MATHEMATICS. IMPORTANT FORMULAE AND CONCEPTS for. Final Revision CLASS XII CHAPTER WISE CONCEPTS, FORMULAS FOR QUICK REVISION.

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1 MATHEMATICS IMPORTANT FORMULAE AND CONCEPTS for Final Revision CLASS XII CHAPTER WISE CONCEPTS, FORMULAS FOR QUICK REVISION Prepared by M. S. KUMARSWAMY, TGT(MATHS) M. Sc. Gold Medallist (Elect.), B. Ed. Kendriya Vidyalaya GaCHIBOWLI Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 1 -

2 Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 1 -

3 CHAPTER 1: RELATIONS AND FUNCTIONS QUICK REVISION (Important Concepts & Formulae) Relation Let A and B be two sets. Then a relation R from A to B is a subset of A B. R is a relation from A to B R A B. Total Number of Relations Let A and B be two nonempty finite sets consisting of m and n elements respectively. Then A B consists of mn ordered pairs. So, total number of relations from A to B is 2 nm. Domain and range of a relation Let R be a relation from a set A to a set B. Then the set of all first components or coordinates of the ordered pairs belonging to R is called the domain of R, while the set of all second components or coordinates of the ordered pairs in R is called the range of R. Thus, Dom (R) = {a : (a, b) R} and Range (R) = {b : (a, b) R}. Inverse relation Let A, B be two sets and let R be a relation from a set A to a set B. Then the inverse of R, denoted by R 1, is a relation from B to A and is defined by R 1 = {(b, a) : (a, b) R}. Types of Relations Void relation : Let A be a set. Then A A and so it is a relation on A. This relation is called the void or empty relation on A. It is the smallest relation on set A. Universal relation : Let A be a set. Then A A A A and so it is a relation on A. This relation is called the universal relation on A. It is the largest relation on set A. Identity relation : Let A be a set. Then the relation I A = {(a, a) : a A} on A is called the identity relation on A. Reflexive Relation : A relation R on a set A is said to be reflexive if every element of A is related to itself. Thus, R reflexive (a, a) R a A. A relation R on a set A is not reflexive if there exists an element a A such that (a, a) R. Symmetric relation : A relation R on a set A is said to be a symmetric relation iff (a, b) R (b, a) R for all a, b A. i.e. arb bra for all a, b A. A relation R on a set A is not a symmetric relation if there are atleast two elements a, b A such that (a, b) R but (b, a) R. Transitive relation : A relation R on A is said to be a transitive relation iff (a, b) R and (b, c) R (a, c) R for all a, b, c A. i.e. arb and brc arc for all a, b, c A. Antisymmetric relation : A relation R on set A is said to be an antisymmetric relation iff (a, b) R and (b, a) R a = b for all a, b A. Equivalence relation : A relation R on a set A is said to be an equivalence relation on A iff It is reflexive i.e. (a, a) R for all a A. It is symmetric i.e. (a, b) R (b, a) R for all a, b A. Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 2 -

4 It is transitive i.e. (a, b) R and (b, c) R (a, c) R for all a, b, c A. Congruence modulo m Let m be an arbitrary but fixed integer. Two integers a and b are said to be congruence modulo m if a b is divisible by m and we write a b(mod m). Thus, a b (mod m) a b is divisible by m. Some Results on Relations If R and S are two equivalence relations on a set A, then R S is also an equivalence relation on A. The union of two equivalence relations on a set is not necessarily an equivalence relation on the set. If R is an equivalence relation on a set A, then R 1 is also an equivalence relation on A. Composition of relations Let R and S be two relations from sets A to B and B to C respectively. Then we can define a relation SoR from A to C such that (a, c) SoR b B such that (a, b) R and (b, c) S. This relation is called the composition of R and S. Functions Let A and B be two empty sets. Then a function 'f ' from set A to set B is a rule or method or correspondence which associates elements of set A to elements of set B such that (i) All elements of set A are associated to elements in set B. (ii) An element of set A is associated to a unique element in set B. A function f from a set A to a set B associates each element of set A to a unique element of set B. If an element a A is associated to an element b B, then b is called 'the f image of a or 'image of a under f or 'the value of the function f at a'. Also, a is called the preimage of b under the function f. We write it as : b = f (a). Domain, CoDomain and Range of a function Let f : AB. Then, the set A is known as the domain of f and the set B is known as the codomain of f. The set of all f images of elements of A is known as the range of f or image set of A under f and is denoted by f (A). Thus, f (A) = {f (x) : x A} = Range of f. Clearly, f (A) B. Equal functions Two functions f and g are said to be equal iff (i) The domain of f = domain of g (ii) The codomain of f = the codomain of g, and (iii) f (x) = g(x) for every x belonging to their common domain. If two functions f and g are equal, then we write f = g. Types of Functions (i) Oneone function (injection) A function f : A B is said to be a oneone function or an injection if different elements of A have different images in B. Thus, f : A B is oneone a b f (a) f (b) for all a, b A f (a) = f (b) a = b for all a, b A. Algorithm to check the injectivity of a function Step I : Take two arbitrary elements x, y (say) in the domain of f. Step II : Put f (x) = f (y) Step III : Solve f (x) = f (y). If f (x) = f (y) gives x = y only, then f : A B is a oneone function (or an injection) otherwise not. Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 3 -

