# POL502: Linear Algebra

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1 POL502: Linear Algebra Kosuke Imai Department of Politics, Princeton University December 12, Matrix and System of Linear Equations Definition 1 A m n matrix A is a rectangular array of numbers with m rows and n columns and written as a 11 a 12 a 1n a 21 a 22 a 2n A = a m1 a m2 a mn a ij is called the (i, j)th element of A Note that a special case of matrix is a vector where either m = 1 or n = 1 If m = 1 and n > 1, then it is called a row vector If m > 1 and n = 1, then it is called a column vector A vector has a nice geometric interpretation where the direction and length of the vector are determined by its elements For example, the vector A = [1 3] has the opposite direction and twice as long as the vector B = [ 1/2 3/2] We will discuss vectors in more detail later in this chapter Now, we define the basic operations of matrices Definition 2 Let A and B be m n matrices 1 (equality) A = B if a ij = b ij 2 (addition) C = A + B if c ij = a ij + b ij and C is an m n matrix 3 (scalar multiplication) Given k R, C = ka if c ij = ka ij where C is an m n matrix 4 (product) Let C be an n l matrix D = AC if d ij = n k=1 a ikc kj and D is an m l matrix 5 (transpose) C = A if c ij = a ji and C is an n m matrix Example 1 Calculate A + 2B, AB, and BA using the following matrices [ ] A =, B = 4 3, The basic algebraic operations for matrices are as follows: Theorem 1 (Algebraic Operations of Matrices) Let A, B, C be matrices of appropriate sizes 1 Addition: 1

2 (a) A + B = B + A and A + (B + C) = (A + B) + C (b) There exists a unique C such that A + C = A and we denote C = O (c) There exists a unique C such that A + C = O and we denote C = A 2 Multiplication: (a) k(la) = (kl)a, k(a+b) = ka+kb, (k +l)a = ka+la, and A(kB) = k(ab) = (ka)b for any k, l R (b) A(BC) = (AB)C (c) (A + B)C = AC + BC and C(A + B) = CA + CB 3 Transpose: (a) (A ) = A (b) (A + B) = A + B (c) (AB) = B A (d) (ka) = ka Example 2 Calculate (ABC) using the following matrices [ ] 0 1 [ A =, B = , C = There are some important special types of matrices Definition 3 Let A be an m n matrix 1 A is called a square matrix if n = m 2 A is called symmetric if A = A 3 A square matrix A is called a diagonal matrix if a ij = 0 for i j A is called upper triangular if a ij = 0 for i > j and called lower triangular if a ij = 0 for i < j 4 A diagonal matrix A is called an identity matrix if a ij = 1 for i = j and is denoted by I n In particular, we have AI n = I n A = A for any square matrix A For a square matrix, there is another operator called trace Definition 4 If A is an n n matrix, then the trace of A denoted by tr(a) is defined as the sum of all the main diagonal elements of A That is, tr(a) = n i=1 a ii Some useful facts about trace operators are given below Theorem 2 (Trace Operator) Let A and B be matrices of appropriate sizes 1 tr(ka) = k tr(a) for any k R 2 tr(a + B) = tr(a) + tr(b) 3 tr(ab) = tr(ba) ], 2

3 4 tr(a ) = tr(a) 5 tr(a A) 0 If we start out with an m n matrix and delete some, but not all, of its rows or columns, then we obtain a submatrix For example, C in Example 2 is a submatrix of the following matrix A matrix can be partitioned into submatrices, and such a matrix is called partitioned matrices Partitioned matrices can be manipulated in the same way (called block manipulation) provided that submatrices are of appropriate sizes Example 3 [ A11 A A = 12 A 21 A 22 ] [ B11 B, B = 12 B 21 B 22 ] [ A11 B, AB = 11 + A 12 B 21 A 11 B 12 + A 12 B 22 A 21 B 11 + A 22 B 21 A 21 B 12 + A 22 B 22 ] We now study a special type of square matrices and formulate the notion corresponding to the reciprocal of a nonzero real number Definition 5 An n n matrix A is called nonsingular or invertible if there exists an n n matrix B such that AB = BA = I n We call such B an inverse of A Otherwise, A is called singular or noninvertible Example 4 Show that B is an inverse of A (or A is an inverse of B) [ ] [ ] A =, B = We prove some important properties about the inverse of a matrix Theorem 3 (Uniqueness of Inverse) The inverse of a matrix, if it exists, is unique We denote the unique inverse of A by A 1 Theorem 4 (Properties of Inverse) Let A and B be nonsingular n n matrices 1 AB is nonsingular and (AB) 1 = B 1 A 1 2 A 1 is nonsingular and (A 1 ) 1 = A 3 (A ) 1 = (A 1 ) One application of inverting a matrix is to solve a system of linear equations In fact, matrices can be motivated in terms of linear equations Consider a set of m linear equations of the form y 1 = a 11 x 1 + a 12 x a 1n x n y 2 = a 21 x 1 + a 22 x a 2n x n y m = a m1 x 1 + a m2 x a mn x n 3

