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Matrices and Linear Algebra Chapter 2 Matrices and Linear Algebra 2.1 Basics Definition 2.1.1. A matrix is an m n array of scalars from a given field F . The individual values in the matrix× are called entries. Examples. 213 12 A = B = 124 34 − The size of the array is–written as m n,where × m n × number of rows number of columns Notation a11 a12 ... a1n a21 a22 ... a2n rows ←− A = a a ... a n1 n2 mn columns A := uppercase denotes a matrix a := lower case denotes an entry of a matrix a F. ∈ Special matrices 33 34 CHAPTER 2. MATRICES AND LINEAR ALGEBRA (1) If m = n, the matrix is called square.Inthiscasewehave (1a) A matrix A is said to be diagonal if aij =0 i = j. (1b) A diagonal matrix A may be denoted by diag(d1,d2,... ,dn) where aii = di aij =0 j = i. The diagonal matrix diag(1, 1,... ,1) is called the identity matrix and is usually denoted by 10... 0 01 In = . . .. 01 or simply I,whenn is assumed to be known. 0 = diag(0,... ,0) is called the zero matrix. (1c) A square matrix L is said to be lower triangular if ij =0 i<j. (1d) A square matrix U is said to be upper triangular if uij =0 i>j. (1e) A square matrix A is called symmetric if aij = aji. (1f) A square matrix A is called Hermitian if aij =¯aji (¯z := complex conjugate of z). (1g) Eij has a 1 in the (i, j) position and zeros in all other positions. (2) A rectangular matrix A is called nonnegative if aij 0alli, j. ≥ It is called positive if aij > 0alli, j. Each of these matrices has some special properties, which we will study during this course. 2.1. BASICS 35 Definition 2.1.2. The set of all m n matrices is denoted by Mm,n(F ), × where F is the underlying field (usually R or C). In the case where m = n we write Mn(F ) to denote the matrices of size n n. × Theorem 2.1.1. Mm,n is a vector space with basis given by Eij, 1 i m, 1 j n. ≤ ≤ ≤ ≤ Equality, Addition, Multiplication Definition 2.1.3. Two matrices A and B are equal if and only if they have thesamesizeand aij = bij all i, j. Definition 2.1.4. If A is any matrix and α F then the scalar multipli- cation B = αA is defined by ∈ bij = αaij all i, j. Definition 2.1.5. If A and B are matrices of the same size then the sum A and B is defined by C = A + B,where cij = aij + bij all i, j We can also compute the difference D = A B by summing A and ( 1)B − − D = A B = A +( 1)B. − − matrix subtraction. Matrix addition “inherits” many properties from the field F . Theorem 2.1.2. If A, B, C Mm,n(F ) and α, β F ,then ∈ ∈ (1) A + B = B + A commutivity (2) A +(B + C)=(A + B)+C associativity (3) α(A + B)=αA + αB distributivity of a scalar (4) If B =0(a matrix of all zeros) then A + B = A +0=A (4) (α + β)A = αA + βA 36 CHAPTER 2. MATRICES AND LINEAR ALGEBRA (5) α(βA)=αβA (6) 0A =0 (7) α 0=0. Definition 2.1.6. If x and y Rn, ∈ x =(x1 ...xn) y =(y1 ...yn). Then the scalar or dot product of x and y is given by n x, y = xiyi. i=1 Remark 2.1.1. (i) Alternate notation for the scalar product: x, y = x y. (ii) The dot product is defined only for vectors of the same length. · Example 2.1.1. Let x =(1, 0, 3, 1) and y =(0, 2, 1, 2) then x, y = 1(0) + 0(2) + 3( 1) 1(2) = 5. − − − − − Definition 2.1.7. If A is m n and B is n p.Letri(A) denote the vector th × × with entries given by the i row of A,andletcj(B) denote the vector with entries given by the jth row of B. The product C = AB is the m p matrix defined by × cij = ri(A),cj(B) th where ri(A) is the vector in Rn consisting of the i row of A and similarly th cj(B) is the vector formed from the j column of B. Other notation for C = AB n cij = aikbkj 1 i m k=1 ≤ ≤ 1 j p. ≤ ≤ Example 2.1.2. Let 21 101 A = and B = 30. 321 11 − Then 12 AB = . 