5 Graphically, if any straight line parallel to x-axis intersects the curve y = f (x) exactly at one point, then the function f (x) is oneone or an injection. Otherwise it is not. If f : R R is an injective map, then the graph of y = f (x) is either a strictly increasing curve or a dy dy strictly decreasing curve. Consequently, 0 or 0 for all x. dx dx n Pm, if n m Number of oneone functions from A to B, 0, if n m where m = n(domain) and n = n(codomain) (ii) Ontofunction (surjection) A function f : AB is said to be an onto function or a surjection if every element of B is the fimage of some element of A i.e., if f (A) = B or range of f is the codomain of f. Thus, f : A B is a surjection iff for each b B, a A that f (a) = b. Algorithm for Checking the Surjectivity of a Function Let f : A B be the given function. Step I : Choose an arbitrary element y in B. Step II : Put f (x) = y. Step III : Solve the equation f (x) = y for x and obtain x in terms of y. Let x = g(y). Step IV : If for all values of y B, for which x, given by x = g(y) are in A, then f is onto. If there are some y B for which x, given by x = g(y) is not in A. Then, f is not onto. Number of onto functions :If A and B are two sets having m and n elements respectively such that 1 n m, then number of onto functions from A to B is n r1 nr n ( 1). C r (iii) Bijection (oneone onto function) A function f : A B is a bijection if it is oneone as well as onto. In other words, a function f : A B is a bijection if r m (i) It is oneone i.e. f (x) = f (y) x = y for all x, y A. (ii) It is onto i.e. for all y B, there exists x A such that f (x) = y. Number of bijections : If A and B are finite sets and f : A B is a bijection, then A and B have the same number of elements. If A has n elements, then the number of bijections from A to B is the total number of arrangements of n items taken all at a time i.e. n! (iv) Manyone function A function f : A B is said to be a manyone function if two or more elements of set A have the same image in B. f : AB is a manyone function if there exist x, y A such that x y but f (x) = f ( y). Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 4 -

6 (v) Into function A function f : AB is an into function if there exists an element in B having no preimage in A. In other words f : A B is an into function if it is not an onto function. (vi) Identity function Let A be a nonempty set. A function f : AA is said to be an identity function on set A if f associates every element of set A to the element itself. Thus f : A A is an identity function iff f (x) = x, for all x A. (vii) Constant function A function f : A B is said to be a constant function if every element of A has the same image under function of B i.e. f (x) = c for all x A, where c B. Composition of functions Let A, B and C be three nonvoid sets and let f : A B, g : B C be two functions. For each x A there exists a unique element g( f (x)) C. The composition of functions is not commutative i.e. fog gof. The composition of functions is associative i.e. if f, g, h are three functions such that (fog)oh and fo(goh) exist, then (fog)oh = fo(goh). The composition of two bijections is a bijection i.e. if f and g are two bijections, then gof is also a bijection. Let f : AB. The foi A = I B of = f i.e. the composition of any function with the identity function is the function itself. Inverse of an element Let A and B be two sets and let f : A B be a mapping. If a A is associated to b B under the function f, then b is called the f image of a and we write it as b = f (a). Inverse of a function If f : A B is a bijection, we can define a new function from B to A which associates each element y B to its preimage f 1 (y) A. Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 5 -

7 Algorithm to find the inverse of a bijection Let f : A B be a bijection. To find the inverse of f we proceed as follows : Step I : Put f (x) = y, where y B and x A. Step II : Solve f (x) = y to obtain x in terms of y. Step III : In the relation obtained in step II replace x by f 1 (y) to obtain the inverse of f. Properties of Inverse of a Function (i) The inverse of a bijection is unique. (ii) The inverse of a bijection is also a bijection. (iii) If f : A B is a bijection and g : B A is the inverse of f, then fog = I B and gof = I A, where I A and I B are the identity functions on the sets A and B respectively. If in the above property, we have B = A. Then we find that for every bijection f : A A there exists a bijection g : A A such that fog = gof = I A. (iv) Let f : A B and g : B A be two functions such that gof = I A and fog = I B. Then f and g are bijections and g = f 1. Binary Operation Let S be a nonvoid set. A function f from S S to S is called a binary operation on S i.e. f : S S S is a binary operation on set S. Generally binary operations are represented by the symbols *,,. etc. instead of letters f, g etc. Addition on the set N of all natural numbers is a binary operation. Subtraction is a binary operation on each of the sets Z, Q, R and C. But, it is a binary operation on N. Division is not a binary operation on any of the sets N, Z, Q, R and C. However, it is not a binary operation on the sets of all nonzero rational (real or complex) numbers. Types of Binary Operations (i) Commutative binary operation A binary operation * on a set S is said to be commutative if a * b = b * a for all a, b S Addition and multiplication are commutative binary operations on Z but subtraction is not a commutative binary operation, since Union and intersection are commutative binary operations on the power set P(S) of all subsets of set S. But difference of sets is not a commutative binary operation on P(S). (ii) Associative binary operation A binary operation * on a set S is said to be associative if (a * b) * c = a * (b * c) for all a, b, c S. (iii) Distributive binary operation Let * and o be two binary operations on a set S. Then * is said to be (i) Left distributive over o if a*(b o c) = (a * b) o (a * c) for all a, b, c S (ii) Right distributive over o if (b o c) * a = (b * a) o (c * a) for all a, b, c S. (iv) Identity element Let * be a binary operation on a set S. An element e S is said to be an identity element for the binary operation * if a * e = a = e * a for all a S. For addition on Z, 0 is the identity element, since 0 + a = a = a + 0 for all a R. For multiplication on R, 1 is the identity element, since 1 a = a = a 1 for all a R. For addition on N the identity element does not exist. But for multiplication on N the identity element is 1. Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 6 -

8 (v) Inverse of an element Let * be a binary operation on a set S and let e be the identity element in S for the binary operation *. An element a S is said to be an inverse of a S, if a * a= e = a* a. Addition on N has no identity element and accordingly N has no invertible element. Multiplication on N has 1 as the identity element and no element other than 1 is invertible. Let S be a finite set containing n elements. Then the total number of binary operations on S is Let S be a finite set containing n elements. Then the total number of commutative binary operation n( n 1) on S is n 2. 2 n n. Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 7 -

