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      Class 12 MATHS – JEE

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      • Class 12
      • Class 12 MATHS – JEE
      CoursesClass 12MathsClass 12 MATHS – JEE
      • 1.Relations and Functions
        11
        • Lecture1.1
          Revision – Functions and its Types 38 min
        • Lecture1.2
          Revision – Functions Types 17 min
        • Lecture1.3
          Revision – Sum Related to Relations 04 min
        • Lecture1.4
          Revision – Sums Related to Relations, Domain and Range 22 min
        • Lecture1.5
          Cartesian Product of Sets, Relation, Domain, Range, Inverse of Relation, Types of Relations 34 min
        • Lecture1.6
          Functions, Intervals 39 min
        • Lecture1.7
          Domain 01 min
        • Lecture1.8
          Problem Based on finding Domain and Range 39 min
        • Lecture1.9
          Types of Real function 31 min
        • Lecture1.10
          Odd & Even Function, Composition of Function 32 min
        • Lecture1.11
          Chapter Notes – Relations and Functions
      • 2.Inverse Trigonometric Functions
        16
        • Lecture2.1
          Revision – Introduction, Some Identities and Some Sums 16 min
        • Lecture2.2
          Revision – Some Sums Related to Trigonometry Identities, trigonometry Functions Table and Its Quadrants 35 min
        • Lecture2.3
          Revision – Trigonometrical Identities-Some important relations and Its related Sums 16 min
        • Lecture2.4
          Revision – Sums Related to Trigonometrical Identities 18 min
        • Lecture2.5
          Revision – Some Trigonometric Identities and its related Sums 42 min
        • Lecture2.6
          Revision – Trigonometry Equations 44 min
        • Lecture2.7
          Revision – Sum Based on Trigonometry Equations 08 min
        • Lecture2.8
          Introduction to Inverse Trigonometry Function, Range, Domain, Question based on Principal Value 37 min
        • Lecture2.9
          Property -1 of Inverse trigo function 28 min
        • Lecture2.10
          Property -2 to 4 of Inverse trigo function 48 min
        • Lecture2.11
          Questions based on properties of Inverse trigo function 19 min
        • Lecture2.12
          Question based on useful substitution 27 min
        • Lecture2.13
          Numerical problems 19 min
        • Lecture2.14
          Numerical problems 24 min
        • Lecture2.15
          Numerical problems , introduction to Differentiation 44 min
        • Lecture2.16
          Chapter Notes – Inverse Trigonometric Functions
      • 3.Matrices
        9
        • Lecture3.1
          What is matrix 26 min
        • Lecture3.2
          Types of matrix 28 min
        • Lecture3.3
          Operations of matrices 28 min
        • Lecture3.4
          Multiplication of matrices 29 min
        • Lecture3.5
          Properties of a matrices 44 min
        • Lecture3.6
          Numerical problems 19 min
        • Lecture3.7
          Solution of simultaneous linear equation 28 min
        • Lecture3.8
          Solution of simultaneous / Homogenous linear equation 24 min
        • Lecture3.9
          Chapter Notes – Matrices
      • 4.Determinants
        6
        • Lecture4.1
          Introduction of Determinants 23 min
        • Lecture4.2
          Properties of Determinants 29 min
        • Lecture4.3
          Numerical problems 15 min
        • Lecture4.4
          Numerical problems 16 min
        • Lecture4.5
          Applications of Determinants 17 min
        • Lecture4.6
          Chapter Notes – Determinants
      • 5.Continuity
        7
        • Lecture5.1
          Introduction to continuity 28 min
        • Lecture5.2
          Numerical problems 17 min
        • Lecture5.3
          Numerical problems 22 min
        • Lecture5.4
          Basics of continuity 26 min
        • Lecture5.5
          Numerical problems 17 min
        • Lecture5.6
          Numerical problems 11 min
        • Lecture5.7
          Chapter Notes – Continuity and Differentiability
      • 6.Differentiation
        14
        • Lecture6.1
          Introduction to Differentiation 27 min
        • Lecture6.2