4 Then, its matrix representation is Y = AX where a 11 a 12 a 1n a 21 a 22 a 2n A =, X = a m1 a m2 a mn x 1 x 2 x m, Y = We call A a coefficient matrix With this notation, we can see that A 1 (provided that A is nonsingular) solves this system since we obtain X = A 1 Y by premultiplying the equation by A 1 y 1 y 2 y m Example 5 Confirm that [ ] 1 = [ solve the following system of linear equations by using the inverse of matrix 2x 1 + 3x 2 = 1 2x 1 + 2x 2 = 2 Since we do not yet know how to find the inverse of a matrix in general, we rely on high-school algebra to solve a system of linear equations To formalize what we mean by high-school algebra, we introduce the following definitions Definition 6 Elementary row (column) operations on an m n matrix A includes the following 1 Interchange rows (columns) r and s of A 2 Multiply row (column) r of A by a nonzero scalar k 0 3 Add k times row (column) r of A to row (column) s of A where r s An m n matrix A is said to be row (column) equivalent to an m n matrix B if B can be obtained by applying a finite sequence of elementary row (column) operations to A Example 6 Show that A is row equivalent to B A = , B = ] Now, we can use these operations to characterize systems of linear equations Theorem 5 (Row Equivalence and Linear Equations) Let AX = B and CX = D be two linear systems with m equations and n unknowns If the augmented matrices [A B] and [C D] are row equivalent, then the linear systems have the same solutions Finally, to solve systems of linear equations using high-school algebra, we need one more concept Definition 7 An m n matrix A is said to be in reduced row echelon form if it satisfies the following properties 1 All rows consisting entirely of zeros, if any, are at the bottom of the matrix, 4

5 2 By reading from left to right, the first nonzero entry in each row that does not consist entirely of zeros is a 1, called the leading entry of its row 3 If rows i and i + 1 are two successive rows that do not consist entirely of zeros, then the leading entry of row i + 1 is to the right of the leading entry of row i 4 If a column contains a leading entry of some row, then all other entries in that column are zero If A satisfies 1, 2, and 3, but not 4, then it is said to be in row echelon form A similar definition can be applied to (reduced) column echelon form Example 7 A is in row echelon form, and B is in reduced row echelon form A = , B = , Finally, we define two methods that can be used to solve systems of linear equations Theorem 6 (Gaussian and Gauss-Jordan Elimination) A system of linear equations AX = Y can be solved by using Gaussian (Gauss-Jordan) elimination, which consists of the following two steps: 1 Use elementary operations to transform the augmented matrix [AB] to the matrix [CD] in (reduced) row echelon form 2 Solve the linear system corresponding to the augmented matrix [CD] using back substitution Example 8 Solve the following system of linear equations using the Gaussian elimination x 1 + 2x 2 + 3x 3 = 9 2x 1 x 2 + x 3 = 8 3x 1 x 3 = 3 5