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10 CHAPTER 2: INVERSE TRIGONOMETRIC FUNCTIONS QUICK REVISION (Important Concepts & Formulae) Inverse Trigonometrical Functions A function f : A B is invertible if it is a bijection. The inverse of f is denoted by f 1 and is defined as f 1 (y) = x f (x) = y. Clearly, domain of f 1 = range of f and range of f 1 = domain of f. The inverse of sine function is defined as sin 1 x = sinq = x, where [ /2, /2] and x [ 1, 1]. Thus, sin 1 x has infinitely many values for given x [ 1, 1] There is one value among these values which lies in the interval [ /2, /2]. This value is called the principal value. Domain and Range of Inverse Trigonometrical Functions Properties of Inverse Trigonometrical Functions sin 1 (sin) = and sin(sin 1 x) = x, provided that 1 x 1 and 2 2 cos 1 (cos) = and cos (cos 1 x) = x, provided that 1 x 1 and 0 tan 1 (tan) = and tan(tan 1 x) = x, provided that x and 2 2 cot 1 (cot) = and cot(cot 1 x) = x, provided that < x < and 0 < <. sec 1 (sec) = and sec(sec 1 x) = x cosec 1 (cosec) = and cosec(cosec 1 x) = x, 1 1 x x 1 sin cos 1 x ec or cos ec 1 x sin 1 Prepared by: M. S. KumarSwamy, TGT(Maths) Page - 9 -

11 1 1 x x 1 cos s 1 x ec or sec 1 x cos x x 1 tan cot 1 x or cot 1 x tan x 1 1 x sin x cos 1 x tan cot sec cosec x x 1 x x x 1 x cos x sin 1 x tan cot cos ec s ec x x 1 x x 1 1 x x tan x sin cos cot sec 1 x cos ec x 1 x x x sin x cos x, where 1 x tan x cot x, where x sec x cos ec x, where x 1 or x x y tan x tan y tan, if xy 1 1 xy x y tan x tan y tan, if xy 1 1 xy x y 1 xy tan 1 x tan 1 y tan 1 sin 1 x sin 1 y sin 1 x 1 y 2 y 1 x 2, if x, y 0, x 2 y 2 1 sin 1 x sin 1 y sin 1 x 1 y 2 y 1 x 2, if x, y 0, x 2 y 2 1 sin 1 x sin 1 y sin 1 x 1 y 2 y 1 x 2, if x, y 0, x 2 y 2 1 sin 1 x sin 1 y sin 1 x 1 y 2 y 1 x 2, if x, y 0, x 2 y 2 1 cos 1 x cos 1 y cos 1 xy 1x 2 1 y 2, if x, y 0, x 2 y Prepared by: M. S. KumarSwamy, TGT(Maths) Page

12 cos 1 x cos 1 y cos 1 xy 1x 2 1 y 2, if x, y 0, x 2 y 2 1 cos 1 x cos 1 y cos 1 xy 1x 2 1 y 2, if x, y 0, x 2 y 2 1 cos 1 x cos 1 y cos 1 xy 1x 2 1 y 2, if x, y 0, x 2 y sin ( ) sin 1, cos 1 ( ) cos 1 x x x x 1 tan ( ) tan 1, cot 1 ( ) cot 1 x x x x 2sin 1 x sin 1 2x 1 x 2, 2cos 1 x cos 1 2x x 1 2x 1 1 x 2 tan x tan sin cos x 1 x 1 x sin x sin 3x 4 x, 3cos x cos 1 4x 3 3x x x 3tan x tan 2 1 3x Prepared by: M. S. KumarSwamy, TGT(Maths) Page

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14 CHAPTER 3: MATRICES QUICK REVISION (Important Concepts & Formulae) Matrix A matrix is an ordered rectangular array of numbers or functions. The numbers or functions are called the elements or the entries of the matrix. We denote matrices by capital letters. Order of a matrix A matrix having m rows and n columns is called a matrix of order m n or simply m n matrix (read as an m by n matrix). In general, an m n matrix has the following rectangular array: a11 a12... a1 n a21 a22... a 2n am1 am 2... a mn or A = [a ij ] m n, 1 i m, 1 j n i, j N Thus the ith row consists of the elements a i1, a i2, a i3,..., a in, while the j th column consists of the elements a 1j, a 2j, a 3j,..., a mj, In general a ij, is an element lying in the i th row and j th column. We can also call it as the (i, j) th element of A. The number of elements in an m n matrix will be equal to mn. x or x y y We can also represent any point (x, y) in a plane by a matrix (column or row) as, Types of Matrices (i) Column matrix A matrix is said to be a column matrix if it has only one column. In general, A = [a ij ] m 1 is a column matrix of order m 1. (ii) Row matrix A matrix is said to be a row matrix if it has only one row. In general, B = [b ij ] 1 n is a row matrix of order 1 n. (iii) Square matrix A matrix in which the number of rows are equal to the number of columns, is said to be a square matrix. Thus an m n matrix is said to be a square matrix if m = n and is known as a square matrix of order n. In general, A = [a ij ] m m is a square matrix of order m. If A = [a ij ] is a square matrix of order n, then elements (entries) a 11, a 22,..., a nn are said to constitute the diagonal, of the matrix A. (iv) Diagonal matrix A square matrix B = [b ij ] m m is said to be a diagonal matrix if all its non diagonal elements are zero, that is a matrix B = [b ij ] m m is said to be a diagonal matrix if b ij = 0, when i j. (v) Scalar matrix Prepared by: M. S. KumarSwamy, TGT(Maths) Page