          Important formula’s 29 min
        • Lecture6.3
          Numerical problems 29 min
        • Lecture6.4
          Numerical problems 31 min
        • Lecture6.5
          Differentiation by using trigonometric substitution 21 min
        • Lecture6.6
          Differentiation of implicit function 21 min
        • Lecture6.7
          Differentiation of logarthmetic function 31 min
        • Lecture6.8
          Differentiation of log function 25 min
        • Lecture6.9
          Infinite series & parametric function 26 min
        • Lecture6.10
          Infinite series & parametric function 27 min
        • Lecture6.11
          Higher order derivatives 27 min
        • Lecture6.12
          Differentiation of function of a function 16 min
        • Lecture6.13
          Numerical problems 27 min
        • Lecture6.14
          Numerical problems 04 min
      • 7.Mean Value Theorem
        4
        • Lecture7.1
          Lagrange theorem 24 min
        • Lecture7.2
          Rolle’s theorem 20 min
        • Lecture7.3
          Lagrange theorem 24 min
        • Lecture7.4
          Rolle’s theorem 20 min
      • 8.Applications of Derivatives
        6
        • Lecture8.1
          Rate of change of Quantities 30 min
        • Lecture8.2
          Rate of change of Quantities 18 min
        • Lecture8.3
          Rate of change of Quantities 18 min
        • Lecture8.4
          Approximation 10 min
        • Lecture8.5
          Approximation 05 min
        • Lecture8.6
          Chapter Notes – Applications of Derivatives
      • 9.Increasing and Decreasing Function
        5
        • Lecture9.1
          Introduction 36 min
        • Lecture9.2
          Numerical Problem 26 min
        • Lecture9.3
          Numerical Problem 22 min
        • Lecture9.4
          Numerical Problem 22 min
        • Lecture9.5
          Numerical Problem 21 min
      • 10.Tangents and Normal
        3
        • Lecture10.1
          Introduction 34 min
        • Lecture10.2
          Numerical Problems 32 min
        • Lecture10.3
          Angle of intersection of two curves 27 min
      • 11.Maxima and Minima
        10
        • Lecture11.1
          Introduction 28 min
        • Lecture11.2
          Local maxima & Local Minima 27 min
        • Lecture11.3
          Numerical Problems 37 min
        • Lecture11.4
          Maximum & minimum value in closed interval 19 min
        • Lecture11.5
          Application of Maxima & Minima 10 min
        • Lecture11.6
          Application of Maxima & Minima 14 min
        • Lecture11.7
          Numerical Problems 17 min
        • Lecture11.8
          Numerical Problems 18 min
        • Lecture11.9
          Numerical Problems 15 min
        • Lecture11.10
          Numerical Problems 14 min
      • 12.Integrations
        19
        • Lecture12.1
          Introduction to Indefinite Integration 37 min
        • Lecture12.2
          Integration by substitution 25 min
        • Lecture12.3
          Numerical problems on Substitution 39 min
        • Lecture12.4
          Numerical problems on Substitution 04 min
        • Lecture12.5
          Integration various types of particular function (Identities) 31 min
        • Lecture12.6
          Integration by parts-1 18 min
        • Lecture12.7
          Integration by parts-2 10 min
        • Lecture12.8
          Integration by parts-2 16 min
        • Lecture12.9
          Integration by parts-2 08 min
        • Lecture12.10
          ILATE Rule 12 min
        • Lecture12.11
          Integration of some special function 07 min
        • Lecture12.12
          Integration of some special function 06 min
        • Lecture12.13
          Integration by substitution using trigonometric 14 min
        • Lecture12.14
          Evaluation of some specific Integration 12 min
        • Lecture12.15
          Evaluation of some specific Integration 29 min
        • Lecture12.16
          Integration by partial fraction 27 min
        • Lecture12.17
          Integration of some special function 11 min
        • Lecture12.18
          Numerical Problems based on partial fraction 20 min
        • Lecture12.19
          Chapter Notes – Integrals
      • 13.Definite Integrals
