# Notes on Mathematics

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1 Notes on Mathematics Peeyush Chandra, A. K. Lal, V. Raghavendra, G. Santhanam 1 Supported by a grant from MHRD

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3 Contents I Linear Algebra 7 1 Matrices Definition of a Matrix Special Matrices Operations on Matrices Multiplication of Matrices Some More Special Matrices Submatrix of a Matrix Block Matrices Matrices over Complex Numbers Linear System of Equations Introduction Definition and a Solution Method A Solution Method Row Operations and Equivalent Systems Gauss Elimination Method Row Reduced Echelon Form of a Matrix Gauss-Jordan Elimination Elementary Matrices Rank of a Matrix Existence of Solution of Ax = b Example Main Theorem Exercises Invertible Matrices Inverse of a Matrix Equivalent conditions for Invertibility Inverse and Gauss-Jordan Method Determinant Adjoint of a Matrix Cramer s Rule Miscellaneous Exercises Finite Dimensional Vector Spaces Vector Spaces Definition Examples

4 4 CONTENTS Subspaces Linear Combinations Linear Independence Bases Important Results Ordered Bases Linear Transformations Definitions and Basic Properties Matrix of a linear transformation Rank-Nullity Theorem Similarity of Matrices Inner Product Spaces Definition and Basic Properties Gram-Schmidt Orthogonalisation Process Orthogonal Projections and Applications Matrix of the Orthogonal Projection Eigenvalues, Eigenvectors and Diagonalization Introduction and Definitions diagonalization Diagonalizable matrices Sylvester s Law of Inertia and Applications II Ordinary Differential Equation Differential Equations Introduction and Preliminaries Separable Equations Equations Reducible to Separable Form Exact Equations Integrating Factors Linear Equations Miscellaneous Remarks Initial Value Problems Orthogonal Trajectories Numerical Methods Second Order and Higher Order Equations Introduction More on Second Order Equations Wronskian Method of Reduction of Order Second Order equations with Constant Coefficients Non Homogeneous Equations Variation of Parameters Higher Order Equations with Constant Coefficients

5 CONTENTS Method of Undetermined Coefficients Solutions Based on Power Series Introduction Properties of Power Series Solutions in terms of Power Series Statement of Frobenius Theorem for Regular (Ordinary) Point Legendre Equations and Legendre Polynomials Introduction Legendre Polynomials III Laplace Transform Laplace Transform Introduction Definitions and Examples Examples Properties of Laplace Transform Inverse Transforms of Rational Functions Transform of Unit Step Function Some Useful Results Limiting Theorems Application to Differential Equations Transform of the Unit-Impulse Function IV Numerical Applications Newton s Interpolation Formulae Introduction Difference Operator Forward Difference Operator Backward Difference Operator Central Difference Operator Shift Operator Averaging Operator Relations between Difference operators Newton s Interpolation Formulae Lagrange s Interpolation Formula Introduction Divided Differences Lagrange s Interpolation formula Gauss s and Stirling s Formulas Numerical Differentiation and Integration Introduction Numerical Differentiation Numerical Integration

6 6 CONTENTS A General Quadrature Formula Trapezoidal Rule Simpson s Rule Appendix System of Linear Equations Determinant Properties of Determinant Dimension of M +N Proof of Rank-Nullity Theorem Condition for Exactness

7 Part I Linear Algebra

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9 Chapter 1 Matrices 1.1 Definition of a Matrix Definition (Matrix) A rectangular array of numbers is called a matrix. We shall mostly be concerned with matrices having real numbers as entries. The horizontal arrays of a matrix are called its rows and the vertical arrays are called its columns. A matrix having m rows and n columns is said to have the order m n. A matrix A of order m n can be represented in the following form: a 11 a 12 a 1n a 21 a 22 a 2n A =......, a m1 a m2 a mn where a ij is the entry at the intersection of the i th row and j th column. In a more concise manner, we also denote the matrix A by [a ij ] by suppressing its order. a 11 a 12 a 1n a 21 a 22 a 2n Remark Some books also use..... to represent a matrix.. a m1 a m2 a mn [ ] Let A =. Then a 11 = 1, a 12 = 3, a 13 = 7, a 21 = 4, a 22 = 5, and a 23 = A matrix having only one column is called a column vector; and a matrix with only one row is called a row vector. Whenever a vector is used, it should be understood from the context whether it is a row vector or a column vector. Definition (Equality of two Matrices) Two matrices A = [a ij ] and B = [b ij ] having the same order m n are equal if a ij = b ij for each i = 1,2,...,m and j = 1,2,...,n. In other words, two matrices are said to be equal if they have the same order and their corresponding entries are equal.