6 2 Determinant and Inverse of Matrix In this section, we will learn how to find the inverse of a matrix Definition 8 A permutation of a finite set of integers S = {1, 2,, n} is a bijective function f : S S A permutation is said to have an inversion if a larger integer precedes a smaller one A permutation is called even (odd) if the total number of inversions is even (odd) That is, if S = {1, 2, 3}, then f defined by f(1) = 3, f(2) = 2, f(3) = 1 is an odd permutation Now, we are ready to define determinant of a matrix Definition 9 Let A be an n n matrix Then, the determinant of A denoted by A or det(a) is (±)a 1f(1) a 2f(2) a nf(n) where the summation is over all permutations f : S S with S = {1, 2,, n} The sign is + ( ) if the corresponding permutation is even (odd) Now, we compute the determinants of the following matrices It should be noted that there is no easy method for computing determinants for n > 3 Example 9 What are the determinants of 1 1, 2 2, and 3 3 matrices? We examine some basic properties of determinants In particular, there is an important relationship between the singularity and the determinant of a matrix Theorem 7 (Determinants) Let A and B be n n matrices 1 I n = 1 and I n = ( 1) n 2 ka = k n A for k R 3 A = A 4 A is nonsingular if and only if A 0 5 AB = A B 6 If A is nonsingular, then A 1 = A 1 According to Definition 9, computing the determinant of an n n matrix can be very cumbersome if n is large We now develop a method which reduces the problem to the computation of the determinant of an (n 1) (n 1) matrix so that we can repeat the process until we get to a 2 2 matrix Definition 10 Let A be an n n matrix 1 Let M ij be the (n 1) (n 1) submatrix of A obtained by deleting the ith row and jth column of A Then, M ij is called the minor of a ij 2 The cofactor of A ij of a ij is defined as A ij = ( 1) i+j M ij Now, the following theorem gives us a new method to compute determinants Theorem 8 (Cofactor Expansion) Let A be an n n matrix Then, for any i and j, A = n j=1 a ija ij and A = n i=1 a ija ij 6

7 Example 10 Find the determinant of the following matrix using cofactor expansion A = The following will show how one can use cofactors to calculate the inverse of a matrix Definition 11 Let A be an n n matrix The adjoint of A, adja, is the matrix whose (i, j) element is the cofactor A ji of a ji That is, adja = A 11 A 21 A n1 A 12 A 22 A n2 A 1n A 2n A nn Example 11 Compute the adjoint of the following matrix A = Finally, the inverse of a square matrix can be written as follows Theorem 9 (Inverse of a Matrix) If A is an n n matrix and A 0, then A 1 = 1 A adja The theorem illustrates why A 0 is required for A 1 to exist Example 12 Compute the inverse of A in Example 11 Now, you can solve a system of linear equations provided its solution exists: ie, the inverse of the coefficient matrix exists We introduce another method to solve a system of linear equations Theorem 10 (Cramer s Rule) Consider a system of n linear equations in n unknown parameters with the coefficient matrix A so that we can write Y = AX y 1 = a 11 x 1 + a 12 x a 1n x n y 2 = a 21 x 1 + a 22 x a 2n x n y n = a n1 x 1 + a n2 x a nn x n If A 0, then the system has the unique solution x 1 = A 1 A, x 2 = A 2 A,, x n = A n A, where A i is the matrix obtained from A by replacing its ith column by Y 7

8 Example 13 Apply the Cramer s Rule to the following system of linear equations 2x 1 + 3x 2 x 3 = 1 x 1 + 2x 2 x 3 = 4 2x 1 x 2 + x 3 = 3 8

9 3 Real Vector Spaces In the very first chapter, we learned the concept of ordered pairs We can give a geometric representation to an ordered pair of real numbers, (x, y) with x, y R This is what we call Euclidean 2-space In particular, we consider a pair of perpendicular lines called coordinate axes (one horizontal line called x-axis and the other vertical one called y-axis) intersecting at a point O, called the origin Then, we can associate each point in the plane with each ordered pair of real numbers The set of all such points are denoted by R 2 We can also associate each ordered pair with the directed line segment called vector from the origin to a point P = (x, y) Such a vector is denoted by OP where O is the tail of the vector and P is its head We generalize this idea Definition 12 A vector in the plane from the point P = (x 1, y 1 ) to Q = (x 2, y 2 ) is a 2 1 matrix with two components (x 2 x 1, y 2 y 1 ) We denote it by P Q This indicates that P Q is equal to another vector OP from the origin where P = (x 2 x 1, y 2 y 1 ) In other words, a vector can be described by its direction and its length alone Now, it is important to develop a geometric understanding of the basic vector operations Definition 13 Let u = (x 1, y 1 ) and v = (x 2, y 2 ) be vectors in the plane Then, 1 (equality) u = v if x 1 = x 2 and y 1 = y 2 2 (addition) u + v = (x 1 + x 2, y 1 + y 2 ) 3 (scalar multiplication) ku = (kx 1, ky 1 ) for any k R Example 14 Consider u = (2, 3), v = (3, 4), and w = (1, 2) Draw u + v, u w, and 3w We can generalize these definitions to vectors in space, R 3, by adding the third coordinate axis, z-axis A vector in space is 3 1 matrix It is also possible to extend beyond 3 dimensions and talk about R n, which is called real vector spaces The following basic algebraic operations hold for any real vector spaces Axiom 1 Let u, v, and w be any vectors in R n, and let c, k R Then, 1 u + v = v + u 2 u + (v + w) = (u + v) + w 3 u + 0 = 0 + u = u where 0 = (0,, 0) 4 u + ( u) = 0 5 c(u + v) = cu + cv 6 (c + k)u = cu + ku 7 c(ku) = (ck)u 8 1u = u Just like we did in the first chapter for real numbers, these axioms are sufficient to give basic results such as 0u = 0, c0 = 0, and ( 1)u = u Now, we are ready to study the structure of real vector spaces First, we introduce some key concepts The length of a vector in plane can be defined as follows 9