        11
        • Lecture13.1
          Introduction 24 min
        • Lecture13.2
          Properties of Definite Integration 19 min
        • Lecture13.3
          Numerical problem based on properties 22 min
        • Lecture13.4
          Area under the curve 16 min
        • Lecture13.5
          Area under the curve (Ellipse) 20 min
        • Lecture13.6
          Area under the curve (Parabola) 10 min
        • Lecture13.7
          Area under the curve (Parabola & Circle) 40 min
        • Lecture13.8
          Area bounded by lines 10 min
        • Lecture13.9
          Numerical problems 25 min
        • Lecture13.10
          Area under the curve (Circle ) 02 min
        • Lecture13.11
          Chapter Notes – Application of Integrals
      • 14.Differential Equations
        6
        • Lecture14.1
          Introduction to chapter 38 min
        • Lecture14.2
          Solution of D.E. – Variable separation methods 14 min
        • Lecture14.3
          Solution of D.E. – Variable separation methods 27 min
        • Lecture14.4
          Solution of D.E. – Second order 21 min
        • Lecture14.5
          Homogeneous D.E. 31 min
        • Lecture14.6
          Chapter Notes – Differential Equations
      • 15.Vectors
        12
        • Lecture15.1
          Introduction , Basic concepts , types of vector 34 min
        • Lecture15.2
          Position vector, distance between two points, section formula 44 min
        • Lecture15.3
          Numerical problem 02 min
        • Lecture15.4
          collinearity of points and coplanarity of vector 34 min
        • Lecture15.5
          Direction cosine 18 min
        • Lecture15.6
          Projection , Dot product, Cauchy- Schwarz inequality 24 min
        • Lecture15.7
          Numerical problem (dot product) 20 min
        • Lecture15.8
          Vector (Cross) product , Lagrange’s Identity 15 min
        • Lecture15.9
          Numerical problem (cross product) 38 min
        • Lecture15.10
          Numerical problem (cross product) 06 min
        • Lecture15.11
          Numerical problem (cross product) 22 min
        • Lecture15.12
          Chapter Notes – Vectors
      • 16.Three Dimensional Geometry
        7
        • Lecture16.1
          Introduction to 3D, axis in 3D, plane in 3D, Distance between two points 32 min
        • Lecture16.2
          Numerical problems , section formula , centroid of a triangle 35 min
        • Lecture16.3
          projection , angle between two lines 40 min
        • Lecture16.4
          Numerical Problem based on Direction ratio & cosine 02 min
        • Lecture16.5
          locus of any point 15 min
        • Lecture16.6
          Numerical Problem based on locus 16 min
        • Lecture16.7
          Chapter Notes – Three Dimensional Geometry
      • 17.Direction Cosine
        2
        • Lecture17.1
          Introduction 34 min
        • Lecture17.2
          Angle Between two vectors 25 min
      • 18.Plane
        3
        • Lecture18.1
          Introduction to plane , general equation of a plane , normal form 31 min
        • Lecture18.2
          Angle between two planes 30 min
        • Lecture18.3
          Distance of a point from a plane 29 min
      • 19.Straight Lines
        22
        • Lecture19.1
          Revision – Introduction, Equation of Line, Slope or Gradient of a line 24 min
        • Lecture19.2
          Revision – Sums Related to Finding the Slope, Angle Between two Lines 22 min
        • Lecture19.3
          Revision – Cases for Angle B/w two Lines, Different forms of Line Equation 23 min
        • Lecture19.4
          Revision – Sums Related Finding the Equation of Line 27 min
        • Lecture19.5
          Revision – Sums based on Previous Concepts of Straight line 32 min
        • Lecture19.6
          Revision – Parametric Form of a Straight Line 16 min
        • Lecture19.7
          Revision – Sums Related to Parametric Form of a Straight Line 17 min
        • Lecture19.8
          Revision – Sums Based on Concurrent of lines, Angle b/w Two Lines 45 min
        • Lecture19.9
          Revision – Different condition for Angle b/w two lines 04 min
        • Lecture19.10