10 1 CHAPTER 1. MATRICES Example [ The] linear system of equations 2x + 3y = 5 and 3x + 2y = 5 can be identified with the 2 3 : 5 matrix. 3 2 : Special Matrices Definition example, 1. A matrix in which each entry is zero is called a zero-matrix, denoted by. For [ ] 2 2 = [ ] and 2 3 =. 2. A matrix having the number of rows equal to the number of columns is called a square matrix. Thus, its order is m m (for some m) and is represented by m only. 3. In a square matrix, A = [a ij ], of order n, the entries a 11,a 22,...,a nn are called the diagonal entries and form the principal diagonal of A. 4. A square matrix A = [a ij ] is said to be a diagonal matrix if a ij = for i j. In other words, [ the ] 4 non-zero entries appear only on the principal diagonal. For example, the zero matrix n and 1 are a few diagonal matrices. AdiagonalmatrixDofordernwiththediagonalentriesd 1,d 2,...,d n isdenotedbyd = diag(d 1,...,d n ). If d i = d for all i = 1,2,...,n then the diagonal matrix D is called a scalar matrix. { 1 if i = j 5. A square matrix A = [a ij ] with a ij = if i j is called the identity matrix, denoted by I n. [ ] 1 1 For example, I 2 =, and I 3 = The subscript n is suppressed in case the order is clear from the context or if no confusion arises. 6. A square matrix A = [a ij ] is said to be an upper triangular matrix if a ij = for i > j. A square matrix A = [a ij ] is said to be an lower triangular matrix if a ij = for i < j. A square matrix A is said to be triangular if it is an upper or a lower triangular matrix For example 3 1 is an upper triangular matrix. An upper triangular matrix will be represented 2 a 11 a 12 a 1n a 22 a 2n by a nn 1.2 Operations on Matrices Definition (Transpose of a Matrix) The transpose of an m n matrix A = [a ij ] is defined as the n m matrix B = [b ij ], with b ij = a ji for 1 i m and 1 j n. The transpose of A is denoted by A t.

11 1.2. OPERATIONS ON MATRICES 11 That is, by the transpose of an m n matrix A, we mean a matrix of order n m having the rows of A as its columns and the columns of A as its rows. [ ] For example, if A = then A t = Thus, the transpose of a row vector is a column vector and vice-versa. Theorem For any matrix A, we have (A t ) t = A. Proof. Let A = [a ij ], A t = [b ij ] and (A t ) t = [c ij ]. Then, the definition of transpose gives c ij = b ji = a ij for all i,j and the result follows. Definition (Addition of Matrices) let A = [a ij ] and B = [b ij ] be are two m n matrices. Then the sum A+B is defined to be the matrix C = [c ij ] with c ij = a ij +b ij. Note that, we define the sum of two matrices only when the order of the two matrices are same. Definition (Multiplying a Scalar to a Matrix) Let A = [a ij ] be an m n matrix. Then for any element k R, we define ka = [ka ij ]. [ ] [ ] For example, if A = and k = 5, then 5A = Theorem Let A,B and C be matrices of order m n, and let k,l R. Then 1. A+B = B +A (commutativity). 2. (A+B)+C = A+(B +C) (associativity). 3. k(la) = (kl)a. 4. (k +l)a = ka+la. Proof. Part 1. Let A = [a ij ] and B = [b ij ]. Then A+B = [a ij ]+[b ij ] = [a ij +b ij ] = [b ij +a ij ] = [b ij ]+[a ij ] = B +A as real numbers commute. The reader is required to prove the other parts as all the results follow from the properties of real numbers. Exercise Suppose A+B = A. Then show that B =. 2. Suppose A+B =. Then show that B = ( 1)A = [ a ij ]. Definition (Additive Inverse) Let A be an m n matrix. 1. Then there exists a matrix B with A+B =. This matrix B is called the additive inverse of A, and is denoted by A = ( 1)A. 2. Also, for the matrix m n, A+ = +A = A. Hence, the matrix m n is called the additive identity.

12 12 CHAPTER 1. MATRICES Multiplication of Matrices Definition (Matrix Multiplication / Product) Let A = [a ij ] be an m n matrix and B = [b ij ] be an n r matrix. The product AB is a matrix C = [c ij ] of order m r, with c ij = n a ik b kj = a i1 b 1j +a i2 b 2j + +a in b nj. k=1 Observe that the product AB is defined if and only if the number of columns of A = the number of rows of B. [ ] For example, if A = and B = 3 then [ ] [ ] AB = = Note that in this example, while AB is defined, the product BA is not defined. However, for square matrices A and B of the same order, both the product AB and BA are defined. Definition Two square matrices A and B are said to commute if AB = BA. Remark Note that if A is a square matrix of order n then AI n = I n A. Also for any d R, the matrix di n commutes with every square matrix of order n. The matrices di n for any d R are called scalar matrices. 2. In general, the [ matrix ] product[ is not ] commutative. For example, consider the following two matrices A = and B =. Then check that the matrix product 1 [ ] [ ] AB = = BA. 1 1 Theorem Suppose that the matrices A, B and C are so chosen that the matrix multiplications are defined. 1. Then (AB)C = A(BC). That is, the matrix multiplication is associative. 2. For any k R, (ka)b = k(ab) = A(kB). 3. Then A(B +C) = AB +AC. That is, multiplication distributes over addition. 4. If A is an n n matrix then AI n = I n A = A. 5. For any square matrix A of order n and D = diag(d 1,d 2,...,d n ), we have the first row of DA is d 1 times the first row of A; for 1 i n, the i th row of DA is d i times the i th row of A. A similar statement holds for the columns of A when A is multiplied on the right by D. Proof. Part 1. Let A = [a ij ] m n, B = [b ij ] n p and C = [c ij ] p q. Then (BC) kj = p b kl c lj and (AB) il = l=1 n a ik b kl. k=1