10 Theorem 11 (Pythagorean Theorem) The length of the vector v = (v 1, v 2 ) in R 2 is v v2 2, and is denoted by v A vector whose length is 1 is called a unit vector Similarly, one can define the distance between two vectors, u = (u 1, u 2 ) and v = (v 1, v 2 ), as u v = (u 1 v 1 ) 2 + (u 2 v 2 ) 2 This definition can be generalized easily to real vector spaces Example 15 Compute the length of the vector v = (1, 2, 3), and the distance between v and u = ( 4, 3, 5) Another important concept is the inner product of two vectors Definition 14 Let u = (u 1, u 2 ) and v = (v 1, v 2 ) be vectors in plane 1 The inner product (or dot product) is defined on R 2 as u 1 v 1 + u 2 v 2 and is denoted by u v 2 u and v are said to be orthogonal if u v = 0 Again, it is easy to generalize this definition to real vector spaces Example 16 Compute the inner product of u = (2, 3, 2) and v = (4, 2, 1) Observe that if we view the vectors u and v as n 1 matrices, then we can obtain another expression for the inner product, u v Theorem 12 (Inner Product) Let u, v, and w be vectors in R n, and k R 1 u v = v u 2 u (v + w) = u v + u w 3 u (kv) = k(u v) = (ku) v 4 u u 0 and u u = 0 implies u = 0 An important application of the inner product is the following theorem Theorem 13 (Cauchy-Schwartz Inequality) Let u and v be vectors in R n Then, (u v) 2 u 2 v 2 Using this theorem, we can prove a more general version of the triangular inequalities that we proved for the real number system Theorem 14 (Triangular Inequalities) Let u and v be vectors in R n Then, 1 u + v u + v 2 u v u v For example, if you replace u and v with a scalar, you obtain usual triangular inequalities for the real number system 10