          Revision – Sums Based on Angle b/w Two Lines 36 min
        • Lecture19.11
          Revision – Equation of Straight line Passes Through a Point and Make an Angle with Another Line 09 min
        • Lecture19.12
          Revision – Sums Based on Equation of Straight line Passes Through a Point and Make an Angle with Another Line 15 min
        • Lecture19.13
          Revision – Sums Based on Equation of Straight line Passes Through a Point and Make an Angle with Another Line 17 min
        • Lecture19.14
          Revision – Finding the Distance of a point from the line 35 min
        • Lecture19.15
          Revision – Sum Based on Finding the Distance of a point from the line and B/w Two Parallel Lines 33 min
        • Lecture19.16
          Introduction to straight line , symmetric form , Angle between the lines 27 min
        • Lecture19.17
          Numerical Problem 18 min
        • Lecture19.18
          Angle between two lines 32 min
        • Lecture19.19
          Unsymmetric form of Line 26 min
        • Lecture19.20
          Numerical problem , perpendicular distance of a point from a line 22 min
        • Lecture19.21
          Numerical Problem 21 min
        • Lecture19.22
          Numerical problem , Condition for a line lie on a plane 26 min
      • 20.Straight Lines (Vector)
        4
        • Lecture20.1
          Vector and Cartesian equation of a straight line 27 min
        • Lecture20.2
          Angle between two straight line 25 min
        • Lecture20.3
          Numerical problems 37 min
        • Lecture20.4
          Shortest Distance between two lines 22 min
      • 21.Linear Programming
        5
        • Lecture21.1
          Introduction to L.P. 30 min
        • Lecture21.2
          Numerical Problems 43 min
        • Lecture21.3
          Numerical Problems 23 min
        • Lecture21.4
          Numerical Problems 17 min
        • Lecture21.5
          Chapter Notes – Linear Programming
      • 22.Probability
        23
        • Lecture22.1
          Introduction to probability 41 min
        • Lecture22.2
          Types of events 42 min
        • Lecture22.3
          Numerical problems 30 min
        • Lecture22.4
          Conditional probability 12 min
        • Lecture22.5
          Numerical problems 09 min
        • Lecture22.6
          Numerical problems (conditional Probability) 04 min
        • Lecture22.7
          Numerical problems (conditional Probability) 06 min
        • Lecture22.8
          Numerical problems (conditional Probability) 05 min
        • Lecture22.9
          Numerical problems (conditional Probability) 06 min
        • Lecture22.10
          Bayes’ Theorem 17 min
        • Lecture22.11
          Numerical problem ( conditional Probability) 04 min
        • Lecture22.12
          Numerical problem ( Baye’s Theorem) 19 min
        • Lecture22.13
          Numerical problem ( Baye’s Theorem) 18 min
        • Lecture22.14
          Numerical problem ( Baye’s Theorem) 10 min
        • Lecture22.15
          Mean and Variance of a random variable 09 min
        • Lecture22.16
          Mean and Variance of a random variable 09 min
        • Lecture22.17
          Mean and Variance of a random variable 08 min
        • Lecture22.18
          Mean and Variance of a discrete random variable 07 min
        • Lecture22.19
          Numerical problem 18 min
        • Lecture22.20
          Bernoulli’s Trials & Binomial Distribution 11 min
        • Lecture22.21
          Numerical problem 12 min
        • Lecture22.22
          Mean and Variance of Binomial Distribution 05 min
        • Lecture22.23
          Chapter Notes – Probability
      • 23.Limits
        4
        • Lecture23.1
          Introduction to limits 35 min
        • Lecture23.2
          Numerical problems 27 min
        • Lecture23.3
          Rationalization 33 min
        • Lecture23.4
          Limits in trigonometry 29 min
      • 24.Partial Fractions
        4
        • Lecture24.1
          Introduction to partial fraction 27 min
        • Lecture24.2
          Partial Fractions 02 29 min
        • Lecture24.3
          Partial Fractions 03 17 min
        • Lecture24.4
          Improper partial fraction 20 min