13 1.3. SOME MORE SPECIAL MATRICES 13 Therefore, ( A(BC) ) ij = = = n ( ) a ik BC = n ( p ) a kj ik b kl c lj k=1 n k=1 l=1 k=1 p ( ) n a ik bkl c lj = p ( n ) a ik b kl clj = l=1 k=1 l=1 = ( (AB)C ) ij. l=1 k=1 l=1 p ( ) aik b kl clj t ( ) AB il c lj Part 5. For all j = 1,2,...,n, we have (DA) ij = n d ik a kj = d i a ij k=1 as d ik = whenever i k. Hence, the required result follows. The reader is required to prove the other parts. Exercise Let A and B be two matrices. If the matrix addition A+B is defined, then prove that (A+B) t = A t +B t. Also, if the matrix product AB is defined then prove that (AB) t = B t A t. b 1 b 2 2. Let A = [a 1,a 2,...,a n ] and B =. Compute the matrix products AB and BA.. 3. Let n be a positive integer. Compute A n for the following matrices: [ ] , 1 1, Can you guess a formula for A n and prove it by induction? 4. Find examples for the following statements. b n (a) Suppose that the matrix product AB is defined. Then the product BA need not be defined. (b) Suppose that the matrix products AB and BA are defined. Then the matrices AB and BA can have different orders. (c) Suppose that the matrices A and B are square matrices of order n. Then AB and BA may or may not be equal. 1.3 Some More Special Matrices Definition A matrix A over R is called symmetric if A t = A and skew-symmetric if A t = A. 2. A matrix A is said to be orthogonal if AA t = A t A = I Example Let A = and B = 1 3. Then A is a symmetric matrix and B is a skew-symmetric matrix.

14 14 CHAPTER 1. MATRICES Let A = Then A is an orthogonal matrix. 1 if i = j Let A = [a ij ] be an n n matrix with a ij =. Then A n = and A l for 1 l otherwise n 1. The matrices A for which a positive integer k exists such that A k = are called nilpotent matrices. The least positive integer k for which A k = is called the order of nilpotency. [ ] 1 4. Let A =. Then A 2 = A. The matrices that satisfy the condition that A 2 = A are called idempotent matrices. Exercise Show that for any square matrix A, S = 1 2 (A+At ) is symmetric, T = 1 2 (A At ) is skew-symmetric, and A = S +T. 2. Show that the product of two lower triangular matrices is a lower triangular matrix. A similar statement holds for upper triangular matrices. 3. Let A and B be symmetric matrices. Show that AB is symmetric if and only if AB = BA. 4. Show that the diagonal entries of a skew-symmetric matrix are zero. 5. Let A, B be skew-symmetric matrices with AB = BA. Is the matrix AB symmetric or skew-symmetric? 6. Let A be a symmetric matrix of order n with A 2 =. Is it necessarily true that A =? 7. Let A be a nilpotent matrix. Show that there exists a matrix B such that B(I +A) = I = (I +A)B Submatrix of a Matrix Definition A matrix obtained by deleting some of the rows and/or columns of a matrix is said to be a submatrix of the given matrix. [ ] For example, if A =, a few submatrices of A are 1 2 [ ] [ ] [1],[2],,[1 5],, A. 2 [ ] [ ] But the matrices and arenotsubmatricesofa.(thereaderisadvisedtogivereasons.) 1 2 Miscellaneous Exercises Exercise Complete the proofs of Theorems and [ ] [ ] [ ] [ ] 1 cosθ sinθ 2. Let x =, y =, A = and B = 1 sinθ cosθ x 1 x 2 and y = Bx. y 1 y 2 3. Consider the two coordinate transformations x 1 = a 11 y 1 +a 12 y 2 y 1 = b 11 z 1 +b 12 z 2 and. x 2 = a 21 y 1 +a 22 y 2 y 2 = b 21 z 1 +b 22 z 2. Geometrically interpret y = Ax

15 1.3. SOME MORE SPECIAL MATRICES 15 (a) Compose the two transformations to express x 1,x 2 in terms of z 1,z 2. (b) If x t = [x 1, x 2 ], y t = [y 1, y 2 ] and z t = [z 1, z 2 ] then find matrices A,B and C such that x = Ay, y = Bz and x = Cz. (c) Is C = AB? 4. For a square matrix A of order n, we define trace of A, denoted by tr (A) as tr (A) = a 11 +a 22 + a nn. Then for two square matrices, A and B of the same order, show the following: (a) tr (A+B) = tr (A)+tr (B). (b) tr (AB) = tr (BA). 5. Show that, there do not exist matrices A and B such that AB BA = ci n for any c. 6. Let A and B be two m n matrices and let x be an n 1 column vector. (a) Prove that if Ax = for all x, then A is the zero matrix. (b) Prove that if Ax = Bx for all x, then A = B. 7. Let A be an n n matrix such that AB = BA for all n n matrices B. Show that A = αi for some α R Let A = 2 1. Show that there exist infinitely many matrices B such that BA = I 2. Also, show 3 1 that there does not exist any matrix C such that AC = I Block Matrices Let A be an n m matrix and B be an m [ p ] matrix. Suppose r < m. Then, we can decompose the H matrices A and B as A = [P Q] and B = ; where P has order n r and H has order r p. That K is, the matrices P and Q are submatrices of A and P consists of the first r columns of A and Q consists of the last m r columns of A. Similarly, H and K are submatrices of B and H consists of the first r rows of B and K consists of the last m r rows of B. We now prove the following important theorem. [ ] H Theorem Let A = [a ij ] = [P Q] and B = [b ij ] = be defined as above. Then K AB = PH +QK. Proof. First note that the matrices PH and QK are each of order n p. The matrix products PH and QK are valid as the order of the matrices P,H,Q and K are respectively, n r, r p, n (m r) and (m r) p. Let P = [P ij ], Q = [Q ij ], H = [H ij ], and K = [k ij ]. Then, for 1 i n and 1 j p, we have m r m (AB) ij = a ik b kj = a ik b kj + a ik b kj = k=1 r P ik H kj + k=1 k=1 m k=r+1 Q ik K kj k=r+1 = (PH) ij +(QK) ij = (PH +QK) ij.

