# CHARACTERISTIC ROOTS AND VECTORS

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1 CHARACTERISTIC ROOTS AND VECTORS 1 DEFINITION OF CHARACTERISTIC ROOTS AND VECTORS 11 Statement of the characteristic root problem Find values of a scalar λ for which there exist vectors x 0 satisfying Ax = λx (1) where A is a given nth order matrix The values of λ that solve the equation are called the characteristic roots or eigenvalues of the matrix A To solve the problem rewrite the equation as Ax = λx = λix (2) (λi A)x =0 x 0 For a given λ, any x which satisfies 1 will satisfy 2 This gives a set of n homogeneous equations in n unknowns The set of x s for which the equation is true is called the null space of the matrix (λi A) This equation can have a non-trivial solution iff the matrix (λi A) is singular This equation is called the characteristic or the determinantal equation of the matrix A To see why the matrix must be singular consider a simple 2x2 case First solve the system for x 1 Now substitute x 1 in the second equation Ax =0 a 11 x 1 + a 12 x 2 =0 a 21 x 1 + a 22 x 2 =0 x 1 = a 12 x 2 a 11 a 12 x 2 a 21 + a 22 x 2 =0 a ( 11 x 2 a 22 a ) 21 a 12 =0 a 11 ( x 2 = 0or a 22 a ) 21 a 12 =0 a 11 ( If x 2 0then a 22 a ) 21 a 12 =0 a 11 (3) (4) A =0 Date: September 16,

2 2 CHARACTERISTIC ROOTS AND VECTORS 12 Determinantal equation used in solving the characteristic root problem Now consider the singularity condition in more detail (λi A)x =0 λi A =0 λ a 11 a 12 a 1n a 21 λ a 22 a 2n =0 a n1 a n2 λ a nn (5) This equation is a polynomial in λ since the formula for the determinant is a sum containing n! terms, each of which is a product of n elements, one element from each column of A The fundamental polynomials are given as λi A = λ n + b n 1 λ n 1 + b n 2 λ n b 1 λ + b 0 (6) This is obvious since each row of λi A contributes one and only one power of λ as the determinant is expanded Only when the permutation is such that column included for each row is the same one will each term contain λ, giving λ n Other permutations will give lesser powers and b 0 comes from the product of the terms on the diagonal (not containing λ) of A with other members of the matrix The fact that b 0 comes from all the terms not involving λ implies that it is equal to A Consider a 2x2 example λi A = λ a 11 a 12 a 21 λ a 22 =(λ a 11 )(λ a 22 ) (a 12 a 21 ) = λ 2 a 11 λ a 22 λ + a 11 a 22 a 12 a 21 = λ 2 +( a 11 a 22 )λ + a 11 a 22 a 12 a 21 = λ 2 λ (a 11 + a 22 )+(a 11 a 22 a 12 a 21 ) (7) = λ 2 + b 1 λ + b 0 b 0 = A Consider also a 3x3 example where we find the determinant using the expansion of the first row λ a 11 a 12 a 13 λi A = a 21 λ a 22 a 23 a 31 a 32 λ a 33 =(λ a 11 ) λ a 22 a 23 a 32 λ a 33 + a 12 a 21 a 23 a 31 λ a 33 a 13 a 21 λ a 22 a 31 a 32 Now expand each of the three determinants in equation 8 We start with the first term (8)

3 CHARACTERISTIC ROOTS AND VECTORS 3 (λ a 11) λ a22 a23 a 32 λ a 33 =(λ a11) [ λ 2 ] λa 33 λa 22 + a 22 a 33 a 23 a 32 =(λ a [ 11) λ 2 λ ( a 33 + a 22 )+a 22a 33 a ] 23a 32) = λ 3 λ 2 (a 33 + a 22)+λ (a 22a 33 a 23a 32) λ 2 a 11 + λa 11 (a 33 + a 22) a 11 (a 22a 33 a 23a 32) = λ 3 λ 2 (a 11 + a 22 + a 33)+λ (a 11 a 33 + a 11a 22 + a 22a 33 a 23a 32) a 11a 22a 33 + a 11a 23a 32 (9) Now the second term a a 21 a a 31 λ a 33 = a 12 [ λa 21 + a 21 a 33 a 23 a 31 ] (10) = λa 12 a 21 + a 12 a 21 a 33 a 12 a 23 a 31 Now the third term a 13 a 21 λ a 22 a 31 a 32 Now combine the three expressions to obtain λ a 11 a 12 a 13 λi A = a 21 λ a 22 a 23 a 31 a 32 λ a 33 = a 13 [a 21 a 32 + λa 31 a 22 a 31 ] (11) = a 13 a 21 a 32 λa 13 a 31 + a 13 a 22 a 31 = λ 3 λ 2 (a 11 + a 22 + a 33 )+λ (a 11 a 33 + a 11 a 22 + a 22 a 33 a 23 a 32 ) a 11 a 22 a 33 + a 11 a 23 a 32 λa 12 a 21 + a 12 a 21 a 33 a 12 a 23 a 31 a 13 a 21 a 32 λa 13 a 31 + a 13 a 22 a 31 = λ 3 λ 2 (a 11 + a 22 + a 33 )+λ (a 11 a 33 + a 11 a 22 + a 22 a 33 a 23 a 32 a 12 a 21 a 13 a 31 ) a 11 a 22 a 33 + a 11 a 23 a 32 + a 12 a 21 a 33 a 12 a 23 a 31 a 13 a 21 a 32 + a 13 a 22 a 31 (12) The first term will be λ 3, the others will give polynomials in λ 2, and λ Note that the constant term is the negative of the determinant of A 13 Fundamental theorem of algebra 131 Statement of Fundamental Theorem Theorem of Algebra Theorem 1 Any polynomial p(x) of degree at least 1, with complex coefficients has at least one zero z (ie, z is a root of the equation p(x) = 0 among the complex numbers Further, if z is a zero of p(x), then x - z divides p(x) and p(x) = (x-z)q(x), where q(x) is a polynomial with complex coefficients, whose degree is 1 smaller than p What this says is that we can write the polynomial as a product of (x-z) and a term which is a polynomial of one less degree than p(x) For example if p(x) is a polynomial of degree 4 then we can write it as p(x) = (x x 1 ) (polynomial with power no greater than 3) (13)

4 4 CHARACTERISTIC ROOTS AND VECTORS where x 1 is a root of the equation Given that q(x) is a polynomial and if it is of degree greater than one, then we can write it in a similar fashion as q(x) = (x x 2 ) (polynomial with power no greater than 2) (14) where x 2 is a root of the equation q(x) = 0 This then implies that p(x) can be written as p(x) = (x x 1 )(x x 2 ) (polynomial with power no greater than 2) (15) or continuing p(x) = (x x 1 )(x x 2 )(x x 3 ) (polynomial with power no greater than 1) (16) p(x) = (x x 1 )(x x 2 )(x x 3 )(x x 4 ) (term not containing x) (17) If we set this equation equal to zero, it implies that p(x) = (x x 1 )(x x 2 )(x x 3 )(x x 4 ) = 0 (18) 132 Example of Fundamental Theorem Theorem of Algebra Consider the equation p(t) = t 3 6t 2 +11t 6 (19) This has roots t 1 =1,t 2 =2,t 3 =3 Consider then that we can write the equation as (t-1)q(t) as follows p(t) = (t 1)(t 2 5t +6) = t 3 5t 2 +6t t 2 +5t 6 = t 3 6t 2 +11t 6 (20) Now carry this one step further as q(t) = t 2 5t +6 =(t 2)s(t) = (t 2)(t 3) p(t) = (t t 1 )(t t 2 )(t t 3 ) (21) 133 Theorem that Follows from the Fundamental Theorem Theorem of Algebra Theorem 2 A polynomial of degree n 1 with complex coefficients has, counting multiplicities, exactly n zeroes among the complex numbers The multiplicity of a root z of p(x) = 0 is the largest integer k for which (x-z) k divides p(x), that is, the number of times z occurs as a root of p(x) = 0 If z has a multiplicity of 2, then it is counted twice (2 times) toward the number n of roots of p(x) = 0