11 4 Linear Independence In this section, we further examine the structure of real vector spaces Definition 15 Let v 1, v 2,, v k be vectors in a real vector space V 1 A vector v in V is called a linear combination of v 1, v 2,, v k if v = a 1 v 1 + a 2 v a k v k for some real numbers a 1, a 2,, a k 2 A set of vectors S = {v 1, v 2,, v k } is said to span V if every vector in V is a linear combination of v 1, v 2,, v k 3 v 1, v 2,, v k are linearly dependent if there exist constants a 1, a 2,, a k not all zero such that a 1 v 1 + a 2 v a k v k = 0 Otherwise, v 1, v 2,, v k are called linearly independent In other words, vectors v 1,, v k in R n are linearly dependent if and only if the linear system AX = 0 with A = [v 1 v 2 v k ] and X = (a 1,, a k ) has a nonzero solution (or equivalently A 0) A set of vectors which spans a real vector space V completely describes V because every vector in V can be constructed as a linear combination of the vectors in that set Let s apply these concepts to some examples Example 17 Answer the following two questions 1 Do v 1 = (1, 2, 1), v 2 = (1, 0, 2), and v 3 = (1, 1, 0) span R 3? 2 Are v 1 = (1, 0, 1, 2), v 2 = (0, 1, 1, 2), and v 3 = (1, 1, 1, 3) in R 4 linearly dependent? Notice also that any set of k vectors in R n is linearly dependent if k > n Theorem 15 (Linear Independence) Let V be a real vector space 1 Let S 1 and S 2 be finite subsets of V Suppose S 1 S 2 If S 1 is linearly dependent, so is S 2 If S 2 is linearly independent, so is S 1 2 The nonzero vectors v 1, v 2,, v k in V are linearly dependent if and only if one of the vectors v j is a linear combination of the preceding vectors v 1, v 2,, v j 1 There can be many sets of vectors that describe a given real vector space V In particular, such a set can contain vectors which constitute a linear combination of one another To obtain a minimal set of vectors which completely describes V, we develop the following concept Definition 16 The vectors v 1, v 2,, v k in a real vector space V are said to form a basis if they span V and are linearly independent For example, v 1 = (1, 0, 0), v 2 = (0, 1, 0), and v 3 = (0, 0, 1) form a natural basis for R 3 Note that there can be infinitely many bases for a given real vector space For example, c 1 v 1, c 2 v 2, and c 3 v 3 where c 1, c 2, c 3 R also form a basis Example 18 Show that the set S = {v 1, v 2, v 3, v 4 } where v 1 = (1, 0, 1, 0), v 2 = (0, 1, 1, 2), v 3 = (0, 2, 2, 1), and v 4 = (1, 0, 0, 1) forms a basis for R 4 Now, we show the main result about a basis of a real vector space Namely, although there exist infinitely many bases for a given real vector space, all the bases have the same number of vectors 11

12 Theorem 16 (Basis) Let v 1, v 2,, v k and w 1, w 2,, w k be vectors in a real vector space V 1 If S = {v 1, v 2,, v k } is a basis for V, then every vector in V can be written in one and only one linear combination of the vectors in S 2 If S = {v 1, v 2,, v k } is a basis for V and T = {w 1, w 2,, w n } is a linearly independent set of vectors in V, then n k 3 If S = {v 1, v 2,, v k } and T = {w 1, w 2,, w n } are bases for V, then k = n The last result of the previous theorem implies that the number of vectors in two different bases for a particular real vector space is the same In particular, every basis of R n contains n vectors This amounts to the following concept Definition 17 The dimension of a real vector space V is the number of vectors in a basis for V We often write dimv Example 19 What is the dimension of the real vector space V spanned by S = {v 1, v 2, v 3 } where v 1 = (0, 1, 1), v 2 = (1, 0, 1), v 3 = (1, 1, 2) Next, We introduce a method to find a basis for the real vector space spanned by a set of vectors Definition 18 Let A be an m n matrix The rows of A, considered as vectors in R n, span a subspace of R n, called the row space of A Similarly, the columns of A span a subset of R m called the column space of A Theorem 17 (Row and Column Spaces) If A and B are two m n row (column) equivalent matrices, then the row (column) spaces of A and B are identical Example 20 Find a basis for the subspace V of R 5 that is spanned by S = {v 1, v 2, v 3, v 4 } where v 1 = (1, 2, 0, 3, 4), v 2 = (3, 2, 8, 1, 4), v 3 = (2, 3, 7, 2, 3), and v 4 = ( 1, 2, 0, 4, 3) Definition 19 Let A be an m n matrix 1 The dimension of the row (column) space of A is called the row (column) rank of A 2 If the row (column) rank of A is equal to m (n), it is said to be of full row (column) rank It follows from the definition that if A and B are row (column) equivalent, then row (column) ranks of A and B are the same Example 21 Compute the row rank of a matrix A = [v 1 v 2 v 3 v 4 ] where the vectors are defined in Example 20 There is the important relationship between rank and singularity We give the following theorem without proof Theorem 18 (Rank and Singularity) Let A be a n n square matrix A is nonsingular if and only if A is of full rank, ie, ranka = n 12