16 16 CHAPTER 1. MATRICES Theorem is very useful due to the following reasons: 1. The order of the matrices P,Q,H and K are smaller than that of A or B. 2. It may be possible to block the matrix in such a way that a few blocks are either identity matrices or zero matrices. In this case, it may be easy to handle the matrix product using the block form. 3. Or when we want to prove results using induction, then we may assume the result for r r submatrices and then look for (r+1) (r+1) submatrices, etc. [ ] a b 1 2 For example, if A = and B = c d, Then 2 5 e f [ ][ ] [ ] [ ] 1 2 a b a+2c b+2d AB = + [e f] =. 2 5 c d 2a+5c 2b+5d 1 2 If A = 3 1 4, then A can be decomposed as follows: A = 3 1 4, or A = 3 1 4, or A = and so on. [ m 1 m ] 2 [ s 1 s 2 ] Suppose A = n 1 P Q and B = r 1 E F. Then the matrices P, Q, R, S and n 2 R S r 2 G H E, F, G, H, are called the blocks of the matrices A and B, respectively. Even if A+B is defined, the orders of P and E may not be same and hence, we [ may not be able ] P +E Q+F to add A and B in the block form. But, if A+B and P +E is defined then A+B =. R+G S +H Similarly, if the product AB is defined, the product PE need not be defined. Therefore, we can talk of matrix product AB as block [ product of matrices, ] if both the products AB and PE are defined. And PE +QG PF +QH in this case, we have AB =. RE +SG RF +SH That is, once a partition of A is fixed, the partition of B has to be properly chosen for purposes of block addition or multiplication. Exercise Compute the matrix product AB using the block matrix multiplication for the matrices A = 1 1 and B = [ ] P Q 2. Let A =. If P,Q,R and S are symmetric, what can you say about A? Are P,Q,R and S R S symmetric, when A is symmetric?

17 1.4. MATRICES OVER COMPLEX NUMBERS Let A = [a ij ] and B = [b ij ] be two matrices. Suppose a 1, a 2,..., a n are the rows of A and b 1, b 2,..., b p are the columns of B. If the product AB is defined, then show that a 1 B a 2 B AB = [Ab 1, Ab 2,..., Ab p ] =... a n B [That is, left multiplication by A, is same as multiplying each column of B by A. Similarly, right multiplication by B, is same as multiplying each row of A by B.] 1.4 Matrices over Complex Numbers Here the entries of the matrix are complex numbers. All the definitions still hold. One just needs to look at the following additional definitions. Definition (Conjugate Transpose of a Matrix) 1. Let A be an m nmatrix overc. If A = [a ij ] then the Conjugate of A, denoted by A, is the matrix B = [b ij ] with b ij = a ij. [ ] 1 4+3i i For example, Let A =. Then 1 i 2 A = [ ] 1 4 3i i. 1 i 2 2. Let A be an m n matrix over C. If A = [a ij ] then the Conjugate Transpose of A, denoted by A, is the matrix B = [b ij ] with b ij = a ji. [ ] 1 4+3i i For example, Let A =. Then 1 i 2 1 A = 4 3i 1. i i 2 3. A square matrix A over C is called Hermitian if A = A. 4. A square matrix A over C is called skew-hermitian if A = A. 5. A square matrix A over C is called unitary if A A = AA = I. 6. A square matrix A over C is called Normal if AA = A A. Remark If A = [a ij ] with a ij R, then A = A t. Exercise Give examples of Hermitian, skew-hermitian and unitary matrices that have entries with non-zero imaginary parts. 2. Restate the results on transpose in terms of conjugate transpose. 3. Show that for any square matrix A, S = A+A 2 is Hermitian, T = A A 2 is skew-hermitian, and A = S +T. 4. Show that if A is a complex triangular matrix and AA = A A then A is a diagonal matrix.