5 CHARACTERISTIC ROOTS AND VECTORS First example of theorem 2 Consider the polynomial p(t) = t 3 6t 2 +11t 6 (22) If we divide this by (t-1) we obtain p(t) (t 1) = (t3 6t 2 +11t 6) (t 1) = t 2 5t +6 =(t 2)(t 3) which is not divisible by (t-1), so the multiplicity of the root 1 is 1 (23) 135 Second example of theorem 2 Consider the polynomial One root of this is 2 because p(t) = t 3 5t 2 +8t 4 (24) p(t) =t 3 5t 2 +8t 4 If we divide the polynomial by (t-2) we obtain = = = 0 p(t) (t 2) = t3 5t 2 +8t 4 (t 2) = t 2 3t +2 =(t 2)(t 1) which is divisible by (t-2) so the multiplicity of the root 2 is 2 (25) (26) 136 factoring nth degree polynomials An nth degree polynomial f(λ) can be written in factored form using the solutions of the equation f(λ) =0as f( λ ) = (λ λ 1 )(λ λ 2 ) (λ λ n ) = 0 n = ( λ λi ) = 0 i=1 If we multiply this out for the special case when the polynomial is of degree 4 we obtain (27) f( λ ) = (λ λ 1 )(λ λ 2 )(λ λ 3 )(λ λ 4 ) = 0 (λ λ 1 ) term containing no power of λ larger than 3 = 0 λ [(λ λ 2 )(λ λ 3 )(λ λ 4 ] λ 1 [(λ λ 2 )(λ λ 3 )(λ λ 4 ] = 0 Examining equation 27 or equation 28, we can see that we will eventually end up with a sum of n terms, the first being λ 4, the second being λ 3 multiplied by a term involving λ 1,λ 2,λ 3, and λ 4, the third being λ 2 multiplied by a term involving λ 1,λ 2,λ 3, and λ 4, and so on until the last term which will be 4 i=1 λi Completing the computations for the 4th degree polynomial will yield (28)

6 6 CHARACTERISTIC ROOTS AND VECTORS f( λ ) = λ 4 + λ 3 ( λ 1 λ 2 λ 3 λ 4 ) + λ 2 (λ 1 λ 2 + λ 1 λ 3 + λ 2 λ 3 + λ 1 λ 4 λ 2 λ 4 + λ 3 λ 4 ) + λ( λ 1 λ 2 λ 3 λ 1 λ 2 λ 4 λ 1 λ 3 λ 4 λ 2 λ 3 λ 4 )+(λ 1 λ 2 λ 3 λ 4 ) = 0 = λ 4 λ 3 (λ 1 + λ 2 + λ 3 + λ 4 ) (29) + λ 2 (λ 1 λ 2 + λ 1 λ 3 + λ 2 λ 3 + λ 1 λ 4 + λ 2 λ 4 + λ 3 λ 4 ) λ(λ 1 λ 2 λ 3 + λ 1 λ 2 λ 4 + λ 1 λ 3 λ 4 + λ 2 λ 3 λ 4 )+(λ 1 λ 2 λ 3 λ 4 ) = 0 We can write this in an alternative way as follows f( λ ) =λ 4 λ 3 Σ 4 i=1 λ i + λ 2 Σ i j λ i λ j λ 1 Σ i j k λ i λ j λ k (30) +( 1) 4 Π 4 i=1 λ i = 0 Similarly for polynomials of degree 2, 3 and 5, we find f( λ ) = λ 2 λ Σ 2 i=1 λ i +( 1) 2 Π 2 i=1 λ i = 0 (31a) f( λ ) = λ 3 λ 2 Σ 3 i=1 λ i + λ Σ i j λ i λ j (31b) +( 1) 3 Π 3 i=1 λ i = 0 f( λ ) = λ 5 λ 4 Σ 5 i=1 λ i + λ 3 Σ i j λ i λ j (31c) Now consider the general case λ 2 Σ i j k λ i λ j λ k + λ 1 Σ i j k l λ i λ j λ k λ l +( 1) 4 Π 4 i=1 λ i = 0 f( λ ) = (λ λ 1 )(λ λ 2 ) (λ λ n ) = 0 (32) Each product contributing to the power λ k in the expansion is the result of k times choosing λ and n k times choosing one of the λ i For example, with the first term of a third degree polynomial, we choose λ three times and do not choose any of the λ i For the second term we choose λ twice and each of the λ i once When we choose λ once we choose each of the λ i twice The term not involving λ implies that we choose all of the λ i This is of course (λ 1 λ 2,λ 3 ) Choosing λktimes and λ i (n k) times is a problem in combinatorics The answer is given by the binomomial formula,

7 CHARACTERISTIC ROOTS AND VECTORS 7 specifically there are ( n n k) products with the power λ k In a cubic, when k = 3, there are no terms involving λ i, when k = 2 there will three terms involving the individual λ i, when k = 1 there will three terms involving the product of two of the λ i, and when k = 0 there will one term involving the product of three of the λ i as can be seen in equation 33 f( λ ) =(λ λ 1 )(λ λ 2 )(λ λ 3 ) =(λ 2 λλ 1 λλ 2 + λ 1 λ 2 )(λ λ 3 ) = λ 3 λ 2 λ 1 λ 2 λ 2 + λλ 1 λ 2 λ 2 λ 3 + λλ 1 λ 3 + λλ 2 λ 3 λ 1 λ 2 λ 3 (33) = λ 3 λ 2 (λ 1 + λ 2 + λ 3 )+λ (λ 1 λ 2 + λ 1 λ 3 + λ 2 λ 3 ) λ 1 λ 2 λ 3 The coefficient of λ k will be the sum of the appropriate products, multiplied by 1 if an odd number of λ i have been chosen The general case follows f( λ ) =(λ λ 1 )(λ λ 2 ) (λ λ n ) = λ n λ n 1 Σ n i=1 λ i + λ n 2 Σ i j λ i λ j λ n 3 Σ i j k λ i λ j λ k (34) + + λ n 4 Σ i j k l λ i λ j λ k λ l +( 1) n Π n i=1 λ i = 0 The term not containing λ is always ( 1) n times the product of the roots (λ 1,λ 2,λ 3,) of the polynomial and term containing λ n 1 is always ( n i=1 λ i ) 137 Implications of factoring result for the fundamental determinantal equation 6 The fundamental equations for computing characteristic roots are (λi A)x =0 (35a) λi A =0 (35b) λi A = λ n + b n 1 λ n 1 + b n 2 λ n b 1 λ + b 0 =0 (35c) Equation 35c is just a polynomial in λ If we solve the equation, we will obtain n roots (some perhaps repeated) Once we find these roots, we can also write equation 35c in factored form using equation 34 It is useful to write the two forms together λi A =λ n +b n 1 λ n 1 +b n 2 λ n b 1 λ + b 0 =0 (36a) λi A =λ n λ n 1 Σ n i=1 λ i +λ n 2 Σ i j λ i λ j + +( 1) n Π n i=1 λ i =0 (36b) We can then find the coefficients of the various powers of λ by comparing the two equations For example, b n 1 = Σ n i=1 λ i and b 0 =( 1) n Π n i=1 λ i