13 5 Eigenvalues, Eigenvectors, and Definiteness As the final topic of this chapter, we study eigenvalues and eigenvectors Although we will not prove many of the theorems, their results are important and will be frequently applied in statistics First, we give the definition of eigenvalues and eigenvectors Definition 20 Let A be an n n square matrix λ R is called an eigenvalue of A if there exists a nonzero vector x such that Ax = λx Every nonzero vector x R n satisfying this equation is called an eigenvector of A associated with the eigenvalue λ Note that x = 0 always satisfies the equation, but it is not an eigenvector Example 22 Confirm that λ 1 = 2 [ and λ 2 = ] 3 are the eigenvalues and x 1 = (1, 1) and x 2 = (1, 2) 1 1 are their associated eigenvectors of 2 4 The connection between eigenvalues and singularity is critical Theorem 19 (Eigenvalues) Let A be an n n square matrix and λ 1,, λ n be its eigenvalues 1 If A is diagonal, then its diagonal elements are the eigenvalues 2 n i=1 λ i = tr(a) 3 n i=1 λ i = A In particular, A is singular if and only if 0 is an eigenvalue of A Before we state the key theorem, we need one more concept Definition 21 Let A be an n n square matrix Then, λi n A is called the characteristic polynomial of A The equation λi n A = 0 is called the characteristic equation of A The next theorem shows how one can find eigenvalues Theorem 20 (Characteristic Polynomial) Let A be an n n matrix The eigenvalues of A are the real roots of the characteristic polynomial of A A is said to be diagonalizable if all the roots of its characteristic polynomial are real and distinct The word diagonalizable comes from the fact that the diagonal matrix whose nonzero elements are the eigenvalues of A represent a linear transformation, a function mapping from one real vector space to another L : V W is a linear transformation if it satisfies L(v + w) = L(v) + L(w) and L(cv) = cl(v) for any vector v V and w W One important linear transformation [ is ] what is called projection defined by L : R 3 R 2 and represented by the matrix A = so that for any vector v = (v 1, v 2, v 3 ) R 3 we have L(v) = (v 1, v 2 ) Example 23 Find the eigenvalues and eigenvectors, if they exist, of Definition 22 An n n square matrix A is called orthogonal if A A = I n [ and In particular, the orthogonal matrix is invertible, and hence A 1 = A We end this chapter with the concept of definiteness of a matrix ] 13

14 Definition 23 Let A be an n n symmetric matrix A and x be a column vector in R n 1 A is positive definite (positive semidefinite) if x Ax > 0 (x Ax 0) for any x 0 2 A is negative definite (negative semidefinite) if x Ax < 0 (x Ax 0) for any x 0 If A fits none of the above definitions, then it is called indefinite From the definition, one immediately sees that A is positive (negative) definite if and only if A is negative (positive) definite Positive (negative) definite matrices have some important properties Theorem 21 (Positive and Negative Definiteness) Let A be an n n symmetric matrix A 1 A is positive (negative) definite if and only if all of its eigenvalues are positive (negative) 2 If A is positive (negative) definite, then A is invertible and A 1 is postive (negative) definite 3 If A is positive definite, then A > 0 4 If A is positive definite, then there exists a upper triangular matrix U such that A = U U (Cholesky decomposition) The first part of the theorem implies that if A is a diagonal matrix and positive definite, then all of its diagonal elements must be positive The Cholesky decomposition can be seen as a square root of a symmetric matrix One way to check the definiteness of matrices is to compute eigen values and use Theorem 21 (1) However, there is an easier way to check the definiteness Definition 24 Let A be an n n square matrix and k be an integer with 1 k n 1 The kth order principal submatrix of A is an k k submatrix of A which can be constructed by deleting the n k columns and the same n k rows If the last n k columns and rows are deleted, then the resulting submatrix is called the kth order leading principal submatrix 2 The determinant of a kth order (leading) principal submatrix of A is called a kth order (leading) principal minor of A There is a special relationship between principal minors and definiteness of a matrix Theorem 22 (Principal Minor and Definiteness) Let A be an n n symmetric matrix and k be an integer with 1 k n 1 A is positive definite if and only if the kth order leading principal minor is positive for all k 2 A is positive semi-definite if and only if every principal minor is greater than or equal to zero for all k 3 A is negative definite if and only if the kth order leading principal minor has the same sign as ( 1) k for all k 4 A is negative semi-definite if and only if every kth order principal minor is zero or has the same sign as ( 1) k for all k An example is given below Example 24 Check the definiteness of the following matrices A = and B = We are done with linear algebra! 14

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