        Chapter Notes – Applications of Derivatives

        Tangents and Normals

        The derivative of the curve y = f(x) is f ‗(x) which represents the slope of tangent and equation of the tangent to the curve at P is

        Tangents and Normals

        where (x, y) is an arbitrary point on the tangent.

        Tangents and Normals

        The equation of normal at (x, y) to the curve is

        Tangents and Normals

        1. IfTangents and Normalsthen the equations of the tangent and normal at (x, y) are (Y – y) = 0 and (X – x) = 0, respectively.
        2. IfTangents and Normalsthen the equation of the tangent and normal at (x, y) are (X – x) = 0 and (Y – y) = 0, respectively.

        Slope of Tangent

        (i) If the tangent at P is perpendicular to x-axis or parallel to y-axis,

        Slope of Tangent

        (ii) If the tangent at P is perpendicular to y-axis or parallel to x-axis,

        Slope of Tangent

        Slope of Normal

        Slope of Normal

        (ii) IfSlope of Normal, then normal at (x, y) is parallel to y-axis and perpendicular to x-axis.

        (iii) IfSlope of Normalthen normal at (x, y) is parallel to x-axis and perpendicular to y-axis.

         

        Length of Tangent and Normal

        (i) Length of tangent, PA = y cosec θ =

        Length of Tangent and Normal

        (ii) Length of normal,

        Length of Tangent and Normal

        (iii) Length of subtangent,

        Length of Tangent and Normal

        (iv) Length of subnormal,

        Length of Tangent and Normal

        Length of Tangent and Normal

        Angle of Intersection of Two Curves

        Let y = f1(x) and y = f2(x) be the two curves, meeting at some point P (x1, y1), then the angle between the two curves at P (x1, y1) = The angle between the tangents to the curves at P (x1, y1)

        Angle of Intersection of Two Curves

        The other angle between the tangents is (180 — θ). Generally, the smaller of these two angles is taken to be the angle of intersection.
        ∴ The angle of intersection of two curves θ is given by

        Angle of Intersection of Two Curves

        Derivatives as the Rate of Change

        If a variable quantity y is some function of time t i.e., y = f(t), then small change in Δt time At have a corresponding change Δy in y.
        Thus, the average rate of change = (Δy/Δt)
        When limit At Δt→ 0 is applied, the rate of change becomes instantaneous and we get the rate of change with respect to at the instant x.

        Derivatives as the Rate of Change

        So, the differential coefficient of y with respect to x i.e., (dy/dx) is nothing but the rate of increase of y relative to x.

        Rolle’s Theorem

        Let f be a real-valued function defined in the closed interval [a, b], such that

        1. f is continuous in the closed interval [a, b].
        2. f(x) is differentiable in the open interval (a, b).
        3. f(a)= f(b)

        Then, there is some point c in the open interval (a, b), such that f‘ (c) = 0.

        Geometrically Under the assumptions of Rolle‘s theorem, the graph of f(x) starts at point (a, 0) and ends at point (b, 0) as shown in figures.

        Rolle’s Theorem

        The conclusion is that there is at least one point c between a and b, such that the tangent to the graph at (c, f(c)) is parallel to the x-axis.

        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

        Let f be a real function, continuous on the closed interval [a, b] and differentiable in the open interval (a, b). Then, there is at least one point c in the open interval (a, b), such that

        Lagrange’s Mean Value Theorem

        Geometrically Any chord of the curve y = f(x), there is a point on the graph, where the tangent is parallel to this chord.
        Remarks In the particular case, where f(a) = f(b).
        The expression [f(b) – f(a)/(b – a)] becomes zero. Thus, when
        f(a) = f (b), f ‗ (c) = 0 for some c in (a, b).
        Thus, Rolle‘s theorem becomes a particular case of the mean value theorem.

        Approximations and Errors

        1. Let y = f(x) be a given function. Let Ax denotes a small increment in Δx, corresponding which y increases by Δy. Then, for small increments, we assume that

        Approximations and Errors

        2. Let Δx be the error in the measurement of independent variable x and Δy is corresponding error in the measurement of dependent variable y. Then,

        Approximations and Errors

        • Δy = Absolute error in measurement of y
        • (Δy/y) = Relative error in measurement of y
        • (Δy/y) * 100 = Percentage error in measurement of y

         

        Monotonicity of Functions

        1. 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).

        2. Strictly Increasing Function

        f(x) is said to be increasing in D1, if for every x1, x2 ∈ D1, x1 > x2 ⇒ f(x1) > f(x2). It means that there is a certain increase in the value of f(x) with an increase in the value of x.

        3. Classification of Strictly Increasing Function
        Classification of Strictly Increasing Function
        4. Non-Decreasing Function

        f(x) is said to be non-decreasing in D1, if for every x1, x2 ∈ D1, x1 > x2 ⇒ f(x1) ≥ f(x2). It means that the value of f(x) would new decrease with an increase in the value of x.

        5. Strictly Decreasing Function

        f(x) is said to be decreasing in D1, if for every x1, x2 ∈ D1, x1 > x2 ⇒ f(x1) < f(x2). It means that there is a certain decrease in the value c f(x) with an increase in the value of x.

        Classification of Strictly Decreasing Function

        Classification of Strictly Decreasing Function

        6. Non-increasing Function

        f(x) is said to be non-increasing in D1, if for every x1, x2 ∈ D1, x1 > x2 ⇒ f(x1) ≤ f(x2). It means that the value of f(x) would never increase with an increase in the value of x.
        If a function is either strictly increasing or strictly decreasing, then it is also a monotonic function.

         

        Important Points to be Remembered

        (i) A function f (x) is said to be increasing (decreasing) at point x0, if there is an interval (x0 — h, x0 + h) containing x0, such that f(x) is increasing (decreasing) on (x0 — h, x0 + h).
        (ii) 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.
        (iii) If (x) is increasing function on (a , b), then tangent at every point on the curve y = f(x) makes an acute angle θ with the positive direction of x-axis.