8 8 CHARACTERISTIC ROOTS AND VECTORS 138 Implications of theorem 1 and theorem 2 The n roots of a polynomial equation need not all be different, but if a root is counted the number of times equal to its multiplicity, there are n roots of the equation Thus there are n roots of the characteristic equation since it is an nth degree polynomial These roots may all be different or some may be the same The theorem also implies that there can not be more than n distinct values of λ for which λi A =0 For values of λ different than the roots of f(λ), solutions of the characteristic equation require x = 0 If we set λ equal to one of the roots of the equation, say λ i, then λ i I A =0 For example, consider a matrix A as follows A = ( ) (37) This implies that λi A = λ λ 4 (38) Taking the determinant we obtain λi A =(λ 4)(λ 4) 4 = λ 2 8 λ +12=0 (λ 6) (λ 2) = 0 λ 1 =6,λ 2 = 2 (39) Now write out equation 39 with λ 1 = 2 as follows A = ( ) I A = = =0 (40) 14 A numerical example of computing characteristic roots (eigenvalues) Consider a simple example matrix

9 CHARACTERISTIC ROOTS AND VECTORS 9 A = λi A = ( ) λ λ 5 =(λ 4)(λ 5) 6 = λ 2 4λ 5λ (41) =λ 2 9λ +14=0 (λ 7) (λ 2) = 0 λ 1 =7,λ 2 =2 15 Characteristic vectors Now return to the general problem Values of λ which solve the determinantal equation are called the characteristic roots or eigenvalues of the matrix A Once λ is known, we may be interested in vectors x which satisfy the characteristic equation In examining the general problem in equation 1, it is clear that if x satisfies the equation for a given λ, so will cx, where c is a scalar Thus x is not determined To remove this indeterminacy, a common practice is to normalize the vector x such that x x = 1 Thus the solution actually consists of λ and n-1 elements of x If there are multiple values of λ, then there will be an x vector associated with each 16 Computing characteristic vectors (eigenvectors) To find a characteristic vector, substitute a characteristic root in the determinantal equation and then solve for x The solution will not be determinate, but will require the normalization Consider the example in equation 41 with λ 1 =2 Making the substitution and writing out the determinantal equation will yield ( ) 4 2 A = 3 5 ( ) [λi A]x = x (42) ( )( ) 2 2 x1 = = x 2 Now solve the system of eqautions for x 1 2x 1 2x 2 =0 3x 1 3x 2 =0 x 1 = x 2 and x x 2 2 = 1 Substitute for x 1 in equation 42 ( x 2 ) 2 + x 2 2 =1 2x 2 2 =1 x 2 2 = 1 2 x 2 = 1 2,x 1 = 1 2 (43) (44)

10 10 CHARACTERISTIC ROOTS AND VECTORS Substitute the characteristic vector from equation 44 in the fundamental relationship given in equation 1 ) ( ) ( ) = Similarly for λ 2 =7 ( 4 2 Ax = 3 5 ) = = ( ( ) (45) ( ) 4 2 A = 3 5 ( ) [λi A]x = x ( )( ) 3 2 x1 = = x 2 3x 1 2x 2 =0 3x 1 +2x 2 =0 (46) Substituting we obtain x 1 = 2 3 x 2 and x x2 2 = x2 2 + x 2 2 = x2 2 =1 x 2 2 = 9 13 x 2 = 3,x 1 = 2 13 Ax = ( ) ) ( = ( ) (47) λx =7x = ( )

11 CHARACTERISTIC ROOTS AND VECTORS Definition of Similar Matrices 2 SIMILAR MATRICES Theorem 3 Let A be a square matrix of order n and let Q be an invertible matrix of order n Then B = Q 1 AQ (48) has the same characteristic roots as A, and if x is a characteristic vector of A, then Q 1 x is a characteristic vector of B The matrices A and B are said to be similar Proof Let (λ, x) be a characteristic root and vector pair for the matrix A Then Ax = λx Q 1 Ax = λq 1 x Also Q 1 A = Q 1 AQQ 1 So [Q 1 AQQ 1 ]x = λ [Q 1 x] (49) Q 1 AQ [Q 1 x] =λ [Q 1 x] This then implies that λ is a characteristic root of Q 1 AQ, with characteristic vector Q 1 x 22 Matrices similar to diagonal matrices 221 Diagonizable matrix A matrix A is said to be diagonalizable if it is similar to a diagonal matrix This means that there exists a matrix P such that P 1 AP = D, where D is a diagonal matrix 222 Theorem on diagonizable matrices Theorem 4 The nxn matrix A is diagonalizable iff there is a set of n linearly independent vectors, each of which is a characteristic vector of A Proof First show that if we have n linearly independent characteristic vectors, A is diagonalizable Take the n linearly independent characteristic vectors of A, denoted x 1,x 2, x n, with their characteristic roots, λ 1, λ n and form a nonsingular matrix P with them as columns So we have P =[x 1 x 2 x n ] = x 1 1 x 2 1 x n 1 x 1 2 x 2 2 x n 2 x 1 3 x 2 3 x n 3 x 1 n x 2 n x n n (50) Let Λ be a diagonal matrix with the characteristic roots of A on the diagonal Now calculate P 1 AP as follows

12 12 CHARACTERISTIC ROOTS AND VECTORS P 1 AP = P 1 [Ax 1 Ax 2 Ax n ] = P 1 [λ 1 x 1 λ 2 x 2 λ n x n ] λ = P 1 [x 1 x 2 x n 0 λ ] λ n = P 1 [x 1 x 2 x n ]Λ (51) = P 1 P Λ = Λ Now suppose that A is diagonalizable so that P 1 AP = D Then multiplying both sides by P we obtain AP = PD This means that A times the ith column of P is the ith diagonal entry of D times the ith column of P This means that the ith column of P is a characteristic vector of A associated with the ith diagonal entry of D Thus D is equal to Λ Since we assume that P is non-singular, the columns (characteristic vectors) are independent 3 INDEPENDENCE OF CHARACTERISTIC VECTORS Theorem 5 Suppose λ 1,,λ k are characteristic roots of an nxn matrix A, no two of which are the same Let x i be the characteristic vector associated with λ i Then the set [x 1, x 2,, x k ] is linearly independent Proof Suppose x 1,, x k are linearly dependent Then there exists a nontrivial linear combination such that x 1 1 x 2 x 1 1 x a x 2 1 x k a x a x k 2 k = 0 (52) a 1 x 1 n x 2 n As an example the sum of the first two vectors might be zero In this case a 1 =1,a 2 = 1, and a 3,, a k = 0 Consider the linear combination of the x s that has the fewest non-zero coefficients Renumber the vectors such that these are the first r Call the coefficients α That is x 3 n α 1 x 1 + α 2 x α r x r = 0for r k, and α r+1,, α k = 0 (53) Notice that r>1, because all x i 0 Now multiply the matrix equation in 53 by A A(α 1 x 1 + α 2 x α r x r )=α 1 Ax 1 + α 2 Ax α r Ax r (54) = α 1 λ 1 x 1 + α 2 λ 2 x α r λ r x r = 0 Now multiply the result in 53 by λ r and subtract from the last expression in 54 First multiply 53 by λ r x k n Now subtract λ r [ α1 x 1 + α 2 x α r x r ] = λ r α 1 x 1 + λ r α 2 x λ r α r x r = α 1 λ r x 1 + α 2 λ r x α r λ r x r = 0 (55)