18 18 CHAPTER 1. MATRICES

19 Chapter 2 Linear System of Equations 2.1 Introduction Let us look at some examples of linear systems. 1. Suppose a,b R. Consider the system ax = b. (a) If a then the system has a unique solution x = b a. (b) If a = and i. b then the system has no solution. ii. b = then the system has infinite number of solutions, namely all x R. 2. We now consider a system with 2 equations in 2 unknowns. Consider the equation ax+by = c. If one of the coefficients, a or b is non-zero, then this linear equation represents a line in R 2. Thus for the system a 1 x+b 1 y = c 1 and a 2 x+b 2 y = c 2, the set of solutions is given by the points of intersection of the two lines. There are three cases to be considered. Each case is illustrated by an example. (a) Unique Solution x+2y = 1 and x+3y = 1. The unique solution is (x,y) t = (1,) t. Observe that in this case, a 1 b 2 a 2 b 1. (b) Infinite Number of Solutions x+2y = 1 and 2x+4y = 2. The set of solutions is (x,y) t = (1 2y,y) t = (1,) t +y( 2,1) t with y arbitrary. In other words, both the equations represent the same line. Observe that in this case, a 1 b 2 a 2 b 1 =, a 1 c 2 a 2 c 1 = and b 1 c 2 b 2 c 1 =. (c) No Solution x+2y = 1 and 2x+4y = 3. The equations represent a pair of parallel lines and hence there is no point of intersection. Observe that in this case, a 1 b 2 a 2 b 1 = but a 1 c 2 a 2 c As a last example, consider 3 equations in 3 unknowns. A linear equation ax+by +cz = d represent a plane in R 3 provided (a,b,c) (,,). As in the case of 2 equations in 2 unknowns, we have to look at the points of intersection of the given three planes. Here again, we have three cases. The three cases are illustrated by examples.

20 2 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS (a) Unique Solution Consider the system x+y+z = 3, x+4y+2z = 7 and 4x+1y z = 13. The unique solution to this system is (x,y,z) t = (1,1,1) t ; i.e. the three planes intersect at a point. (b) Infinite Number of Solutions Consider the system x + y + z = 3, x + 2y + 2z = 5 and 3x + 4y + 4z = 11. The set of solutions to this system is (x,y,z) t = (1,2 z,z) t = (1,2,) t +z(, 1,1) t, with z arbitrary: the three planes intersect on a line. (c) No Solution The system x+y +z = 3, x+2y +2z = 5 and 3x+4y +4z = 13 has no solution. In this case, we get three parallel lines as intersections of the above planes taken two at a time. The readers are advised to supply the proof. 2.2 Definition and a Solution Method Definition (Linear System) A linear system of m equations in n unknowns x 1,x 2,...,x n is a set of equations of the form a 11 x 1 +a 12 x 2 + +a 1n x n = b 1 a 21 x 1 +a 22 x 2 + +a 2n x n = b 2.. (2.2.1) a m1 x 1 +a m2 x 2 + +a mn x n = b m where for 1 i n, and 1 j m; a ij,b i R. Linear System (2.2.1) is called homogeneous if b 1 = = b 2 = = b m and non-homogeneous otherwise. We rewrite the above equations in the form Ax = b, where a 11 a 12 a 1n x 1 b 1 a 21 a 22 a 2n A = , x = x 2.., and b = b 2.. a m1 a m2 a mn x n b m The matrix A is called the coefficient matrix and the block matrix [A b], is the augmented matrix of the linear system (2.2.1). Remark Observe that the i th row of the augmented matrix [A b] represents the i th equation and the j th column of the coefficient matrix A corresponds to coefficients of the j th variable x j. That is, for 1 i m and 1 j n, the entry a ij of the coefficient matrix A corresponds to the i th equation and j th variable x j.. ForasystemoflinearequationsAx = b, thesystemax = iscalledtheassociated homogeneous system. Definition (Solution of a Linear System) Asolutionofthe linearsystem Ax = b is a columnvector y with entries y 1,y 2,...,y n such that the linear system (2.2.1) is satisfied by substituting y i in place of x i. That is, if y t = [y 1,y 2,...,y n ] then Ay = b holds. Note: The zero n-tuple x = is always a solution of the system Ax =, and is called the trivial solution. A non-zero n-tuple x, if it satisfies Ax =, is called a non-trivial solution.

21 2.3. ROW OPERATIONS AND EQUIVALENT SYSTEMS A Solution Method Example Let us solve the linear system x+7y +3z = 11, x+y +z = 3, and 4x+1y z = 13. Solution: 1. The above linear system and the linear system x+y +z = 3 Interchange the first two equations. x+7y +3z = 11 (2.2.2) 4x+1y z = 13 have the same set of solutions. (why?) 2. Eliminating x from 2 nd and 3 rd equation, we get the linear system x+y +z = 3 6y + 2z = 8 (obtained by subtracting the first equation from the second equation.) 6y 5z = 1 (obtained by subtracting 4 times the first equation from the third equation.) (2.2.3) This system and the system (2.2.2) has the same set of solution. (why?) 3. Eliminating y from the last two equations of system (2.2.3), we get the system x+y +z = 3 6y +2z = 8 7z = 7 obtained by subtracting the third equation which has the same set of solution as the system (2.2.3). (why?) 4. The system (2.2.4) and system x+y +z = 3 has the same set of solution. (why?) from the second equation. (2.2.4) 3y +z = 4 divide the second equation by 2 z = 1 divide the second equation by 2 (2.2.5) 5. Now, z = 1 implies y = 4 1 = 1 and x = 3 (1+1) = 1. Or in terms of a vector, the set of solution 3 is { (x,y,z) t : (x,y,z) = (1,1,1)}. 2.3 Row Operations and Equivalent Systems Definition (Elementary Operations) The following operations 1, 2 and 3 are called elementary operations. 1. interchange of two equations, say interchange the i th and j th equations ; (compare the system (2.2.2) with the original system.)