        Important Points to be Remembered

        (iv) Let f be a differentiable real function defined on an open interval (a, b).
        • If f ‗ (x) > 0 for all x ∈ (a, b), then f (x) is increasing on (a, b).
        • If f ‗ (x) < 0 for all x ∈ (a , b), then f (x) is decreasing on (a, b).

        (v) Let f be a function defined on (a, 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 increasing on (a, 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).

        Properties of Monotonic Functions

        1. If f(x)is strictly increasing function on an interval [a, b], then f-1 exist and also a strictly increasing function.
        2. If f(x) is strictly increasing function on [a, b], such that it is continuous, then f-1 is continuous on [f(a), f(b)].
        3. If f(x) and g(x) are strictly increasing (or decreasing) function on [a, b], then gof(x) is strictly increasing (or decreasing) function on [a, b].
        4. If one of the two functions f(x) and g(x) is strictly increasing and other a strictly decreasing, then gof(x) is strictly decreasing on [a, b].
        5. If f(x) is continuous on [a, b], such that f‘ (c) ≥ 0 (f ‗ (c) > 0) for each c ∈ (a, b) is strictly increasing function on [a, b].
        6. If f(x) is continuous on [a, b] such that f ‗(c) ≤ (f ‗ (c) < 0) for each c ∈ (a, b), then f(x) is strictly decreasing function on [a, b].

        Maxima and Minima of Functions

        1. A function y = f(x) is said to have a local maximum at a point x = a. If f(x) ≤ f(a) for all x ∈ (a – h, a + h), where h is somewhat small but positive quantity.

        Maxima and Minima of Functions

        The point x = a is called a point of maximum of the function f(x) and f(a) is known as the maximum value or the greatest value or the absolute maximum value of f(x).

        2. The function y = f(x) is said to have a local minimum at a point x = a, if f(x) ≥ f(a) for all x ∈ (a – h, a + h), where h is somewhat small but positive quantity.

        Maxima and Minima of Functions

        The point x = a is called a point of minimum of the function f(x) and f(a) is known as the minimum value or the least value or the absolute minimum value of f(x).

        Properties of Maxima and Minima

        1. If f(x) is continuous function in its domain, then at least one maxima and one minima must lie between two equal values of x.
        2. Maxima and minima occur alternately, i.e., between two maxima there is one minima and vice-versa.
        3. If f(x) → ∞ as x → a or b and f ‗ (x) = 0 only for one value of x (sayc) between a and b, then f(c) is necessarily the minimum and the least value.
        4. If f(x) → p -∞ as x → a or b and f(c) is necessarily the maximum and the greatest value.

        Important Points to be Remembered

        1. If f(x) be a differentiable functions, then f ‗(x) vanishes at every local maximum and at every local minimum.
        2. The converse of above is not true, i.e., every point at which f‘ (x) vanishes need not be a local maximum or minimum. e.g., if f(x) = x3 then f ‗(0) = 0, but at x =0. The function has neither minimum nor maximum. In general these points are point of inflection.
        3. A function may attain an extreme value at a point without being derivable there at, e.g., f(x) = |x| has a minima at x = 0 but f'(0) does not exist.
        4. A function f(x) can has several local maximum and local minimum values in an interval. Thus, the maximum and minimum values of f(x) defined above are not necessarily the greatest and the least values of f(x) in a given interval.
        5. A minimum value at some point may even be greater than a maximum values at some other point.

         

        Maximum and Minimum Values in a Closed Interval

        Let y = f(x) be a function defined on [a, b]. By a local maximum (or local minimum) value of a function at a point c ∈ [a, b] we mean the greatest (or the least) value in the immediate neighbourhood of x = c. It does not mean the greatest or absolute maximum (or the least or absolute minimum) of f(x) in the interval [a, b].
        A function may have a number of local maxima or local minima in a given interval and even a local minimum may be greater than a relative maximum.

        Local Maximum

        A function f(x) is said to attain a local maximum at x = a, if there exists a neighbourhood (a – δ, a + δ), of c such that, f(x) < f(a), ∀ x ∈ (a – δ, α + δ), x ≠ a or f(x) – f(a)< 0, ∀ x ∈ (a – δ, α + δ), 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), ∀ x ∈ (a – δ, α + δ), x ≠ a or f(x) – f(a) > 0, ∀ x ∈ (a – δ, α + δ), x ≠ a
        In such a case f(a) is called the local minimum value of f(x) at x = a.