13 CHARACTERISTIC ROOTS AND VECTORS 13 α 1λ 1x 1 + α 2λ 2x α r 1λ r 1x r 1 + α rλ rx r = 0 α 1λ rx 1 + α 2λ rx α r 1λ rx r 1 +α rλ rx r = 0 α 1(λ 1 λ r)x 1 + α 2(λ 2 λ r)x r + + α r 1(λ r 1 λ r)x r 1 + α r(λ r λ r)x r = 0 α 1(λ 1 λ r)x 1 + α 2(λ 2 λ r)x α r 1(λ r 1 λ r)x r 1 = 0 (56) This dependence relation has fewer coefficients than 53, thus contradicting the assumption that 53 contains the minimal number Thus the vectors x 1,, x k must be independent 4 DISTINCT CHARACTERISTIC ROOTS AND DIAGONALIZABILITY Theorem 6 If an nxn matrix A has n distinct characteristic roots, then it is diagonalizable Proof Let the distinct characteristic roots of A be given by λ 1,, λ n Since they are all different, x 1,, x n is a linearly independent set by the last theorem Then A is diagonalizable by the Theorem 4 5 DETERMINANTS, TRACES, AND CHARACTERISTIC ROOTS 51 Theorem on determinants Theorem 7 Let A be a square matrix of order n Let λ 1,, λ n be its characteristic roots Then A = Π n i=1 λ i (57) Proof Consider the n roots of the equation defining the characteristic roots Consider the general equation for the roots of an equation (as in equation 6, the example in equation 12 or equation 34 ) and the specific case here λi A = (λ λ 1)(λ λ 2) (λ λ n) =0 λ n + λ n 1 (terms involving λ i )+ + λ (terms involving λ i )+ n ( λ i ) =0 λ n λ n 1 Σ n 1 i=1 λ i + λ n 2 Σ i =jλ iλ j λ n 3 Σ i =j =kλ iλ jλ k i=1 (58) + λ n 4 Σ i =j =k =lλ iλ jλ kλ l +( 1) n Π n i=1 λ i =0 The first term is λ n and the last term contains only the λ i This last term is given by Π n i=1 ( λ i ) = ( 1) n Π n i=1λ i (59) Now compare equation 6 and equation 58 (6) λi A = λ n + b n 1 λ n 1 + b n 2 λ n b 0 =0 (60a) (58) λi A = λ n λ n 1 Σ n 1 i=1 λ i +λ n 2 Σ i j λ i λ j +( 1) n Π n i=1 λ i =0 (60b) Notice that the last expression in 58 must be equivalent to the b 0 in equation 6 because it is the only term not containing λ Specifically, b 0 = ( 1) n Π n i=1 λ i (61)

14 14 CHARACTERISTIC ROOTS AND VECTORS Now set λ = 0 in 60a This will give Therefore λi A = λ n + b n 1 λ n 1 + b n 2 λ n b 0 =0 A = b 0 (62) ( 1) n A = b 0 b 0 =( 1) n Π n i=1 λ i = ( 1) n A = b 0 ( 1) n Π n i=1 λ i =( 1) n A Π n i=1 λ i = A (63) 52 Theorem on traces Theorem 8 Let A be a square matrix of order n Let λ 1,, λ n be its characteristic roots Then tr A = Σ n i=1 λ i (64) To help visualize the proof, consider the following case where n = 4 λ a 11 a 12 a 13 a 14 λi A = a 21 λ a 22 a 23 a 24 a 31 a 32 λ a 33 a 34 a 41 a 42 a 43 λ a 44 λ a 22 a 23 a 24 =(λ a 11 ) a 32 λ a 33 a 34 a 42 a 43 λ a 44 + a a 21 a 23 a a 31 λ a 33 a 34 a 41 a 43 λ a 44 a 21 λ a 22 a 24 +( a 13 ) a 31 a 32 a 34 a 41 a 42 λ a 44 + a a 21 λ a 22 a a 31 a 32 λ a 33 a 41 a 42 a 43 Also consider expanding the determinant λ a 22 a 23 a 24 a 32 λ a 33 a 34 (66) a 42 a 43 λ a 44 by its first row (λ a 22 ) λ a 33 a 34 a 43 λ a 44 + a 23 a 32 a 34 a 42 λ a 44 +( a 24) a 32 λ a 33 a 42 a 43 (67) Now proceed with the proof Proof Expanding the determinantal equation (5) by the first row we obtain λi A = f(λ) = (λ a 11 )C 11 +Σ n j=2 ( a 1j )C 1j (68) Here, C 1j is the cofactor of a 1j in the matrix λi A Note that there are only n-2 elements involving a ii - λ in the matrices C 12, C 13, C 1n Given that there are only n-2 elements involving (65)

15 CHARACTERISTIC ROOTS AND VECTORS 15 a ii - λ in the matrices C 1j,,C 1n, when we later expand these matrices, there will be no powers of λ greater than n-2 This then means that we can write 68 as λi A = (λ a 11 )C 11 +[terms of degree n 2 or less in λ] (69) We can then make similar arguments about C 11 and C 22 and so on and thus obtain λi A = (λ a 11 )(λ a 22 ) (λ a nn )+[terms of degree n-2 or less in λ ] (70) Consider the first term in equation 70 It is the same as equation 34 with a 11 replacing λ i We can therefore write this first term as follows (λ a 11 )(λ a 22 ) (λ a nn ) = λ n λ n 1 Σ n i=1 a ii + λ n 2 Σ i j a ii a jj λ n 3 Σ i j k a ii a jj a kk + λ n 4 Σ i j k l a ii a jj a kk a ll (71) + Substituting in equation 70, we obtain +( 1) n Π n i=1 a ii λi A =(λ a 11 )(λ a 22 ) (λ a nn )+[terms of degree n 2 or less in λ] = λ n λ n 1 Σ n i=1 a ii + λ n 2 Σ i j a ii a jj λ n 3 Σ i j k a ii a jj a kk + λ n 4 Σ i j k l a ii a jj a kk a ll + (72) +( 1) n Π n i=1 a ii +[other terms of degree n-2 or less in λ ] Now the compare the coefficient of ( λ n 1 ) in equation 72 with that in equation 36b or 60b which we repeat here for convenience λi A = λ n λ n 1 Σ n 1 i=1 λ i +λ n 2 Σ i j λ i λ j +( 1) n Π n i=1 λ i (73) It is clear from this comparison that Σ n i=1 a ii = Σ n i=1 λ i (74) Note that this expansion provides an alternative proof of theorem 7 61 Statement of theorem 6 TRANSPOSES AND INVERSES Theorem 9 Let A be a square matrix of order n with characteristic roots λ i, i = 1,,n Then 1: The characteristic roots of A are the same as those of A 2: If A is nonsingular, the characteristic roots of A 1 are given by µ i = 1 λ i, i = 1,,n