11 4 2.1. BASICS 37 Properties of matrix multiplication (1) If AB exists, does it happen that BA exists and AB = BA?The answer is usually no. First AB and BA exist if and only if A ∈ Mm,n(F )andB Mn,m(F ). Even if this is so the sizes of AB and BA are different∈ (AB is m m and BA is n n) unless m = n. However even if m = n we may× have AB = BA×.Seetheexamples below. They may be different sizes and if they are the same size (i.e. A and B aresquare)theentriesmaybedifferent 1 A =[1, 2] B = AB =[1] −1 1 2 BA = −12− 12 11 13 A = B = AB = 34 −01 −37 − 22 BA = 34 (2) If A is square we define A1 = A, A2 = AA, A3 = A2A = AAA n n 1 A = A − A = A A (n factors). ··· (3) I = diag(1,... ,1). If A Mm,n(F )then ∈ AIn = A and ImA = A. Theorem 2.1.3 (Matrix Multiplication Rules). Assume A, B,andC are matrices for which all products below make sense. Then (1) A(BC)=(AB)C (2) A(B C)=AB AC and (A B)C = AC BC ± ± ± ± (3) AI = A and IA = A (4) c(AB)=(cA)B (5) A0=0and 0B =0 38 CHAPTER 2. MATRICES AND LINEAR ALGEBRA (6) For A square ArAs = AsAr for all integers r, s 1. ≥ Fact: If AC and BC are equal, it does not follow that A = B. See Exercise 60. Remark 2.1.2. We use an alternate notation for matrix entries. For any matrix B denote the (i, j)-entry by (B)ij. Definition 2.1.8. Let A Mm,n(F ). ∈ (i) Define the transpose of A, denoted by AT ,tobethen m matrix with entries × T (A )ij = aji. (ii) Define the adjoint of A, denoted by A∗,tobethen m matrix with entries × (A∗)ij =¯aji complex conjugate Example 2.1.3. 15 123 A = AT = 24 541 31 In words... “The rows of A become the columns of AT , taken in the same order.” The following results are easy to prove. T T Theorem 2.1.4 (Laws of transposes). (1) (A ) = A and (A∗)∗ = A (2) (A B)T = AT BT (and for ) ± ± ∗ T T (3) (cA) = cA (cA)∗ =¯cA∗ (4) (AB)T = BT AT (5) If A is symmetric A = AT 2.1. BASICS 39 (6) If A is Hermitian A = A∗. More facts about symmetry. T T T T T Proof. (1) We know (A )ij = aji.So((A ) )ij = aij.Thus(A ) = A. T T T T (2) (A B) = aji bji.So(A B) = A B . ± ± ± ± Proposition 2.1.1. (1) A is symmetric if and only if AT is symmetric. (1)∗ A is Hermitian if and only if A∗ is Hermitian. (2) If A is symmetric, then A2 is also symmetric. (3) If A is symmetric, then An is also symmetric for all n. Definition 2.1.9. A matrix is called skew-symmetric if AT = A. − Example 2.1.4. The matrix 012 A = 10 3 −23− 0 − is skew-symmetric. Theorem 2.1.5. (1) If A is skew symmetric, then A is a square matrix and aii =0, i =1,... ,n. (2) For any matrix A Mn(F ) ∈ A AT − is skew-symmetric while A + AT is symmetric. (3) Every matrix A Mn(F ) can be uniquely written as the sum of a skew-symmetric and∈ symmetric matrix. T T Proof. (1) If A Mm,n(F ), then A Mn,m(F ). So, if A = A we must have m∈= n.Also ∈ − aii = aii − for i =1,... ,n.Soaii =0foralli. 40 CHAPTER 2. MATRICES AND LINEAR ALGEBRA (2) Since (A AT )T = AT A = (A AT ), it follows that A AT is skew-symmetric.− − − − − (3) Let A = B + C be a second such decomposition. Subtraction gives 1 1 (A + AT ) B = C (A AT ). 2 − − 2 − The left matrix is symmetric while the right matrix is skew-symmetric. Hence both are the zero matrix. 1 1 A = (A + AT )+ (A AT ). 2 2 − 0 1 Examples. A = 10− is skew-symmetric. Let 12 B = 14 − 1 1 BT = 24− 03 B BT = − 30 − 21 B + BT = . 18 Then 1 1 B = (B BT )+ (B + BT ). 2 − 2 An important observation about matrix multiplication is related to ideas from vector spaces. Indeed, two very important vector spaces are associated with matrices. Definition 2.1.10. Let A Mm,n(C). (i)Denote by ∈ th cj(A):=j column of A cj(A) Cm. We call the subspace of Cm spanned by the columns of A the ∈ column space of A.Withc1 (A) ,...,cn (A) denoting the columns of A 2.1. BASICS 41 the column space is S (c1 (A) ,...,cn (A)) . (ii) Similarly, we call the subspace of Cn spanned by the rows of A the row space of A.Withr1 (A) ,...,rm (A) denoting the rows of A the row space is therefore S (r1 (A) ,...,rm (A)) .
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