22 22 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS 2. multiply a non-zero constant throughout an equation, say multiply the k th equation by c ; (compare the system (2.2.5) and the system (2.2.4).) 3. replace an equation by itself plus a constant multiple of anotherequation, say replace the k th equation by k th equation plus c times the j th equation. (compare the system (2.2.3) with (2.2.2) or the system (2.2.4) with (2.2.3).) Observations: 1. In the above example, observe that the elementary operations helped us in getting a linear system (2.2.5), which was easily solvable. 2. Note that at Step 1, if we interchange the first and the second equation, we get back to the linear system from which we had started. This means the operation at Step 1, has an inverse operation. In other words, inverse operation sends us back to the step where we had precisely started. It will be a useful exercise for the reader to identify the inverse operations at each step in Example So, in Example 2.2.4, the application of a finite number of elementary operations helped us to obtain a simpler system whose solution can be obtained directly. That is, after applying a finite number of elementary operations, a simpler linear system is obtained which can be easily solved. Note that the three elementary operations defined above, have corresponding inverse operations, namely, 1. interchange the i th and j th equations, 2. divide the k th equation by c ; 3. replace the k th equation by k th equation minus c times the j th equation. It will be a useful exercise for the reader to identify the inverse operations at each step in Example Definition (Equivalent Linear Systems) Two linear systems are said to be equivalent if one can be obtained from the other by a finite number of elementary operations. The linear systems at each step in Example are equivalent to each other and also to the original linear system. Lemma Let Cx = d be the linear system obtained from the linear system Ax = b by a single elementary operation. Then the linear systems Ax = b and Cx = d have the same set of solutions. Proof. We prove the result for the elementary operation the k th equation is replaced by k th equation plus c times the j th equation. The reader is advised to prove the result for other elementary operations. In this case, the systems Ax = b and Cx = d vary only in the k th equation. Let (α 1,α 2,...,α n ) be a solution of the linear system Ax = b. Then substituting for α i s in place of x i s in the k th and j th equations, we get a k1 α 1 +a k2 α 2 + a kn α n = b k, and a j1 α 1 +a j2 α 2 + a jn α n = b j. Therefore, (a k1 +ca j1 )α 1 +(a k2 +ca j2 )α 2 + +(a kn +ca jn )α n = b k +cb j. (2.3.1) But then the k th equation of the linear system Cx = d is (a k1 +ca j1 )x 1 +(a k2 +ca j2 )x 2 + +(a kn +ca jn )x n = b k +cb j. (2.3.2)

23 2.3. ROW OPERATIONS AND EQUIVALENT SYSTEMS 23 Therefore, using Equation (2.3.1), (α 1,α 2,...,α n ) is also a solution for the k th Equation (2.3.2). Use a similar argument to show that if (β 1,β 2,...,β n ) is a solution of the linear system Cx = d then it is also a solution of the linear system Ax = b. Hence, we have the proof in this case. Lemma is now used as an induction step to prove the main result of this section (Theorem 2.3.4). Theorem Two equivalent systems have the same set of solutions. Proof. Let n be the number of elementary operations performed on Ax = b to get Cx = d. We prove the theorem by induction on n. If n = 1, Lemma answers the question. If n > 1, assume that the theorem is true for n = m. Now, suppose n = m+1. Apply the Lemma again at the last step (that is, at the (m+1) th step from the m th step) to get the required result using induction. Let us formalise the above section which led to Theorem For solving a linear system of equations, we applied elementary operations to equations. It is observed that in performing the elementary operations, the calculations were made on the coefficients (numbers). The variables x 1,x 2,...,x n and the sign of equality (that is, = ) are not disturbed. Therefore, in place of looking at the system of equations as a whole, we just need to work with the coefficients. These coefficients when arranged in a rectangular array gives us the augmented matrix [A b]. Definition (Elementary Row Operations) The elementary row operations are defined as: 1. interchange of two rows, say interchange the i th and j th rows, denoted R ij ; 2. multiply a non-zero constant throughout a row, say multiply the k th row by c, denoted R k (c); 3. replace a row by itself plus a constant multiple of another row, say replace the k th row by k th row plus c times the j th row, denoted R kj (c). Exercise Find the inverse row operations corresponding to the elementary row operations that have been defined just above. Definition (Row Equivalent Matrices) Two matrices are said to be row-equivalent if one can be obtained from the other by a finite number of elementary row operations. Example The three matrices given below are row equivalent R R 1 (1/2) Whereas the matrix is not row equivalent to the matrix