15 A diagonal matrix is said to be a scalar matrix if its diagonal elements are equal, that is, a square matrix B = [b ij ] n n is said to be a scalar matrix if b ij = 0, when i j b ij = k, when i = j, for some constant k. (vi) Identity matrix A square matrix in which elements in the diagonal are all 1 and rest are all zero is called an identity 1if i j matrix. In other words, the square matrix A = [aij] n n is an identity matrix, if aij 0if i j We denote the identity matrix of order n by I n. When order is clear from the context, we simply write it as I. Observe that a scalar matrix is an identity matrix when k = 1. But every identity matrix is clearly a scalar matrix. (vii) Zero matrix A matrix is said to be zero matrix or null matrix if all its elements are zero. We denote zero matrix by O. Equality of matrices Two matrices A = [a ij ] and B = [b ij ] are said to be equal if (i) they are of the same order (ii) each element of A is equal to the corresponding element of B, that is a ij = b ij for all i and j. Operations on Matrices Addition of matrices The sum of two matrices is a matrix obtained by adding the corresponding elements of the given matrices. Furthermore, the two matrices have to be of the same order. a11 a12 a13 b11 b12 b13 Thus, if A = is a 2 3 matrix and B = is another 2 3 matrix. Then, a21 a22 a23 b21 b22 b23 we define a11 b11 a12 b12 a13 b13 A + B =. a21 b21 a22 b22 a23 b23 In general, if A = [a ij ] and B = [b ij ] are two matrices of the same order, say m n. Then, the sum of the two matrices A and B is defined as a matrix C = [c ij ] m n, where c ij = a ij + b ij, for all possible values of i and j. If A and B are not of the same order, then A + B is not defined. Multiplication of a matrix by a scalar If A = [a ij ] m n is a matrix and k is a scalar, then ka is another matrix which is obtained by multiplying each element of A by the scalar k. In other words, ka = k [a ij ] m n = [k (a ij )] m n, that is, (i, j)th element of ka is ka ij for all possible values of i and j. Negative of a matrix The negative of a matrix is denoted by A. We define A = ( 1) A. Difference of matrices If A = [a ij ], B = [b ij ] are two matrices of the same order, say m n, then difference A B is defined as a matrix D = [d ij ], where d ij = a ij b ij, for all value of i and j. In other words, D = A B = A + ( 1) B, that is sum of the matrix A and the matrix B. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

16 Properties of matrix addition (i) Commutative Law If A = [a ij ], B = [b ij ] are matrices of the same order, say m n, then A + B = B + A. (ii) Associative Law For any three matrices A = [a ij ], B = [b ij ], C = [c ij ] of the same order, say m n, (A + B) + C = A + (B + C). (iii) Existence of additive identity Let A = [a ij ] be an m n matrix and O be an m n zero matrix, then A + O = O + A = A. In other words, O is the additive identity for matrix addition. (iv) The existence of additive inverse Let A = [a ij ] m n be any matrix, then we have another matrix as A = [ a ij ] m n such that A + ( A) = ( A) + A= O. So A is the additive inverse of A or negative of A. Properties of scalar multiplication of a matrix If A = [a ij ] and B = [b ij ] be two matrices of the same order, say m n, and k and l are scalars, then (i) k(a +B) = k A + kb, (ii) (k + l)a = k A + l A Multiplication of matrices The product of two matrices A and B is defined if the number of columns of A is equal to the number of rows of B. Let A = [a ij ] be an m n matrix and B = [b jk ] be an n p matrix. Then the product of the matrices A and B is the matrix C of order m p. To get the (i, k) th element c ik of the matrix C, we take the ith row of A and k th column of B, multiply them elementwise and take the sum of all these products. In other words, if A = [a ij ] m n, B = [b jk ] n b1 k b 2k p, then the ith row of A is [a i1 a i2... a in ] and the kth column of B is. then. b nk c ik = a i1 b 1k + a i2 b 2k + a i3 b 3k a in b nk = The matrix C = [c ik ] m p is the product of A and B. If AB is defined, then BA need not be defined. In the above example, AB is defined but BA is not defined because B has 3 column while A has only 2 (and not 3) rows. If A, B are, respectively m n, k l matrices, then both AB and BA are defined if and only if n = k and l = m. In particular, if both A and B are square matrices of the same order, then both AB and BA are defined. Non-commutativity of multiplication of matrices Now, we shall see by an example that even if AB and BA are both defined, it is not necessary that AB = BA. Zero matrix as the product of two non zero matrices We know that, for real numbers a, b if ab = 0, then either a = 0 or b = 0. If the product of two matrices is a zero matrix, it is not necessary that one of the matrices is a zero matrix. Properties of multiplication of matrices The multiplication of matrices possesses the following properties: The associative law For any three matrices A, B and C. We have (AB) C = A (BC), whenever both sides of the equality are defined. The distributive law For three matrices A, B and C. A (B+C) = AB + AC (A+B) C = AC + BC, whenever both sides of equality are defined. The existence of multiplicative identity For every square matrix A, there exist an identity matrix of same order such that IA = AI = A. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

17 Transpose of a Matrix If A = [a ij ] be an m n matrix, then the matrix obtained by interchanging the rows and columns of A is called the transpose of A. Transpose of the matrix A is denoted by A or (A T ). In other words, if A = [a ij ] m n, then A = [a ji ] n m. Properties of transpose of the matrices For any matrices A and B of suitable orders, we have (i) (A ) = A, (ii) (ka) = ka (where k is any constant) (iii) (A + B) = A + B (iv) (A B) = B A Symmetric and Skew Symmetric Matrices A square matrix A = [a ij ] is said to be symmetric if A = A, that is, [a ij ] = [a ji ] for all possible values of i and j. A square matrix A = [a ij ] is said to be skew symmetric matrix if A = A, that is aji = aij for all possible values of i and j. Now, if we put i = j, we have a ii = a ii. Therefore 2a ii = 0 or a ii = 0 for all i s. This means that all the diagonal elements of a skew symmetric matrix are zero. Theorem 1 For any square matrix A with real number entries, A + A is a symmetric matrix and A A is a skew symmetric matrix. Theorem 2 Any square matrix can be expressed as the sum of a symmetric and a skew symmetric matrix. Elementary Operation (Transformation) of a Matrix There are six operations (transformations) on a matrix, three of which are due to rows and three due to columns, which are known as elementary operations or transformations. (i) The interchange of any two rows or two columns. Symbolically the interchange of ith and jth rows is denoted by R i R j and interchange of ith and jth column is denoted by C i C j. (ii) The multiplication of the elements of any row or column by a non zero number. Symbolically, the multiplication of each element of the ith row by k, where k 0 is denoted by R i k R i. The corresponding column operation is denoted by C i kc i (iii) The addition to the elements of any row or column, the corresponding elements of any other row or column multiplied by any non zero number. Symbolically, the addition to the elements of i th row, the corresponding elements of j th row multiplied by k is denoted by R i R i + kr j. The corresponding column operation is denoted by C i C i + kc j. Invertible Matrices If A is a square matrix of order m, and if there exists another square matrix B of the same order m, such that AB = BA = I, then B is called the inverse matrix of A and it is denoted by A 1. In that case A is said to be invertible. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