16 16 CHARACTERISTIC ROOTS AND VECTORS 62 proof of part 1 Proof The characteristic roots of A and A are the solutions of the equations λi A =0 (75a) θi A =0 (75b) Now note that θi A = (θi A) Furthermore it is clear from the definition of a determinant that B = B since the permutations could as well be taken over rows as columns This means then that (θi A) = θi A which implies that θ i = λ i 63 proof of part 2 Proof Write the characteristic equation of A 1 as µi A 1 = 0 (76) Now rewrite the matrix µi -A 1 in the following fashion µi A 1 = A 1 (µa I) postmultiply each term by A and then premultiply =( µ) A 1 ( 1 µ µa + 1 µ I) the whole expression by A 1 ( ) 1 multiply each term inside the parentheses by µ and premultiply whole expression by ( µ) =( µ) A 1 ( A + 1 µ I) simplify =( µ) A 1 ( 1 µ I A) commute (77) Now take the determinant of both sides of equation 77 µi A ( ) 1 = 1 µa 1 µ I A ( ) = 1 µa 1 µ I A (78) =( 1) n µ n A 1 λi A, where λ = 1 µ If µ = 0, then µi A 1 = A 1 = ( 1) n A 1 Because the matrix is A nonsingular, A 1 is also non-singular This imples that µ cannot equal zero, so µ = 0 is not a root of the equation Thus the expression on the right hand side of 78 can be zero iff λi A = 0, where λ = 1 µ Thus if µ i are the roots of A 1 we must have that µ j = 1, λ j j =1, 2,, n (79)

17 CHARACTERISTIC ROOTS AND VECTORS 17 7 ORTHOGONAL MATRICES 71 Orthogonal and orthonormal vectors and matrices Definition 1 The nx1 vectors a and b are orthogonal if Definition 2 The nx1 vectors a and b are orthonormal if a b = 0 (80) a b =0 a a =1 b b =1 (81) Definition 3 The square matrix Q (nxn) is orthogonal if its columns are orthonormal Specifically Q = ( x 1 x 2 x 3 x ) n x i x i =1 x i x j =0i j (82) A consequence of the definition is that Q Q = I (83) 72 Non-singularity property of orthogonal matrices Theorem 10 An orthogonal matrix is nonsingular Proof If the columns of the matrix are independent then it is nonsingular Consider the matrix Q given by q1 1 q1 2 q n 1 q2 1 q2 2 q n 2 Q = [q 1 q 2 q n ] = q3 1 q3 2 q3 n (84) qn 1 qn 2 qn n If the columns are dependent then, a 1 q 1 1 q 2 1 q a q a q a n q 3 1 q n 1 q n 2 = 0, a i 0for at least one i (85) q 1 n q 2 n q 3 n q n n Now premultiply the dependence equation by the transpose of one of the columns of Q, say q j

18 18 CHARACTERISTIC ROOTS AND VECTORS a 1 [q j 1 q j 2 q j n ] q 1 1 q 2 1 q a 2 [q j 1 q j 2 qn j ] q 2 2 q 1 n + + a n [q j 1 q j 2 qn j ] q n 2 = 0,a i q n 1 q 2 n 0for at least one i (86) By orthogonality all the terms involving j and i j are zero This then implies q n n q j 1 q j 2 a j [q j 1 q j 2 qn j ] q j n = 0 (87) But because the columns are orthonormal q j q j = 1 which implies that a j = 0 Because j is arbitrary this implies that all the a j are zero Thus the columns are independent and the matrix has an inverse 73 Tranposes and inverses of orthogonal matrices Theorem 11 If Q is an orthogonal matrix, then Q = Q 1 Proof By the definition of an orthogonal matrix Q Q = I Now postmultiply the identity by Q 1 It exists by Theorem Determinants and characteristic roots of orthogonal matrices Theorem 12 If Q is an orthogonal matrix of order n,then a: Q =1or Q = 1 b: If λ i is a characteristic root of Q, then λ i = ±1, i=1,,n Proof To show note that Q Q = I Then Q Q = I Q QQ 1 = IQ 1 (88) Q = Q 1 1 = I = Q Q = Q Q (89) = Q 2 because Q = Q To show the second part note that Q = Q 1 Also note that the characteristic roots of Q are the same as the roots of Q By theorem 9, the characteristic roots of Q 1 are (1/λ i ) This implies then that the roots of Q are the same as Q 1 This means then that λ i =1/λ i This can be true iff λ i = ±1

19 CHARACTERISTIC ROOTS AND VECTORS 19 8 A DIGRESSION ON COMPLEX NUMBERS 81 Definition of a complex number A complex number is an ordered pair of real numbers denoted by (x 1, x 2 ) The first member, x 1, is called the real part of the complex number; the second member, x 2, is called the imaginary part We define equality, addition, subtraction, and multiplication so as to preserve the familiar rules of algebra for real numbers 811 Equality of complex numbers Two complex numbers x = (x 1, x 2 ) and y = (y 1, y 2 ) are called equal iff x 1 = y 1, and x 2 = y 2 (90) 812 Sum of complex numbers The sum of two complex numbers x + y is defined as x + y =(x 1 + y 1,x 2 + y 2 ) (91) 813 Difference of complex numbers To subtract two complex numbers, the following rule applies 814 Product of complex numbers The product xy is defined as x y =(x 1 y 1,x 2 y 2 ) (92) xy =(x 1 y 1 x 2 y 2,x 1 y 2 + x 2 y 1 ) (93) The properties of addition and multiplication defined satisfy the commutative, associative and distributive laws 82 The imaginary unit The complex number (0,1) is denoted by i and is called the imaginary unit We can show that i 2 = -1 as follows i 2 =(0, 1)(0, 1) = (0 1, 0+0)=( 1, 0) = 1 (94) 83 Representation of a complex number A complex number x = (x 1, x 2 ) can be written in the form x = x 1 + ix 2 (95) Alternatively a complex number z = (x,y) is sometimes written z = x + iy (96) 84 Modulus of a complex number The modulus or absolute value of a complex number x = (x 1, x 2 ) is the nonnegative real number x given by x = x x2 2 (97)

20 20 CHARACTERISTIC ROOTS AND VECTORS 85 Complex conjugate of a complex number For each complex number z = x + iy, the number z = x - iy is called the complex conjugate of z The product of a complex number and its conjugate is a real number In particular, if z = x + iy then zz =(x, y)(x, y) =(x 2 + y 2, xy + yx) =(x 2 + y 2, 0) = x 2 + y 2 (98) Sometimes we will use the notation z to represent the complex conjugate of a complex number So z = x - iy We can then write z z =(x, y)(x, y) =(x 2 + y 2, xy + yx) =(x 2 + y 2, 0) = x 2 + y 2 (99) 86 Graphical representation of a complex number Consider representing a complex number in a two dimensional graph with the vertical axis representing the imaginary part In this framework the modulus of the complex number is the distance from the origin to the point This is seen clearly in figure 1 FIGURE 1 Graphical Representation of Complex Number Imaginary axis x Sqrt x 1^2 x 2^2 x x 1 ix 2 x 2 0 x 1 Real axis 87 Polar form of a complex number We can represent a complex number by its angle and distance from the origin Consider a complex number z 1 = x 1 +iy 1 Now consider the angle θ 1 which the ray from the origin to the point z 1 makes with the x axis Let the modulus of z be denoted by r 1 Then cos θ 1 = x 1 /r 1 and sin θ 1 = y 1 /r 1 This then implies that z 1 = x 1 + iy 1 = r 1 cos θ 1 + ir 1 sin θ 1 = r 1 (cos θ 1 + i sin θ 1 ) Figure 2 shows how a complex number is represented in polar corrdinates (100) 88 Complex Exponentials The exponential e x is a real number We want to define e z when z is a complex number in such a way that the principle properties of the real exponential function will be preserved The main properties of e x, for x real, are the law of exponents, e x1 e x2 =e x1+x2 and the equation e 0 = 1 If we want the law of exponents to hold for complex numbers, then it must be that

