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Chapter 3. Basic Properties of Matrices

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Chapter 3. Basic Properties of Matrices 3.1. Basic Definitions and Notations 1 Chapter 3. Basic Properties of Matrices Note. This long chapter (over 100 pages) contains the bulk of the material for this course. As in Chapter 2, unless stated otherwise, all matrix entries are real numbers. Chapters 8 and 9 include material on applications of this material to regression and statistics. Section 3.1. Basic Definitions and Notation Note. In this section, we reintroduce many of the definitions you should be famil- iar with from sophomore Linear Algebra (diagonal, trace, minor matrix, cofactor, determinant). We also introduce some notation and matrix/vector manipulations which might be new to you. Definition. Let A be an n×m matrix. Treating the columns of A as n-vectors, the vector space generated by these column vectors (that is, the span of the columns of A) is the column space of matrix A. This is sometimes called the range or manifold of A and is denoted span(A). Treating the rows of A as m-vectors, the vectors generated by these row vectors (that is, the span of the rows of A) is the row space of matrix A. 3.1. Basic Definitions and Notations 2 Definition. For matrix A = [aij], define scalar multiplication entry-wise as cA = c[aij] = [caij]. For A = [aij] and B = [bij] m × n matrices, define matrix addition entry-wise as A + B = [aij] + [bij] = [aij + bij]. The diagonal elements of A = [aij] are the elements aii for i = 1, 2,..., min{m, n} (these elements make up the principal diagonal), elements aij where i < j are above the diagonal, and elements aij where i > j are below the diagonal. The elements ai,i+k (for fixed k) form the kth codiagonal or kth minor diagonal. For n × m matrix A, the elements ai,m+1−i for i = 1, 2,..., min{m, n} are the skew diagonal elements of A. Definition. Square matrix A of size n × n is symmetric if aij = aji for all 1 ≤ i, j ≤ n. Square matrix A is skew symmetric if aij = −aji for 1 ≤ i, j ≤ n. Square matrix A with complex entries is Hermetian (or self-adjoint) if aij = aji (where aji represents the complex conjugate of aji). Definition. If all entries of a square matrix are 0 except for the principal diagonal elements, then matrix A is a diagonal matrix. If all the principal diagonal elements are 0, the matrix is a hollow matrix. If all entries except the principal skew diagonal elements of a matrix are 0, the matrix is a skew diagonal matrix. Note. A skew symmetric matrix must satisfy aii = −aii for 1 ≤ i ≤ n and so is necessarily hollow. 3.1. Basic Definitions and Notations 3 Pm Definition. An n × m matrix A for which |aii| > j=1,j6=i |aij| for each i = 1, 2, . , n is row diagonally dominant (that is, the absolute value of the diagonal entry is larger than the sum of the absolute values of the other entries in the row Pn containing that diagonal entry [for each row]). Similarly, if |ajj| > i=1,i6=j |aij| for each j = 1, 2, . , m then A is column diagonally dominant. Note. If A is symmetric (and hence square) then row and column diagonal domi- nance are equivalent in which case A is simply called diagonally dominant. Definition. A square matrix such that all elements below the diagonal are 0 is an upper triangular matrix; if all entries above the diagonal are 0 the matrix is a lower triangular matrix. If a nonsquare matrix has all entries below the diagonal {aii} equal to 0 (or all entries above the diagonal equal to 0) then the matrix is trapezoidal. Definition. A square matrix A for which all elements are 0 except for ai,i+ck for some (“small”) ck where ck ∈ {−w`, −w` + 1,..., −1, 0, 1, . , wu−1, wu} is a band matrix (or banded matrix). Value w` is the lower band width and wu is the upper band width. A band matrix with lower and upper band widths of 1 is tridiagonal. A matrix is in upper Hessenburg form if it is upper triangular except for the first (lower) subdiagonal. Note. Gentle states that band matrices arise in time series, stochastic processes, and differential equations (see page 43). 