18 A rectangular matrix does not possess inverse matrix, since for products BA and AB to be defined and to be equal, it is necessary that matrices A and B should be square matrices of the same order. If B is the inverse of A, then A is also the inverse of B. Theorem 3 (Uniqueness of inverse) Inverse of a square matrix, if it exists, is unique. Theorem 4 If A and B are invertible matrices of the same order, then (AB) 1 = B 1 A 1. Inverse of a matrix by elementary operations If A is a matrix such that A 1 exists, then to find A 1 using elementary row operations, write A = IA and apply a sequence of row operation on A = IA till we get, I = BA. The matrix B will be the inverse of A. Similarly, if we wish to find A 1 using column operations, then, write A = AI and apply a sequence of column operations on A = AI till we get, I = AB. In case, after applying one or more elementary row (column) operations on A = IA (A = AI), if we obtain all zeros in one or more rows of the matrix A on L.H.S., then A 1 does not exist. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

19 Prepared by: M. S. KumarSwamy, TGT(Maths) Page

20 CHAPTER 4: DETERMINANTS QUICK REVISION (Important Concepts & Formulae) Determinant a b If A = c d, then determinant of A is written as A = a b = det (A) or Δ c d (i) For matrix A, A is read as determinant of A and not modulus of A. (ii) Only square matrices have determinants. Determinant of a matrix of order one Let A = [a ] be the matrix of order 1, then determinant of A is defined to be equal to a Determinant of a matrix of order two a11 a12 Let A = a21 a be a matrix of order 2 2, then the determinant of A is defined as: 22 a11 a12 det (A) = A = Δ = = a11a 22 a21a12 a a Determinant of a matrix of order 3 3 Determinant of a matrix of order three can be determined by expressing it in terms of second order determinants. This is known as expansion of a determinant along a row (or a column). There are six ways of expanding a determinant of order 3 corresponding to each of three rows (R 1, R 2 and R 3 ) and three columns (C 1, C 2 and C 3 ) giving the same value as shown below. Consider the determinant of square matrix A = [a ij ] 3 3 a a a i.e., A = a a a a a a Expansion along first Row (R 1 ) Step 1 Multiply first element a 11 of R 1 by ( 1) (1 + 1) [( 1) sum of suffixes in a11 ] and with the second order determinant obtained by deleting the elements of first row (R 1 ) and first column (C 1 ) of A as a 11 lies in R 1 and C 1, a22 a i.e., ( 1) a11 a a Step 2 Multiply 2nd element a 12 of R 1 by ( 1) [( 1) sum of suffixes in a12 ] and the second order determinant obtained by deleting elements of first row (R 1 ) and 2nd column (C 2 ) of A as a 12 lies in R 1 and C 2, a21 a i.e., ( 1) a12 a a Step 3 Multiply third element a13 of R1 by ( 1) [( 1) sum of suffixes in a13 ] and the second order determinant obtained by deleting elements of first row (R 1 ) and third column (C 3 ) of A as a 13 lies in R 1 and C 3, Prepared by: M. S. KumarSwamy, TGT(Maths) Page

21 i.e., ( 1) a a a13 a 31 a 32 Step 4 Now the expansion of determinant of A, that is, A written as sum of all three terms obtained in steps 1, 2 and 3 above is given by a22 a23 a a23 a a 22 A ( 1) a11 ( 1) a12 ( 1) a13 a a a a a a or A = a 11 (a 22 a 33 a 32 a 23 ) a 12 (a 21 a 33 a 31 a 23 ) + a 13 (a 21 a 32 a 31 a 22 ) Expansion along second row (R 2 ) A = a a a a a a a a a Expanding along R 2, we get A ( 1) a a a ( 1) a a a ( 1) a a a Expansion along first Column (C 1 ) a32 a33 a31 a33 a31 a32 A = a a a a a a a a a By expanding along C 1, we get a a a a a a A ( 1) a11 ( 1) a21 ( 1) a31 a a a a a a For easier calculations, we shall expand the determinant along that row or column which contains maximum number of zeros. While expanding, instead of multiplying by ( 1) i + j, we can multiply by +1 or 1 according as (i + j) is even or odd. If A = kb where A and B are square matrices of order n, then A = k n B, where n = 1, 2, 3 Properties of Determinants Property 1 The value of the determinant remains unchanged if its rows and columns are interchanged. if A is a square matrix, then det (A) = det (A ), where A = transpose of A. If R i = i th row and C i = i th column, then for interchange of row and columns, we will symbolically write C i R i Property 2 If any two rows (or columns) of a determinant are interchanged, then sign of determinant changes. We can denote the interchange of rows by R i R j and interchange of columns by C i C j. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

22 Property 3 If any two rows (or columns) of a determinant are identical (all corresponding elements are same), then value of determinant is zero. Property 4 If each element of a row (or a column) of a determinant is multiplied by a constant k, then its value gets multiplied by k. o By this property, we can take out any common factor from any one row or any one column of a given determinant. o If corresponding elements of any two rows (or columns) of a determinant are proportional (in the same ratio), then its value is zero. Property 5 If some or all elements of a row or column of a determinant are expressed as sum of two (or more) terms, then the determinant can be expressed as sum of two (or more) determinants. Property 6 If, to each element of any row or column of a determinant, the equimultiples of corresponding elements of other row (or column) are added, then value of determinant remains the same, i.e., the value of determinant remain same if we apply the operation R i R i + kr j or C i C i + k C j. If Δ 1 is the determinant obtained by applying R i kr i or C i kc i to the determinant Δ, then Δ 1 = kδ. If more than one operation like R i R i + kr j is done in one step, care should be taken to see that a row that is affected in one operation should not be used in another operation. A similar remark applies to column operations. Area of triangle Area of a triangle whose vertices are (x 1, y 1 ), (x 2, y 2 ) and (x 3, y 3 ), is given by the expression x1 y1 1 1 x2 y2 1 (1) 2 x y Since area is a positive quantity, we always take the absolute value of the determinant in (1). If area is given, use both positive and negative values of the determinant for calculation. The area of the triangle formed by three collinear points is zero. Minors and Cofactors Minor of an element a ij of a determinant is the determinant obtained by deleting its i th row and j th column in which element a ij lies. Minor of an element a= is denoted by M ij. Minor of an element of a determinant of order n(n 2) is a determinant of order n 1. Cofactor of an element a ij, denoted by A ij is defined by Aij = ( 1) i + j M ij, where M ij is minor of a ij. If elements of a row (or column) are multiplied with cofactors of any other row (or column), then their sum is zero. Adjoint and Inverse of a Matrix The adjoint of a square matrix A = [a ij ] n n is defined as the transpose of the matrix [A ij ] n n, where A ij is the cofactor of the element a ij. Adjoint of the matrix A is denoted by adj A. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