21 CHARACTERISTIC ROOTS AND VECTORS 21 FIGURE 2 Graphical Representation of Complex Number Imaginary axis z 1 r 1 cos Θ 1 i sin Θ 1 y 1 Θ 1 0 x 1 Real axis e z = e x+iy = e x e iy (101) We already know the meaning of e x We therefore need to define what we mean by e iy Specifically we define e iy in equation 102 Definition 4 e iy = cos y + i sin y (102) With this in mind we can then define e z =e x+iy as follows Definition 5 e z = e x e iy = e x (cos y + i sin y) (103) Obviously if x = 0 so that z is a pure imaginary number this yields e iy = (cos y + i sin y) (104) It is easy to show that e 0 = 1 If z is real then y = 0 Equation 105 then becomes e z = e x e i 0 = e x (cos (0) + i sin (0) ) (105) = e x e i 0 = e x (1+0)=e x So e 0 obviously is equal to 1 To show that e x e iy =e x + iy or e z1 e z2 =e z1 + z2 we will need to remember some trigonometric formulas Theorem 13 sin(φ + θ) = sin φ cos θ + cos φ sin θ (106a) sin(φ θ) = sin φ cos θ cos φ sin θ cos(φ + θ) = cos φ cos θ sin φ sin θ cos(φ θ) = cos φ cos θ + sin φ sin θ (106b) (106c) (106d)

22 22 CHARACTERISTIC ROOTS AND VECTORS Now to the theorem showing that e z1 e z2 z1 + z2 =e Theorem 14 If z 1 = x 1 + iy t and z 2 = x 2 + iy 2 are two complex numbers, then we have e z1 e z2 =e z1+z2 Proof Now e x1 e x2 e z1 = e x1 (cos y 1 + i sin y 1 ),e z2 = e x2 (cos y 1 + i sin y 2 ), e z1 e z2 = e x1 e x2 [cos y 1 cos y 2 sin y 1 sin y 2 + i(cos y 1 sin y 2 + sin y 1 cos y 2 )] = e x1+x2, since x 1 and x 2 are both real Also, (107) and and hence cos y 1 cos y 2 sin y 1 sin y 2 = cos (y 1 + y 2 ) (108) cos y 1 sin y 2 + sin y 1 cos y 2 = sin (y 1 + y 2 ), (109) e z1 e z2 = e x1+x2 [cos (y 1 + y 2 )+isin (y 1 + y 2 ) = e z1+z2 (110) Now combine the results in equations 100, 105 and 104 to obtain the polar representation of a complex number z 1 = x 1 + iy 1 = r 1 (cos θ 1 + i sin θ 1 ) = r 1 e iθ1, (by equation 102) The usual rules for multiplication and division hold so that (111) z 1 z 2 = r 1 e iθ1 r 2 e iθ2 i(θ1+ θ2) = (r 1 r 2 ) e z 1 = r ( ) 1 e iθ1 r1 (112) = e i(θ1 θ2) r 2 e iθ2 z 2 89 Complex vectors We can extend the concept of a complex number to complex vectors where z = x + iy is a vector as are the components x and y In this case the modulus is given by r 2 z = x x + y y = (x x + y y) 1 2 (113) 9 SYMMETRIC MATRICES AND CHARACTERISTIC ROOTS 91 Symmetric matrices and real characteristic roots Theorem 15 If S is a symmetric matrix whose elements are real, its characteristic roots are real Proof Let λ = λ 1 +iλ 2 be a characteristic root of S and let z = x + iy be its associated characteristic vector By definition of a characteristic root Sz = λz (114) S(x + iy) = (λ 1 + iλ 2 )(x + iy) Now premultiply 114 by the complex conjugate (written as a row vector) of z denoted by z This will give

23 CHARACTERISTIC ROOTS AND VECTORS 23 z Sz = λ z z (x iy) S(x + iy) = (λ 1 + iλ 2 )(x iy) (x + iy) (115) We can simplify 115 as follows (x iy) S(x + iy) = (λ 1 + iλ 2 )(x iy) (x + iy) (x S y is)(x + iy) = (λ 1 + iλ 2 )(x x + y y, x y y x) x Sx + ix Sy iy Sx (ii)y Sy =(λ 1 + iλ 2 )(x x + y y, 0) x Sx + iy S x iy Sx (ii)y Sy =(λ 1 + iλ 2 )(x x + y y, 0), x Sy a scalar x Sx + iy Sy iy Sx (ii)y Sy =(λ 1 + iλ 2 )(x x + y y, 0), S is symmetric x Sx + y Sy =(λ 1 + iλ 2 )(x x + y y, 0), ii=-1 x Sx + y Sy =(λ 1 + iλ 2 )(x x + y y) (116) Further note that z z =(x iy) (x + iy) =(x x + y y, x y y x) =(x x + y y, 0) = x x + y y > 0 (117) is a real number The left hand side of 116 is a real number Given that x x + y y is positive and real, this implies that λ 2 =0 This then implies that λ = λ 1 +iλ 2 = λ 1 and is real Theorem 16 If S is a symmetric matrix whose elements are real, corresponding to any characteristic root there exist characteristic vectors that are real Proof The matrix S is symmetric so we can write equation 114 as follows Sz = λz S(x + iy) =(λ 1 + iλ 2 )(x + iy) =(λ 1 )(x + iy) = λ 1 x + iλ 1 y (118) Thus z = x + iy will be a characteristic vector of S corrresponding to λ = λ 1 as long as x and y satisfy Ax = λx Ay = λy (119) and both are not zero, so that z 0 Then construct a real characteristic vector by choosing x 0, such that Ax = λ 1 x and y = 0

24 24 CHARACTERISTIC ROOTS AND VECTORS 92 Symmetric Matices and Orthogonal Characteristic Vectors Theorem 17 If A is a symmetric matrix, the characteristic vectors corresponding to different characteristic roots are orthogonal, ie, if x i corresponds to λ i and x j corresponds to λ j (λ i λ j ), then x i x j =0 Proof We will use the fact that the matrix is symmetric and characteristic root equation 1 Multiply the equations by x j and x i Ax i = λ i x i x j Ax i = λ i x j x i (120a) Ax j = λ j x j x i Ax j = λ j x i x j (120b) Because the matrix A is symmetric, it has real characteristic vectors The inner product of two of them will be a real scalar, so Now subtract 120b for 120a using 121 x j x i = x i x j (121) x j Ax i = λ i x j x i x i Ax j = λ j x i x j x j Ax i x i Ax j =(λ i λ j )x j x i (122) Because A is symmetric and the characteristic vectors are real, x j Ax i is symmetric and real Therefore x j Ax i = x i A x j = x i Ax j The left hand side of 122 is therfore zero This then implies 0 =(λ i λ j )x j x i x j x i =0, λ i λ j (123) 93 An Aside on the Row Space, Column Space and the Null Space of a Square Matrix 931 Definition of R n The space R n consists of all column vectors with n components The components are real numbers 932 Subspaces A subspace of a vector space is a set of vectors (including 0) that satisfies two requirements: If a and b are vectors in the subspace and c is any scalar, then 1: a + b is in the subspace 2: ca is in the subspace 933 Subspaces and linear combinations A subspace containing the vectors a and b must contain all linear combinations of a and b