24 24 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS Gauss Elimination Method Definition (Forward/Gauss Elimination Method) Gaussian elimination is a method of solving a linear system Ax = b (consisting of m equations in n unknowns) by bringing the augmented matrix a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 [A b] = a m1 a m2 a mn b m to an upper triangular form c 11 c 12 c 1n d 1 c 22 c 2n d c mn d m This elimination process is also called the forward elimination method. The following examples illustrate the Gauss elimination procedure. Example Solve the linear system by Gauss elimination method. y +z = 2 2x+3z = 5 x+y +z = Solution: In this case, the augmented matrix is The method proceeds along the following steps. 1. Interchange 1 st and 2 nd equation (or R 12 ). 2x+3z = 5 y +z = 2 x+y +z = 3 2. Divide the 1 st equation by 2 (or R 1 (1/2)). x+ 3 2 z = 5 2 y +z = 2 x+y +z = Add 1 times the 1 st equation to the 3 rd equation (or R 31 ( 1)). x+ 3 2 z = y +z = y 1 2 z = Add 1 times the 2 nd equation to the 3 rd equation (or R 32 ( 1)). x+ 3 2 z = y +z = z =

25 2.3. ROW OPERATIONS AND EQUIVALENT SYSTEMS Multiply the 3 rd equation by 2 3 (or R 3( 2 3 )). x+ 3 2 z = 5 2 y +z = 2 z = The last equation gives z = 1, the second equation now gives y = 1. Finally the first equation gives x = 1. Hence the set of solutions is (x,y,z) t = (1,1,1) t, a unique solution. Example Solve the linear system by Gauss elimination method. x+y +z = 3 x+2y +2z = 5 3x+4y +4z = Solution: In this case, the augmented matrix is and the method proceeds as follows: Add 1 times the first equation to the second equation. x+y +z = 3 y +z = 2 3x+4y +4z = Add 3 times the first equation to the third equation. x+y +z = 3 y +z = 2 y +z = 2 3. Add 1 times the second equation to the third equation x+y +z = 3 y +z = Thus, the set of solutions is (x,y,z) t = (1,2 z,z) t = (1,2,) t +z(, 1,1) t, with z arbitrary. In other words, the system has infinite number of solutions. Example Solve the linear system by Gauss elimination method. x+y +z = 3 x+2y +2z = 5 3x+4y +4z = Solution: In this case, the augmented matrix is and the method proceeds as follows: Add 1 times the first equation to the second equation. x+y +z = 3 y +z = 2 3x+4y +4z =

26 26 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS 2. Add 3 times the first equation to the third equation. x+y +z = 3 y +z = 2 y +z = 3 3. Add 1 times the second equation to the third equation x+y +z = 3 y +z = 2 = The third equation in the last step is x+y +z = 1. This can never hold for any value of x,y,z. Hence, the system has no solution. Remark Note that to solve a linear system, Ax = b, one needs to apply only the elementary row operations to the augmented matrix [A b]. 2.4 Row Reduced Echelon Form of a Matrix Definition (Row Reduced Form of a Matrix) A matrix C is said to be in the row reduced form if 1. the first non-zero entry in each row of C is 1; 2. the column containing this 1 has all its other entries zero. A matrix in the row reduced form is also called a row reduced matrix. Example One of the most important examples of a row reduced matrix is the n n identity matrix, I n. Recall that the (i,j) th entry of the identity matrix is 1 if i = j I ij = δ ij = if i j.. δ ij is usually referred to as the Kronecker delta function The matrices 1 1 and 1 are also in row reduced form The matrix is not in the row reduced form. (why?) 1 1 Definition (Leading Term, Leading Column) For a row-reduced matrix, the first non-zero entry of any row is called a leading term. The columns containing the leading terms are called the leading columns.

29 2.4. ROW REDUCED ECHELON FORM OF A MATRIX 29 (a) x+y +z +w =, x y +z +w = and x+y +3z +3w =. (b) x+2y +3z = 1 and x+3y+2z = 1. (c) x+y +z = 3, x+y z = 1 and x+y +7z = 6. (d) x+y +z = 3, x+y z = 1 and x+y +4z = 6. (e) x+y +z = 3, x+y z = 1, x+y +4z = 6 and x+y 4z = Find the row-reduced echelon form of the following matrices , Elementary Matrices Definition A square matrix E of order n is called an elementary matrix if it is obtained by applying exactly one elementary row operation to the identity matrix, I n. Remark There are three types of elementary matrices. 1. E ij, which is obtained by the application of the elementary row operation R ij to the identity 1 if k = l and l i,j matrix, I n. Thus, the (k,l) th entry of E ij is (E ij ) (k,l) = 1 if (k,l) = (i,j) or (k,l) = (j,i). otherwise 2. E k (c), which is obtained by the application of the elementary row operation R k (c) to the identity 1 if i = j and i k matrix, I n. The (i,j) th entry of E k (c) is (E k (c)) (i,j) = c if i = j = k. otherwise 3. E ij (c), which is obtained by the application of the elementary row operation R ij (c) to the identity 1 if k = l matrix, I n. The (k,l) th entry of E ij (c) is (E ij ) (k,l) c if (k,l) = (i,j). otherwise In particular, 1 c 1 E 23 = 1, E 1 (c) = 1, and E 23 (c) = 1 c Example Let A = Then R = 1 A = E 23 A That is, interchanging the two rows of the matrix A is same as multiplying on the left by the corresponding elementary matrix. In other words, we see that the left multiplication of elementary matrices to a matrix results in elementary row operations.