23 a11 a12 For a square matrix of order 2, given by a21 a 22 The adj A can also be obtained by interchanging a 11 and a 22 and by changing signs of a 12 and a 21, i.e., Theorem 1 If A be any given square matrix of order n, then A(adj A) = (adj A) A = A I, where I is the identity matrix of order n A square matrix A is said to be singular if A = 0. A square matrix A is said to be non-singular if A 0 Theorem 2 If A and B are nonsingular matrices of the same order, then AB and BA are also nonsingular matrices of the same order. Theorem 3 The determinant of the product of matrices is equal to product of their respective determinants, that is, AB = A B, where A and B are square matrices of the same order If A is a square matrix of order n, then adj(a) = A n 1. Theorem 4 A square matrix A is invertible if and only if A is nonsingular matrix. Let A is a non-singular matrix then we have A 0 A is invertible and A adja A 1 1 Applications of Determinants and Matrices Application of determinants and matrices for solving the system of linear equations in two or three variables and for checking the consistency of the system of linear equations: Consistent system A system of equations is said to be consistent if its solution (one or more) exists. Inconsistent system A system of equations is said to be inconsistent if its solution does not exist. Solution of system of linear equations using inverse of a matrix Consider the system of equations a 1 x + b 1 y + c 1 z = d 1 a 2 x + b 2 y + c 2 z = d 2 a 3 x + b 3 y + c 3 z = d 3 a1 b1 c1 x d1 Let A a b c, X y and B d a3 b3 c 3 z d 3 Then, the system of equations can be written as, AX = B, i.e., Prepared by: M. S. KumarSwamy, TGT(Maths) Page

24 a1 b1 c1 x d1 a b c y d a3 b3 c 3 z d 3 Case I If A is a nonsingular matrix, then its inverse exists. Now AX = B or A 1 (AX) = A 1 B (premultiplying by A 1 ) or (A 1 A) X = A 1 B (by associative property) or I X = A 1 B or X = A 1 B This matrix equation provides unique solution for the given system of equations as inverse of a matrix is unique. This method of solving system of equations is known as Matrix Method. Case II If A is a singular matrix, then A = 0. In this case, we calculate (adj A) B. If (adj A) B O, (O being zero matrix), then solution does not exist and the system of equations is called inconsistent. If (adj A) B = O, then system may be either consistent or inconsistent according as the system have either infinitely many solutions or no solution. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

25 Prepared by: M. S. KumarSwamy, TGT(Maths) Page

26 CHAPTER 5: CONTINUITY & DIFFERENTIABILITY QUICK REVISION (Important Concepts & Formulae) Continuity at a Point : A function f (x) is said to be continuous at a point x = a of its domain, if and only if lim f ( x) f ( a). x a Continuity on an open interval : A function f (x) is said to be continuous on an open interval (a, b) if and only if it is continuous at every point on the interval (a, b). Continuity on a closed interval : A function f (x) is said to be continuous on a closed interval [a, b] if and only if (i) f is continuous on the open interval (a, b). lim (ii) f ( x) f ( a) x a lim (iii) f ( x) f ( a) x a In other words, f (x) is continuous on [a, b] if and only if it is continuous on (a, b) and it is continuous at a from the right and at b from the left. Continuous Function : A function f (x) is said to be continuous, if it is continuous at each point of its domain. Everywhere Continuous Function : A function f (x) is said to be everywhere continuous if it is continuous on the entire real line ( ). Theorem Suppose f and g be two real functions continuous at a real number c. Then (1) f + g is continuous at x = c. (2) f g is continuous at x = c. (3) f. g is continuous at x = c. (4) f g is continuous at x = c, (provided g (c) 0). Discontinuous Functions A function f is said to be discontinuous at a point a of its domain D if it is not continuous at a. The point a is then called a point of discontinuity of the function. The discontinuity may arise due to any of the following situations: lim lim f ( x) or f ( x) both may not exist. x a x a lim lim f ( x) as well as f ( x) may exist, but are unequal. x a x a lim lim f ( x) as well as f ( x) both may exist, but either of the two or both may not be equal x a x a to f (a). Prepared by: M. S. KumarSwamy, TGT(Maths) Page