25 CHARACTERISTIC ROOTS AND VECTORS Column space of a matrix The column space of the mxn matrix A consists of all linear combinations of the columns of A The combinations can be written as Ax The column space of A is a subspace of R m because each of the columns of A has m rows The system of equations Ax= b is solvable if and only if b is in the column space of A What this means is that the system is solvable if there is some way to write b as a linear combination of the columns of A 935 Nullspace of a matrix The nullspace of an m n matrix A consists of all solutions to Ax = 0 These vectors x are in R n The elements of x are the multipliers of the columns of A whose weighted sum gives the zero vector The nullspace containing the solutions x is denoted by N(A) The null space is a subspace of R n while the column space is a subspace of R m For many matrices, the only solution to Ax = 0 is x = 0 If n > m, the nullspace will always contain vectors other than x = Basis vectors A set of vectors in a vector space is a basis for that vector space if any vector in the vector space can be written as a linear combination of them The minimum number of vectors needed to form a basis for R k is k For example, in R 2, we need two vectors of length two to form a basis One vector would only allow for other points in R 2 that lie along the line through that vector 937 Linear dependence A set of vectors is linearly dependent if any one of the vectors in the set can be written as a linear combination of the others The largest number of linearly independent vectors we can have in R k is k 938 Linear independence A set of vectors is linearly independent if and only if the only solution to is α 1 a 1 + α 2 a α kak = 0 (124) α 1 = α 2 = = α k = 0 (125) We can also write this in matrix form The columns of the matrix A are independent if the only solution to the equation Ax = 0 is x = Spanning vectors The set of all linear combinations of a set of vectors is the vector space spanned by those vectors By this we mean all vectors in this space can be written as a linear combination of this particular set of vectors 9310 Rowspace of a matrix The rowspace of the m n matrix A consists of all linear combinations of the rows of A The combinations can be written as x A or A x depending on whether one considers the resulting vectors to be rows or columns The row space of A is a subspace of R n because each of the rows of A has n columns The row space of a matrix is the subspace of R n spanned by the rows 9311 Linear independence and the basis for a vector space A basis for a vector space with k dimensions is any set of k linearly independent vectors in that space 9312 Formal relationship between a vector space and its basis vectors A basis for a vector space is a sequence of vectors that has two properties simultaneously 1: The vectors are linearly independent 2: The vectors span the space There will be one and only one way to write any vector in a given vector space as a linear combination of a set of basis vectors There are an infinite number of basis vectors for a given space, but only one way to write any given vector as a linear combination of a particular basis

26 26 CHARACTERISTIC ROOTS AND VECTORS 9313 Bases and Invertible Matrices The vectors ξ 1,ξ 2,,ξ n are a basis for R n exactly when they are the columns of and n n invertible natirx Therefore R n has infinitely many bases, one associated with every differnt invertible matrix 9314 Pivots and Bases When reducing an m n matrix A to row-echelon form, the pivot columns form a basis for the column space of the matrix A The the pivot rows form a basis for the row space of the matrix A 9315 Dimension of a vector space The dimension of a vector space is the number of vectors in every basis For example, the dimension of R 2 is 2, while the dimension of the vector space consisting of points on a particular plane is R 3 is also Dimension of a subspace of a vector space The dimension of a subspace space S n of an n- dimensional vector space V n is the maximum number of linearly independent vectors in the subspace 9317 Rank of a Matrix 1: The number of non-zero rows in the row echelon form of an m n matrix A produced by elementary operations on A is called the rank of A 2: The rank of an m n matrix A is the number of pivot columns in the row echelon form of A 3: The column rank of an m n matrix A is the maximum number of linearly independent columns in A 4: The row rank of an m n matrix A is the maximum number of linearly independent rows in A 5: The column rank of an m n matrix A is eqaul to row rank of the m n matrix A This common number is called the rank of A 6: An n n matrix A with rank = n is said to be of full rank 9318 Dimension and Rank The dimension of the column space of an m n matrix A equals the rank of A, which also equals the dimension of the row space of A The number of independent columns of A equals the number of independent rows of A As stated earlier, the r columns containing pivots in the row echelon form of the matrix A form a basis for the cloumn space of A 9319 Vector spaces and matrices An mxn matrix A with full column rank (ie, the rank of the matrix is equal to the number of columns) has all the following properties 1: The n columns are independent 2: The only solution to AX = 0 is x = 0 3: The rank of the matrix = dimension of the column space = n 4: The columns are a basis for the column space 9320 Determinants, Minors, and Rank Theorem 18 The rank of an m n matrix A is k if and only if every minor in A of order k +1vanishes, while there is at least one minor of order k which does not vanish Proposition 1 Consider an m n matrix A 1: det A= 0 if every minor of order n - 1 vanishes 2: If every minor of order n equals zero, then the same holds for the minors of higher order 3 (restatement of theorem): The largest among the orders of the non-zero minors generated by a matrix is the rank of the matrix

27 CHARACTERISTIC ROOTS AND VECTORS Nullity of an m n Matrix A The nullspace of an m n Matrix A is made up of vectors in R n and is a subspace of R n The dimension of the nullspace of A is called the nullity of A It is the the maximum number of linearly independent vectors in the nullspace 9322 Dimension of Row Space and Null Space of an m n Matrix A Consider an m n Matrix A The dimension of the nullspace, the maximum number of linearly independent vectors in the nullspace, plus the rank of A, is equal to n Specifically, Theorem 19 rank(a) +nullity(a) =n (126)

28 28 CHARACTERISTIC ROOTS AND VECTORS 94 Gram-Schmidt Orthogonalization and Orthonormal Vectors Theorem 20 Let e i, i= 1,2,,n be a set of n linearly independent n-element vectors They can be transformed into a set of orthonormal vectors Proof Transform them into an orthogonal set and divide each vector by the square root of its length Start by defining the orthogonal vectors as y 1 = e 1 y 2 = a 12 e 1 + e 2 y 3 = a 13 e 1 + a 23 e 2 + e 3 y n = a 1n e 1 + a 2n e a n 1 e n 1 + e n The a ij must be chosen in a way such that the y vectors are orthogonal (127) y i y j = 0, i =1, 2,, j 1 (128) Note that since y i depends only on the e i, the following condition is equivalent e i y j = 0, i =1, 2,, j 1 (129) Now define matrices containing the above information X j =[e 1 e 2 e 3 e j 1 ] a 1j a j a 2j = a (j 1) j The matrix X 2 = [e 1 ], X 3 = [e 1 e 2 ], X 4 = [e 1 e 2 e 3 ] and so forth Now rewrite the system of equations defining the a ij (130) y 1 = e 1 y j = X j a j + e j =[e 1 e 2 e 3 e j 1 ] a 1j a 2j + ej a (j 1) j (131) = a 1j e 1 + a 2j e a (j 1) j e j 1 + e j We can write the condition that y i and y j be orthogonal for all i and j as follows e i y j =0, i =1, 2,, j 1 X j y j =0 X j X j a j + X j X j e j =0, (substitute from 131 ) (132)