3.1. Basic Definitions and Notations 4 Definition. For m × n matrix A = [aij], the transpose matrix is the n × m matrix T A = [aji]. If the entries of A are complex numbers, then the adjoint of A is the H conjugate transpose A = [aji], also called the Hermetian of A. Note. As you’ve seen in sophomore Linear Algebra, we often put vectors into ma- trices and extract vectors from matrices. We now define such operations formally. Definition. Define the “constructor function” diag : Rn → Rn×n as d1 0 0 ··· 0 0 d2 0 ··· 0 diag([d1, d2, . , dn]) = 0 0 d ··· 0 3 . . ... 0 0 0 ··· dn which maps an n-vector into an n × n diagonal matrix with diagonal entries equal to the entries in the n-vector. Define vecdiag : Rn×m → Rk where k = min{m, n} as vecdiag(A) = [a11, a22, . , akk]. Define vec : Rn×m → Rnm as T T T vec(A) = [a1 , a2 , . , am] T T T where a1, a2, . , am are the columns of matrix A (so “[a1 , a2 , . , am]” denotes an nm row vector). Define vech : Rm×n → Rm(m+1)/2 as vech(A) = [a11, a21, . , am1, a22, a32, . , am2, a33, a43, . , amm] (Gentle restricts vech to the symmetric matrices in Rm×n so that vech(A) produces a vector with entries as those entries on and above the diagonal of A). 3.1. Basic Definitions and Notations 5 Note. Gentle doesn’t give a formal definition of a partitioned matrix. You are familiar with the idea of a partitioned matrix since the system of equations A~x = ~b (in the notation of sophomore Linear Algebra) is dealt with in terms of the partitioned matrix (or “augmented matrix”) [A |~b]. “Definition.” An n×m matrix A can be partitioned into four matrices A11,A12,A21, and A as 22 A11 A12 A = A21 A22 where A11 and A12 have the same number of rows (say r1); A21 and A22 have the same number of rows (say r2); A11 and A21 have the same number of columns (say c1); and A12 and A22 have the same number of columns (say c2). The ith row of A11 “combined with” the ith row of A12 gives the ith row of A for 1 ≤ i ≤ r1; the ith row of A21 “combined with” the ith row of A22 gives the (r1 + i)th row of A for 1 ≤ i ≤ r2; the jth column of A11 “combined with” the jth column of A21 gives the jth column of A for 1 ≤ j ≤ c1; and the jth column of A12 “combined with” the jth column of A22 gives the (c1 + j)th column of A for 1 ≤ j ≤ c2. Definition. Matrix B is a submatrix of matrix A if B is obtained from A by deleting some of the rows and some of the columns of A. A square submatrix whose principal diagonal elements are elements of the principal diagonal of matrix A is a principal submatrix of A. A principal submatrix B obtained from n × m matrix A such that if column j is eliminated from A then all columns j + 1, j + 2, . m are eliminated in producing B, and if row i is eliminated from A then all rows i + 1, i + 2, . , n are eliminated, is a leading principal submatrix. 3.1. Basic Definitions and Notations 6 Theorem 3.1.1. Suppose matrix A is diagonally dominant (that is, A is symmetric and row and column diagonally dominant). If B is a principal submatrix of A then B is also diagonally dominant. Definition. A partitioned matrix is block diagonal if it is of the form A1 0 ··· 0 0 A2 ··· 0 . .. . 0 0 ··· Ak for i = 1, 2, . , k and where “0” represents a submatrix of all 0’s of appropriate dimensions to yield a partitioned matrix. Similar to the function diag : Rn → Rn×n, we denote the matrix above as diag(A1,A2,...,Ak). Definition. Let A = [aij] and B = [bij] be n × m matrices. The sum A + B is the n × m matrix C = [cij] where cij = aij + bij. For r ∈ R a scalar, rA is the matrix D = [dij] where dij = raij. Theorem 3.1.2. Let A and B be n × m matrices and r, s ∈ R. Then (a) A + B = B + A (Commutivity Law of Matrix Addition). (b) (A + B) + C = A + (B + C) (Associative Law of Matrix Addition). (c) A + 0 = 0 + A where 0 is the n × m zero matrix (Matrix Additive Identity). 3.1. Basic Definitions and Notations 7 (d) r(A + B) = rA + rB (Distribution of Scalar Multiplication over Matrix Addi- tion). (e) (r+s)A = rA+sA (Distribution of Scalar Multiplication over Scalar Addition). (f) (rs)A = r(sA) (Associativity Law of Scalar Multiplication). Note. Since we have defined matrix addition and scalar multiplication, we can show that Rn×m is in fact a vector space (of dimension nm). A subspace of Rn×m is the space of all symmetric n × n matrices; Rn×n is of dimension n(n + 1)/2 (see Exercise 3.1). Notation. For a ∈ R and A = [aij] we define A + a = [aij + a]. If all elements of real matrix A are positive, we write A > 0; if all elements of A are nonnegative we write A ≥ 0.
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