27 Removable Discontinuity : A function f is said to have removable discontinuity at x = a if lim lim f ( x) f ( x) but their common value is not equal to f (a). x a x a Differentiability at a Point Let f (x) be a real valued function defined on an open interval (a, b) and let c ( a, b). Then f (x) is said lim f ( x) f ( c) to be differentiable or derivable at x = c, if and only if exists finitely. x c x c f (x) is differentiable at x = c Lf '( c) Rf '( c). If Lf '( c) Rf '( c), we say that f (x) is not differentiable at x = c. f (x) is differentiable at point P, if and only if there exists a unique tangent at point P. In other words, f (x) is differentiable at a point P if and only if the curve does not have P as a corner point. f (x) is differentiable at x = c f(x) is continuous at x = c. Differentiability in a Set A function f (x) defined on an open interval (a, b) is said to be differentiable or derivable in open interval (a, b) if it is differentiable at each point of (a, b). Some Standard Results on Differentiability I. Every polynomial function is differentiable at each x R. II. The exponential function a x, a > 0 is differentiable at each x R. III. Every constant function is differentiable at each x R. IV. The logarithmic function is differentiable at each point in its domain. V. Trigonometric and inverse trigonometric functions are differentiable in their domains. VI. The sum, difference, product and quotient of two differentiable functions is differentiable. VII. The composition of differentiable functions is a differentiable function. Derivative : The rate of change of a function with respect to the independent variable. For the function y = f (x) it is denoted by dy dx Differentiation : The process of obtaining the derivative of a function by considering small changes in the function and independent variable, and finding the limiting value of the ratio of such changes. d lim ( ) ( ) lim ( ) ( ) ( f ( x)) f x h f x, d ( f ( x)) f x h f x dx x c h dx x c h Geometrically Meaning of Derivative at a Point Geometrically derivative of a function at a point x = c is the slope of the tangent to the curve y = f (x) at the point (c, f (c)). Slope of tangent at P = lim f ( x) f ( c) df ( x) x c x c dx x c Prepared by: M. S. KumarSwamy, TGT(Maths) Page

28 d Differentiation of a constant function is zero i.e. ( c ) 0 dx Let f (x) be a differentiable function and let c be a constant. Then c.f (x) is also differentiable such d d that { c. f ( x)} c. f ( x) dx dx That is the derivative of a constant times a function is the constant times the derivatives of the function. If f (x) and g(x) are differentiable functions, then f (x) ± g(x) are also differentiable such that d [ f ( x) g( x)] d f ( x) d g( x) dx dx dx That is the derivative of the sum or difference of two functions is the sum or difference of their derivatives. Product Rule : If f (x) and g(x) are two differentiable functions, then f (x).g(x) is also differentiable such that d [ f ( x). g( x)] f ( x) d g( x) g( x) d f ( x) dx dx dx That is, derivative of the product of two functions = [(First function) (derivative of second function) + (second function) (derivative of first function)]. Quotient Rule : If f (x) and g(x) are two differentiable functions and g(x) 0, then differentiable such that d d g( x) f ( x) f ( x) g( x) d f ( x) dx dx dx g( x) g( x) 2 f ( x) is also g( x) Prepared by: M. S. KumarSwamy, TGT(Maths) Page

29 Differentiation of Implicit Functions When it is not possible to express y as a function of x in the form of y = f(x), then y is said to be an implicit function of x. To find the derivative in such case we differentiate both sides of the given relation with respect of x. Differentiation of Logarithmic Functions y = f (x) g(x) = e g(x).log{f (x)} and then differentiating with respect to x, we may get dy g ( x)log{ f ( x)} 1 d d e g( x). f ( x) log{ f ( x)} g( x) dx f ( x) dx dx g ( x ( ) ) g ( x ) d d f x f ( x) log{ f ( x)} g( x) f ( x) dx dx Differentiation of Parametric Functions When x and y are given as functions of a single variable, i.e., x = f(t), y = g(t) are two functions and t is a variable. Then dy dy dt. dx dx dt Differentiation of a Function with respect to Another Function Let u = f (x) and v = g(x) be two functions of x. Then to find the derivative of f (x) w.r.t. g(x) i.e., to find du du du dx we use the formula dv dv dv dx Thus, to find the derivative of f (x) w.r.t. g(x), we first differentiate both w.r.t. x and then divide the derivative of f (x) w.r.t. x by the derivative of g(x) w.r.t. x. Rolle's Theorem Statement : Let f be a real valued function defined on the closed interval [a, b] such that (i) It is continuous on the closed interval [a, b] (ii) It is differentiable on the open interval (a, b) (iii) f (a) = f (b). Then there exists a real number c ( a, b) such that f (c) = 0. Algebraic Interpretation of Rolle's Theorem : Between any two roots of a polynomial f (x), there is always a root of its derivative f (x). Lagrange's Mean Value Theorem Statement : Let f (x) be a function defined on [a, b] such that (i) It is continuous on [a, b]. (ii) It is differentiable on (a, b). (iii) f (a) f (b) f ( b) f ( a) Then there exists a real number c ( a, b) such that f (c) = b a Geometrical Interpretation of Lagrange's Mean Value Theorem : Let f (x) be a function defined on [a, b] such that the curve y = f (x) is a continuous curve between points A(a, f (a)) and B(b, f (b)) and at every point on the curve, except at the end points, it is possible to draw a unique tangent. Then there exists a point on the curve such that the tangent at this is parallel to the chord joining the end points of the curve. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

30 Prepared by: M. S. KumarSwamy, TGT(Maths) Page

31 CHAPTER 6: APPLICATION OF DERIVATIVES QUICK REVISION (Important Concepts & Formulae) Rate of Change of Quantities If a quantity y varies with another quantity x, satisfying some rule y = f (x), then f (x) represents the rate of change of y with respect to x and f (h) represents the rate of change of y with respect to x at x = h. dy is positive if y increases as x increases and is negative if y decreases as x increases. dx Strictly Increasing Function : A function f (x) is said to be a strictly increasing function on (a, b) if x1 x2 f ( x1 ) f ( x2) for all x1, x2 ( a, b). Strictly Decreasing Function : A function f (x) is said to be a strictly decreasing function on (a, b) if x1 x2 f ( x1 ) f ( x2) for all x1, x2 ( a, b). Monotonic Function : A function f (x) is said to be monotonic on an interval (a, b) if it is either increasing or decreasing on (a, b). A function f (x) is said to be increasing (decreasing) at point x 0 if there is an interval (x 0 h, x 0 + h ) containing x 0 such that f (x) is increasing (decreasing) on (x 0 h, x 0 + h). A function f (x) is said to be increasing on [a, b] if it is increasing (decreasing) on (a, b) and it is also increasing at x = a and x = b. If f (x) is increasing function on (a, b), then tangent at every point on the curve y = f (x) makes an acute angle q with the positive direction of x-axis. Let f be a differentiable real function defined on an open interval (a, b). (a) If f (x) > 0 for all x ( a, b), then f (x) is increasing on (a, b). (b) If f'(x) < 0 for all x ( a, b), then f (x) is decreasing on (a, b). Let f be a function defined on (a, b). (a) If f'(x) > 0 for all x ( a, b) except for a finite number of points, where f (x) = 0, then f (x) is increasing on (a, b). (b) If f'(x) < 0 for all x ( a, b) except for a finite number of points, where f (x) = 0, then f (x) is decreasing on (a, b). Slope of Tangent If a tangent line to the curve y = f (x) makes an angle θ with x-axis in the positive direction, then dy dx = slope of the tangent tan θ. Algorithm for Finding The Equation of Tangent and Normal to The Curve y = f (x) at the given Point (x 0, y 0 ) Step I : Find dy from the given equation y = f (x). dx Step II : Find the value of dy dx at the given point P(x 0, y 0 ). Prepared by: M. S. KumarSwamy, TGT(Maths) Page

