices A and B are said to be equal , written A = B , if aij = bij for all i = 1, . . . , m; j = 1, . . . , n. Example If E = 42 15 and F = 41 , then E ∕= F but 25 E = FT. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 8 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Addition and subtraction We can add two m × n matrices. The sum, denoted by A + B is an m × n matrix C = [cij ] such that cij = aij + bij for all i , j . Similarly, the difference, denoted by A − B is an m × n matrix D = [dij ] such that dij = aij − bij for all i , j . Example 42 35 + −2 1 2 −2 = 4−2 2+1 3+2 5−2 = 2 5 3 3 . 42 35 − −2 1 2 −2 = 4+2 2−1 3−2 5+2 = 6 1 1 7 . AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 9 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Scalar multiplication If k is a scalar (real or complex), then kA is the matrix C = [cij ] such that cij = kaij for all i , j . Example 3 4 2 3 −5 = AMA1061 (By Catherine Liu) 12 6 9 −15 , k −2 1 34 Lecture 1 Matrices and Determinants = −2k 3k k 4k 17/09/2012 . 10 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Zero matrix This is a matrix all whose entries are 0. Zero matrices are denoted by O . However, zero vectors are usually denoted by 0. Example O= 00 00 AMA1061 (By Catherine Liu) , ⎡ ⎤ 000 O = ⎣ 0 0 0 ⎦, 000 Lecture 1 Matrices and Determinants ⎡⎤ 0 0 = ⎣0⎦ . 0 17/09/2012 11 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Multiplication of vectors If a row n-vector is multiplied by a column n-vector on the right, then the product is a scalar which is equal to the sum of the products of the corresponding entries in the two vectors. Using symbols, if ⎡⎤ b1 ⎢b2 ⎥ ⎢⎥ aT = [a1 a2 ⋅ ⋅ ⋅ an ] and b = ⎢ . ⎥ ⎣.⎦ . bn then aT b = a1 b1 + a2 b2 + ⋅ ⋅ ⋅ + an bn . Example 3 −2 1 AMA1061 (By Catherine Liu) ⎡ ⎤ 2 ⎣ 4 ⎦ = 3 × 2 + (−2) × 4 + 1 × (−3) = −5. −3 Lecture 1 Matrices and Determinants 17/09/2012 12 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Multiplication of matrices If A = [aij ] is m × n, B = [bij ] is n × p then the product AB is the m × p matrix C = [cij ] such that cij = ai 1 b1j + ai 2 b2j + ⋅ ⋅ ⋅ + ain bnj for all i , j , i.e. the entry cij is equal to row i of A right-multiplied by column j of B . The product AB is well-defined only when the number of columns of A is equal to the number of rows of B . AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 13 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Example If A = 5 2 1 2 2 −1 3 3 ⎡ 1 ⎢ −2 and B = ⎢ ⎣ 3 −4 2 3 −3 2 ⎤ 3 −1 ⎥ ⎥ then −1 ⎦ 2 c11 c21 c12 c22 c13 c23 AB = = −3 −17 13 19 18 11 where the entries cij are computed as follows: c11 = 5 × 1 + 1 × ( −2 ) + 2 × 3 + 3 × ( −4 ) = −3 . c12 = 5 × 2 + 1 × 3 + 2 × (−3) + 3 × 2 = 13. c13 = 5 × 3 + 1 × (−1) + 2 × (−1) + 3 × 2 = 18. c21 = 2 × 1 + 2 × (−2) + (−1) × 3 + 3 × (−4) = −17. c22 = 2 × 2 + 2 × 3 + (−1) × (−3) + 3 × 2 = 19. c23 = 2 × 3 + 2 × (−1) + (−1) × (−1) + 3 × 2 = 11. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 14 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Properties If the sums are well-defined, we have: 1 A+B =B+A 2 (A + B ) + C = A + (B + C ) 3 A+O =A 4 A + (−A) = O where −A = (−1)A TT 5 (A ) = A 6 (A + B )T = AT + B T If the products are well-defined, then 7 A(BC ) = (AB )C 8 A(B + C ) = AB + AC , (A + B )C = AC + BC 9 (AB )T = B T AT 10 There are examples of A and B for which AB ∕= BA. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 15 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Square matrices An n × n matrix is a square matrix . Its order is n. Note that a square matrix of order 1 is just a number. Example The following matrices A, B , C are square matrices. A= 52 13 AMA1061 (By Catherine Liu) ⎡ ⎤ ⎡ ⎤ 203 200 , B = ⎣ 4 1 0 ⎦, C = ⎣ 0 1 0 ⎦ 724 004 Lecture 1 Matrices and Determinants 17/09/2012 16 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Diagonal elements If A = [aij ] is a square matrix of order n, then the entries a11 , a22 , . . . , ann are the diagonal elements of A. For example, the diagonal elements of the matrix ⎡ ⎤ 2 5 −3 A = ⎣ 3 −1 7 ⎦ 204 are 2, −1, 4. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 17 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Diagonal matrix A diagonal matrix is a square matrix whose entries are all zero except possibly the diagonal elements. Example The matrices ⎡ ⎤ 200 A = ⎣ 0 −1 0 ⎦ 004 are diagonal matrices. ⎡ ⎤ 200 and B = ⎣ 0 1 0 ⎦ 000 We use diag(a11 , . . . , ann ) to denote the diagonal matrix of order n whose diagonal elements are a11 , . . . , ann . For example, the matrix B above can be represented by diag(2, 1, 0). AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 18 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Identity matrix A diagonal matrix whose diagonal elements are all unity is called an identity matrix . It is denoted by In if the specification of its order n is necessary. Usually we denote it simply by I . Example The matrices I2 = are identity matrices. 10 01 , ⎡ ⎤ 100 I3 = ⎣ 0 1 0 ⎦ , 001 Theorem If A is a square matrix of order n and I the identity matrix of the same order then AI = IA = A. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 19 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Determinant Using matrix terminology, a determinant is defined for every square matrix. If A is a square matrix, then a determinant is formed and is called the determinant of A and is denoted by det A or det(A). For example, let ⎤ ⎡ 3 2 −1 0 ⎦. A = ⎣ 2 −1 (2) 2 3 1 Then det A = AMA1061 (By Catherine Liu) 3 2 −1 2 −1 0 2 3 1 = −15. Lecture 1 Matrices and Determinants (3) 17/09/2012 20 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Second-order determinants By a determinant of the second order we mean the symbol a1 b1 a2 b2 which represents the number a1 b2 − a2 b1 evaluated in the following way: a1 b1 a2 b2 The second order determinant has two (horizontal) rows and two (vertical) columns1 . 1 Afterward Ri is for row i and Cj for column j in this course. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 21 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Cramer’s rule Consider a system of two linear equations in two unknowns x and y : a 1 x + b 1 y = k1 a 2 x + b 2 y = k2 (4) An ordered pair of numbers (x , y ) is said to be a solution of the system if x and y satisfy the equations in the system. By some calculation, if a1 b2 − a2 b1 ∕= 0, the solution is given by x= k1 b 2 − k2 b 1 , y= , a1 b2 − a2 b1 a 1 k2 − a 2 k1 y= a1 b2 − a2 b1 (5) . With the notation of determinants, we may now write (5) as x= k1 k2 b1 b2 ÷ a1 a2 b1 k1 a2 b2 a1 k2 ÷ a1 b1 a2 b2 (6) . The formulas (6) are called Cramer’s rule in which we assume that the denominator a1 a2 AMA1061 (By Catherine Liu) b1 b2 ∕= 0. Lecture 1 Matrices and Determinants 17/09/2012 22 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Examples Example Solve the following linear system by Cramer’s rule. 2x +3y =4 4x + y = −2 Solution. Using Cramer’s rule (6), we get x= 4 3 −2 1 ÷ 2 3 4 1 AMA1061 (By Catherine Liu) = 10 = −1, −10 y= 2 4 4 −2 Lecture 1 Matrices and Determinants ÷ (−10) = −20 = 2. −10 17/09/2012 23 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Third-order determinants A third-order determinant consists of three rows and three columns. It is denoted by a1 b1 c1 a2 b2 c2 a3 b3 c3 and is a number which can be found as follows: a1 b1 c1 a2 b2 c2 a3 b3 c3 b2 c2 = a1 a2 c2 b3 c3 a2 b2 a3 b3 + c1 − b1 a3 c3 (7) = a1 (b2 c3 − b3 c2 ) − b1 (a2 c3 − a3 c2 ) + c1 (a2 b3 − a3 b2 ) = a1 b2 c3 + a3 b1 c2 + a2 b3 c1 − a3 b2 c1 − a1 b3 c2 − a2 b1 c3 . AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants (8) (9) 17/09/2012 24 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order First row expansion Formula (7) or (8) is the result obtained by expanding the determinant along the first row . In this method, in order to find the coefficient of b1 , say, we imagine the row and column containing b1 erased and the determinant of the remaining entries put down as they stand. Also we put the + sign to a1 (the upper-left entry), − sign to b1 (the one adjacent to a1 ), etc. so that the signs associated with the entries of the determinant appear alternately as shown below. Thus we have +a1 , −b1 , +c1 , −c2 , +b2 , etc. with + − + − + − + − + AMA1061 (By Catherine Liu) a1 c1 a2 b2 c2 . a3 overlaying on b1 b3 c3 Lecture 1 Matrices and Determinants 17/09/2012 25 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Row and Column expansion We can also get the same value of the determinant by expanding along any row or column. For example, if we expand along the second column, we get a1 b1 c1 a2 b2 c2 a3 b3 c3 a2 c2 = −b1 a1 c1 + b2 a3 c3 a1 c1 a2 c2 − b3 a3 c3 = −b1 (a2 c3 − a3 c2 ) + b2 (a1 c3 − a3 c1 ) − b3 (a1 c2 − a2 c1 ) = a1 b2 c3 + a3 b1 c2 + a2 b3 c1 − a3 b2 c1 − a1 b3 c2 − a2 b1 c3 . which is the same as (9). It can be proved that all values obtained by expansion along a row or column are equal. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 26 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Sarrus’ rule for 3rd order determinant: This rule says that we evaluated the determinant directly as a1 b1 c1 a2 b2 c2 a3 b3 c3 = a1 b2 c3 + b1 c2 a3 + c1 a2 b3 − a3 b2 c1 − b3 c2 a1 − c3 a2 b1 . The rule can be easily memorized using the following diagram. The six terms on the RHS are obtained by multiplying the entries following the arrows. A downward right arrow is associated with a positive sign while an upward right arrow a negative sign. a1 c1 a1 b1 a2 b2 c2 a2 b2 a3 AMA1061 (By Catherine Liu) b1 b3 c3 a3 b3 Lecture 1 Matrices and Determinants 17/09/2012 27 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Examples Example Evaluate the determinant 3 2 2 3 4 5 D= 1 3 1 by (a) expanding along the first row; (b) expanding along the second column; (c) expanding along the third row; (d) Sarrus’ rule. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 28 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Solution 3 4 3 (a) 1 2 4 5 D=3 1 2 3 5 3 +2 −1 (expansion along R1 ) = 3(−9) − (−18) + 2(−9) = −27. 2 (b) 4 D = −1 3 2 +3 5 3 2 2 4 −3 1 5 1 (expansion along C2 ) = −(−18) + 3(−7) − 3(8) = −27. 1 (c) 2 D=5 3 2 3 3 1 2 3 +1 −3 4 2 4 (expansion along R3 ) = 5(−2) − 3(8) + (7) = −27. (d) By Sarrus’ rule D = (3)(3)(1) + (1)(4)(5) + (2)(2)(3) − (5)(3)(2) − (3)(4)(3) − (1)(2)(1) = −27. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 29 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Basic properties 1. The value of a determinant is not changed if we put its rows as columns, in the same consecutive order, e.g. a1 b1 c1 a1 a2 a3 a2 b2 c2 = b1 b2 b3 a3 b3 c3 c1 c2 c3 (Transposition) 2. Interchanging any two columns (or any two rows) changes the sign of the determinant, e.g. a1 b1 c1 c1 b1 a1 a2 b2 c2 = − c2 b2 a2 a3 b3 c3 b3 a3 c3 (C1 ∼ C3 ) where the first and third columns (C1 , C3 ) of the first determinant have been interchanged to form the second determinant. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 30 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order 3. If a determinant has two rows (or two columns) identical, its value is zero, e.g. a1 b1 c1 a2 b2 c2 = 0 a2 b2 (R2 = R3 ) c2 since the second and third rows (R2 , R3 ) are identical. 4. A common factor of any row or column can be taken out to multiply the remaining determinant value, e.g. kma1 mc1 ka2 b2 ka3 AMA1061 (By Catherine Liu) mb1 a1 b1 c1 c2 = km a2 b2 c2 . b3 c3 a3 b3 c3 Lecture 1 Matrices and Determinants 17/09/2012 31 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order 5. If each element of a row (or a column) is the sum of two or more terms, the determinant can be expressed as the sum of two or more determinants, e.g. a1 + m1 b1 c1 a1 b1 c1 m1 b1 c1 a2 + m2 b2 c2 = a2 b2 c2 + m2 b2 c2 . a3 + m3 b3 c3 b3 c3 m3 b3 c3 a3 A more general formula is ka1 + lm1 b1 c1 a1 b1 c1 m1 b1 c1 ka2 + lm2 b2 c2 = k a2 b2 c2 + l m2 b2 c2 . ka3 + lm3 b3 c3 a3 b3 c3 b3 c3 AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants m3 17/09/2012 (10) 32 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order 6. By choosing l = −a3 /b3 (possible if b3 ∕= 0) so that a3 + lb3 = 0, we can simplify the determinant as a1 b1 c1 a1 + lb1 b1 c1 a2 b2 c2 = a2 + lb2 b2 c2 . a3 b3 c3 b3 c3 0 (C1 + l C2 → C1 ) Note that in the result, a3 is replaced by 0 since a3 + lb3 = 0. This result follows by putting k = 1 and mi = bi in (10). This helps to change one or more entries of the determinant to 0 without changing the value of the determinant. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 33 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Remark In No. 6 above, we used the abbreviation C1 + l C2 → C1 to represent the column operation: New column 1 is formed by adding old column 1 to l times column 2 For convenience, we will simply write C1 + l C2 to represent this operation in the future. The convention being used is: when we write k Ci + mCj (i ∕= j ) to represent a column operation, the result is used to replace Ci , the column being first written. Row operations k Ri + mRj (i ∕= j ) are defined similarly. If a determinant D is transformed to another determinant E by the operation: k Ci + mCj or k Ri + mRj (i ∕= j ), then E = kD . This fact follows from (10) above. AMA1061 (By Catherine Liu) Lecture 1 Matrices and Determinants 17/09/2012 34 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Example Example 2 3 23 1 6 16 3 Evaluate the determinant Δ = 8 38 . Solution 1. By expansion along a row or a column, or using Sarrus’ rule, we can get Δ = 0. For details, see Example 14 on page 28. Solution 2. 2 = 3 6 16 8 0 38 = −9 −9 1 6 16 0 23 1 3 Δ −10 0 −10 90 1 1 1 6 16 0 = 1 1 = 0. Solution 3. Obviously the third column is 10 times the first plus the second. Therefore 2 = 3 6 16 8 38 2 AMA1061 (By Catherine Liu) = 10 3 6 1 8 3 2 2 1 3 23 1 3 Δ + 3 3 1 6 6 3 8 8 Lecture 1 Matrices and Determinants = 10 × 0 + 0 = 0. 17/09/2012 35 / 46 Outline Introduction Determinants Basic Properties Cramer’s rule for linear equations Determinants of higher order Example 1 1 1 Factorize the determinant Δ = a3 b3 c3 a b ...
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