30 3 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS Consider the augmented matrix [A b] = Then the result of the steps given below is same as the matrix product E 23 ( 1)E 12 ( 1)E 3 (1/3)E 32 (2)E 23 E 21 ( 2)E 13 [A b] R 13 R 32(2) R 23( 1) R 21( 2) R R 3(1/3) R 12( 1) Now, consider an m n matrix A and an elementary matrix E of order n. Then multiplying by E on the right to A corresponds to applying column transformation on the matrix A. Therefore, for each elementary matrix, there is a corresponding column transformation. We summarize: Definition The column transformations obtained by right multiplication of elementary matrices are called elementary column operations Example Let A = 2 3 and consider the elementary column operation f which interchanges the second and the third column of A. Then f(a) = 2 3 = A 1 = AE Exercise Let e be an elementary row operation and let E = e(i) be the corresponding elementary matrix. That is, E is the matrix obtained from I by applying the elementary row operation e. Show that e(a) = EA. 2. Show that the Gauss elimination method is same as multiplying by a series of elementary matrices on the left to the augmented matrix. Does the Gauss-Jordan method also corresponds to multiplying by elementary matrices on the left? Give reasons. 3. Let A and B be two m n matrices. Then prove that the two matrices A,B are row-equivalent if and only if B = PA, where P is product of elementary matrices. When is this P unique? 2.5 Rank of a Matrix In previous sections, we solved linear systems using Gauss elimination method or the Gauss-Jordan method. In the examples considered, we have encountered three possibilities, namely 1. existence of a unique solution, 2. existence of an infinite number of solutions, and

31 2.5. RANK OF A MATRIX no solution. Based on the above possibilities, we have the following definition. Definition (Consistent, Inconsistent) A linear system is called consistent if it admits a solution and is called inconsistent if it admits no solution. The question arises, as to whether there are conditions under which the linear system Ax = b is consistent. The answer to this question is in the affirmative. To proceed further, we need a few definitions and remarks. Recall that the row reduced echelon form of a matrix is unique and therefore, the number of non-zero rows is a unique number. Also, note that the number of non-zero rows in either the row reduced form or the row reduced echelon form of a matrix are same. Definition (Row rank of a Matrix) The number of non-zero rows in the row reduced form of a matrix is called the row-rank of the matrix. By the very definition, it is clear that row-equivalent matrices have the same row-rank. For a matrix A, we write row-rank (A) to denote the row-rank of A Example Determine the row-rank of A = Solution: To determine the row-rank of A, we proceed as follows (a) R 21 ( 2),R 31 ( 1) (b) R 2 ( 1),R 32 (1) (c) R 3 (1/2),R 12 ( 2) (d) R 23 ( 1),R 13 (1) ThelastmatrixinStep1distherowreducedformofAwhichhas3non-zerorows. Thus,row-rank(A) = 3. This result can also be easily deduced from the last matrix in Step 1b Determine the row-rank of A = Solution: Here we have (a) R 21 ( 2),R 31 ( 1) (b) R 2 ( 1),R 32 (1)

32 32 CHAPTER 2. LINEAR SYSTEM OF EQUATIONS From the last matrix in Step 2b, we deduce row-rank(a) = 2. Remark LetAx = bbealinearsystemwithmequationsandnunknowns. Thentherow-reduced echelon form of A agrees with the first n columns of [A b], and hence The reader is advised to supply a proof. row-rank(a) row-rank([a b]). Remark Consider a matrix A. After application of a finite number of elementary column operations (see Definition ) to the matrix A, we can have a matrix, say B, which has the following properties: 1. The first nonzero entry in each column is A column containing only s comes after all columns with at least one non-zero entry. 3. The first non-zero entry (the leading term) in each non-zero column moves down in successive columns. Therefore, we can define column-rank of A as the number of non-zero columns in B. It will be proved later that Thus we are led to the following definition. row-rank(a) = column-rank(a). Definition The number of non-zero rows in the row reduced form of a matrix A is called the rank of A, denoted rank (A). Theorem Let A be a matrix of rank r. Then there exist elementary matrices E 1,E 2,...,E s and F 1,F 2,...,F l such that [ ] I r E 1 E 2...E s A F 1 F 2...F l =. Proof. Let C be the row reduced echelon matrix obtained by applying elementary row operations to the given matrix A. As rank(a) = r, the matrix C will have the first r rows as the non-zero rows. So by Remark 2.4.5, C will have r leading columns, say i 1,i 2,...,i r. Note that, for 1 s r, the i th s column will have 1 in the s th row and zero elsewhere. We now apply column operations to the matrix C. Let D be the matrix obtained from C by successively[ interchanging ] the s th and i th s column of C for 1 s r. Then the matrix D can be written in the I r B form, where B is a matrix of appropriate size. As the (1,1) block of D is an identity matrix, the block (1,2) can be made the zero matrix by application of column operations to D. This gives the required result. Exercise Determine the ranks of the coefficient and the augmented matrices that appear in Part 1 and Part 2 of Exercise For any matrix A, prove that rank(a) = rank(a t ). 3. Let A be an n n matrix with rank(a) = n. Then prove that A is row-equivalent to I n.

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