32 Step III : The equation of the tangent at (x 0, y 0 ) to the curve y = f (x) is given by dy y y0 ( x x0 ) dx ( x0, y0 ) The equation of the normal to the curve y = f (x) at a point (x 0, y 0 ) is given by 1 y y0 ( x x0 ) dy dx ( x, y ) 0 0 If dy dx does not exist at the point (x 0, y 0 ), then the tangent at this point is parallel to the y-axis and its equation is x = x 0. dy If tangent to a curve y = f (x) at x = x 0 is parallel to x-axis, then 0 dx If dy dx at the point (x 0, y 0 ) is zero, then equation of the normal is x = x 0. If dy dx at the point (x 0, y 0 ) does not exist, then the normal is parallel to x-axis and its equation is y = y 0. Particular cases (i) If slope of the tangent line is zero, then tan θ = 0 and so θ = 0 which means the tangent line is parallel to the x-axis. In this case, the equation of the tangent at the point (x 0, y 0 ) is given by y = y 0. (ii) If, then tan θ, which means the tangent line is perpendicular to the x-axis, i.e., parallel to 2 the y-axis. In this case, the equation of the tangent at (x 0, y 0 ) is given by x = x 0 Angle of Intersection of Two Curves The angle of intersection of two curves is defined to be the angle between the tangents to the two curves at their point of intersection. The other angle between the tangents is 180. Generally the smaller of these two angles is taken to be the angle of intersection. Orthogonal Curves If the angle of intersection of two curves is a right angle, the two curves are said to intersect orthogonally and the curves are called orthogonal curves. Approximation Let y = f (x), Δx be a small increment in x and Δy be the increment in y corresponding to the increment in x, i.e., Δy = f (x + Δx) f (x). Then dy given by dy dy f '( x) dx or dy dx is a good approximation of Δy when dx = Δx is relatively small and we dx denote it by dy Δy. Maximum Let f (x) be a function with domain D R. Then f (x) is said to attain the maximum value at a point a D, if f (x) f (a) for all x D. In such a case, a is called point of maxima and f (a) is known as the maximum value or the greatest value or the absolute maximum value of f (x). xx0 Prepared by: M. S. KumarSwamy, TGT(Maths) Page

33 Minimum Let f (x) be a function with domain D R. Then f (x) is said to attain the minimum value at a point a D, if f (x) f (a) for all x D In such a case, a is called point of minima and f (a) is known as the minimum value or the least value or the absolute minimum value of f (x). Local Maximum : A function f (x) is said to attain a local maximum at x = a if there exists a neighbourhood ( a, a ) of a such that, f (x) < f (a) for all x ( a, a ), x a or, f (x) f (a) < 0 for all x ( a, a ),x a. In such a case f (a) is called to attain a local maximum value of f (x) at x = a. Local Minimum : f (x) > f (a) for all x ( a, a ), x a or f (x) f (a) > 0 for all x ( a, a ), x a. In such a case f(a) is called the local minimum value of f (x) at x = a. If c is a point of local maxima of f, then f (c) is a local maximum value of f. Similarly, if c is a point of local minima of f, then f(c) is a local minimum value of f. A point c in the domain of a function f at which either f (c) = 0 or f is not differentiable is called a critical point of f. Note that if f is continuous at c and f (c) = 0, then there exists an h > 0 such that f is differentiable in the interval (c h, c + h). First Derivative Test for Local Maxima and Minima Let f be a function defined on an open interval I. Let f be continuous at a critical point c in I. Then (i) If f (x) changes sign from positive to negative as x increases through c, i.e., if f (x) > 0 at every point sufficiently close to and to the left of c, and f (x) < 0 at every point sufficiently close to and to the right of c, then c is a point of local maxima. (ii) If f (x) changes sign from negative to positive as x increases through c, i.e., if f (x) < 0 at every point sufficiently close to and to the left of c, and f (x) > 0 at every point sufficiently close to and to the right of c, then c is a point of local minima. (iii)if f (x) does not change sign as x increases through c, then c is neither a point of local maxima nor a point of local minima. Infact, such a point is called point of inflexion. Algorithm for Determining Extreme Values of a Function by using First Derivative Test Step I : Put y = f (x) Step II : Find dy dx. Step III : Put dy dx = 0 and solve this equation for x. Let c 1, c 2, c 3,.be the roots of the equation. c 1, c 2, c 3,. are stationary values of x and these are the possible points where the function can attain a local maximum or a local minimum. So we test the function at each of these points. Step IV : Consider x = c 1, if dy changes its sign from positive to negative as x increases through c 1, dx then the function attains a local maximum at x = c 1. If dy changes its sign from negative to positive as x increases through c 1, then the function attains dx a local minimum at x = c 1. If dy dx does not change sign as x increase through c 1, then x = c 1 is neither a points of local maximum nor a point of local minimum. In this case x = c 1 is a point inflexion. Prepared by: M. S. KumarSwamy, TGT(Maths) Page

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2

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