29 CHARACTERISTIC ROOTS AND VECTORS 29 The columns of X j are independent This implies that the matrix ( X j X j ) can be inverted Then we can solve the system as follows The system in equation 131 can now be written Normalizing gives orthonormal vectors v (133) X j X j a j + X j X j e j =0 X j X j a j = X j e j a j = (X j X j ) 1 X j e j (134) y 1 = e 1 y j = X j (X j X j ) 1 X j e j + e j y j = e j X j (X j X j ) 1 X j e j v i y i = i =1, 2,, n (135) (y i y i ) 1/2 The vectors y i and y j are orthogonal by definition We can see that they are orthonormal as follows v i v i = y i y i (y i y i ) 1/2 (y i y i ) 1/2 = yi y i (y i y i ) = 1 (136) 95 Symmetric Matices and Linearly Independent Orthonormal Characteristic Vectors Theorem 21 Let A be an nxn symmetric matrix with possibly non-distinct characteristic roots Let the distinct roots be λ i,i=1, 2,,s n and let the multiplicty of j th root be k j with s k=1 k j = n Then 1: If a characteristic root λ j has multiplicity k j 2, there exist k j orthonormal linearly independent characteristic vectors with characteristic root λ j 2: There cannot be more than k j linearly independent characteristic vectors with the same characteristic root λ j, hence, if a characteristic root has multiplicityk j, the characteristic vectors with root k j span a subspace of R n with dimension k j Thus the orthonormal set in [1] spans a k j dimensional subspace of R n By combining these bases for different subspaces, a basis for the original space R n can be obtained Proof Let λ j be a characteristic root of A Let its associated characteristic vector be given by q 1 Assume that the multiplicity of q 1 is k 1 Assume that it is normalized to length 1 Now choose a set of n-1 vectors u (of length n) that are linearly independent of each other and q 1 Number them u 2,, u k Use the Gram-Schmidt theorem to form an orthonormal set of vectors Notice that the algorithm will not change q 1 because it is the first vector Call this matrix of vectors Q 1 Specifically Q 1 = [q 1 u 2 u 3 u n ] (137) Now consider the product of A and Q 1

30 30 CHARACTERISTIC ROOTS AND VECTORS Now premultiply AQ 1 by Q 1 Q 1 AQ 1 = AQ 1 =(Aq 1 Au 2 Au 3 Au n ) =(λ j q 1 Au 2 Au 3 Au n ) λ j q 1 Au 2 q 1 Au 3 q 1 Au n λ j u 2 q 1 u 2 Au 2 u 2 Au 3 u 2 Au n λ j u 3 q 1 u 3 Au 2 u 3 Au 3 u 3 Au n λ j u n q 1 u n Au 2 u n Au 3 u n Au n (138) (139) The last n-1 elements of the first column are zero by orthogonality of q 1 and the vectors u Similarly because A is symmetric and q 1 Au i is a scalar, the first row is zero except for the first element because Now rewrite Q 1 AQ 1 as Q 1 AQ 1 = q 1 Au i = u i Aq 1 = u i λ j q 1 = λ j u i q 1 = 0 λ j u 2 Au 2 u 2 Au 3 u 2 Au n 0 u 3 Au 2 u 3 Au 3 u 3 Au n 0 u n Au 2 u n Au 3 u n Au n (140) (141) = [ ] λj 0 0 α 1 = A 1 where α 1 in an (n-1)x(n-1) symmetric matrix Because Q 1 is an orthogonal matrix its inverse is equal to its transpose so that A and A 1 are similar matrices (see theorem 3 ) and have the same characteristic roots With the same characteristic roots (one of which is here denoted by λ) we obtain λi n A = λi n A 1 =0 (142) Now consider cases where k 1 2, that is the root λ j is not distinct Write the equation λi n A 1 =0 in the following form [ ] [ ] λi n A 1 = λ 0 λj 0 0 λi n 1 0 α 1 = λ λ (143) j 0 0 λi n 1 α 1 We can compute this determinant by a cofactor expansion of the first row We obtain λi n A = (λ λ j ) λi n 1 α 1 = 0 (144)

31 CHARACTERISTIC ROOTS AND VECTORS 31 Because the multiplicity of λ j 2, at least 2 elements of λ will be equal to λ j If a characteristic root that solves equation 144 is not equal to λ j then λi n 1 α 1 must equal zero, ie,if λ λ j λ j I n 1 α 1 = 0 (145) If this determinant is zero then all minors of the matrix [ ] 0 0 λ j I n A 1 = (146) 0 λ j I n 1 α 1 of order n-1 will vanish This means that the nullity of λ j I n A 1 is at least two This is true because the nullity of the submatrix λ j I n 1 - α is at least one and it is a submatrix of A 1 - λ j I n 1 which has a row and column of zeroes and thus already has a nullity of one Because rank(λ j I n A) =rank(λ j I n A 1 ), the nullity of λ j I n A is 2 This means that we can find another characteristic vector q 2, that is linearly independent of q 1 and is also orthogonal to q 1 If the multiplicity of λ j = 2, we are finished Otherwise define the matrix Q 2 as Now let A 2 be defined as Q 2 = [q 1 q 2 u 3 u n ] (147) λ j λ j 0 0 Q 2 AQ 2 = 0 u 3 Au 2 u 3 Au 3 u 3 Au n 0 u n Au 2 u n Au 3 u n Au n λ j 0 0 = 0 λ j α 2 where α 2 in an (n-2)x(n-2) symmetric matrix Because Q 2 is an orthogonal matrix its inverse is equal to its transpose so that A and A 2 are similar matrices and have the same characteristic roots, that is λi n A = λi n A 2 =0 (149) Now consider cases where k 1 2, that is the root λ j is not distinctand has multiplicity 3 Write the equation λi n A 2 = 0 in the following form λi n A 2 = λ λ 0 λ j λ j λi n α 2 (150) λ λ j 0 0 = 0 λ λ j λi n 2 α 2 If we expand this by the first row and then again by the first row we obtain (148) λi n A = (λ λ j ) 2 λi n 2 α 2 = 0 (151)

32 32 CHARACTERISTIC ROOTS AND VECTORS Because the multiplicity of λ j 3, at least 3 elements of λ will be equal to λ j If a characteristic root that solves equation 151 is not equal to λ j then λi n 2 α 2 must equal zero, ie,if λ λ j λ j I n 2 α 2 = 0 (152) If this determinant is zero then all minors of the matrix λ j I n A 2 = (153) 0 0 λ j I n 1 α 2 of order n-2 will vanish This means that the rank of matrix is less than n-2 and so its nullity is greater than or equal to 3 This means that we can find another characteristic vector q 3, that is linearly independent of q 1 and q 2 and is also orthogonal to q 1 If the multiplicity of λ j = 3, we are finished Otherwise, proceed as before Continuing in this way we can find k j orthonormal vectors Now we need to show that we cannot choose more than k j such vectors After choosing q m,we would have λi n A = λi n A kj = (λ λ j) I kj 0 0 λi n kj α kj (154) If we expand this we obtain It is evident that implies λi n A = (λ λ j ) kj λin kj α kj = 0 (155) λi n A = (λ λ j ) kj λin kj α kj = 0 (156) λ j I n kj α kj 0 (157) If this determinant were zero the multiplicity of λ j would exceed k j Thus rank (λ j I A) = n k j (158) (159) nullity (λ j I A) = k j (160) And this implies that the vectors we have chosen form a basis for the null space of λ j -Aand any additional vectors would be linearly dependent Corollary 1 Let A be a matrix as in the above theorem The multiplicity of the root λ j is equal to the nullity of λ j I-A Theorem 22 Let S be a symmetric matrix of order n Then the characteristic vectors of S can be chosen to be an orthonormal set, ie, there exists a Q such that Q SQ = Λ where Q is orthonormal (161)

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