        Methods to Find Local Extremum
        1. First Derivative Test

        Let f(x) be a differentiable function on an interval I and a ∈ I. Then,

        1. (i) Point a is a local maximum of f(x), if
        (a) f ‗(a) = 0
        (b) f ‗(x) > 0, if x ∈ (a – h, a) and f‘ (x) < 0, if x ∈ (a, a + h), where h is a small but positive quantity.

        2. (ii) Point a is a local minimum of f(x), if
        (a) f ‗(a) = 0
        (b) f ‗(a) < 0, if x ∈ (a – h, a) and f ‗(x) > 0, if x ∈ (a, a + h), where h is a small but positive quantity.

        3. (iii) If f ‗(a) = 0 but f ‗(x) does not changes sign in (a – h, a + h), for any positive quantity h, then x = a is neither a point of minimum nor a point of maximum.

        2. Second Derivative Test

        Let f(x) be a differentiable function on an interval I. Let a ∈ I is such that f ―(x) is continuous at x = a. Then,

        1. x = a is a point of local maximum, if f ‗(a) = 0 and f ―(a) < 0.
        2. x = a is a point of local minimum, if f ‗(a) = 0 and f‖(a) > 0.
        3. If f ‗(a) = f ―(a) = 0, but f‖ (a) ≠ 0, if exists, then x = a is neither a point of local maximum nor a point of local minimum and is called point of inflection.
        4. If f ‗(a) = f ―(a) = f ‗‖(a) = 0 and f iv(a) < 0, then it is a local maximum. And if f iv > 0, then it is a local minimum.

        nth Derivative Test

        Let f be a differentiable function on an interval / and let a be an interior point of / such that

        (i) f ‗(a) = f ―(a) = f ‗‖(a) = … f n – 1(a) = 0 and
        (ii) fn (a) exists and is non-zero, then
        • If n is even and f n (a) < 0 ⇒ x = a is a point of local maximum.
        • If n is even and f n (a) > 0 ⇒ x = a is a point of local minimum.
        • If n is odd ⇒ x = a is a point of local maximum nor a point of local minimum.

        Important Points to be Remembered

        1. To Find Range of a Continuous Function Let f(x) be a continuous function on [a, b], such that its least value in [a, b1 is m and the greatest value in [a, b] is M. Then, range of value of f(x) for x ∈ [a, b] is [m, M].
        2. To Check for the injectivity of a Function A strictly monotonic function is always oneone (injective). Hence, a function f (x) is one-one in the interval [a, b], if f ‗(x) > 0 , ∀ x ∈ [a, b] or f‘ (x) < 0 , ∀ x ∈ [a, b].
        3. The points at which a function attains either the local maximum value or local minimum values are known as the extreme points or turning points and both local maximum and local minimum values are called the extreme values of f(x). Thus, a function attains an extreme value at x = a, if f(a) is either a local maximum value or a local minimum value. Consequently at an extreme point ‗a‘, f (x) — f (a) keeps the same sign for all values of x in a deleted nbd of a.
        4. A necessary condition for (a) to be an extreme value of a function (x) is that f ‗(a) = 0 in case it exists.
        5. This condition is only a necessary condition for the point x = a to be an extreme point. It is not sufficient. i.e., f ‗(a) = 0 does not necessarily imply that x = a is an extreme point.
        There are functions for which the derivatives vanish at a point but do not have an extreme value. e.g., the function f(x) = x3 , f ‗(0) = 0 but at x = 0 the function does not attain an extreme value.
        6. Geometrically the above condition means that the tangent to the curve y = f(x) at a point where the ordinate is maximum or minimum is parallel to the x-axis.
        7. All x,for which f ‗(x) = 0, do not give us the extreme values. The values of x for which f ‗(x) = 0 are called stationary values or critical values of x and the corresponding values of f(x) are called stationary or turning values of f(x).

        Critical Points of a Function

        Points where a function f(x) is not differentiable and points where its derivative (differentiable coefficient) is z ?,ro are called the critical points of the function f(x).

        Maximum and minimum values of a function f(x) can occur only at critical points. However, this does not mean that the function will have maximum or minimum values at all critical points. Thus, the points where maximum or minimum value occurs are necessarily critical Points but a function may or may not have maximum or minimum value at a critical point.

        Point of Inflection

        Consider function f(x) = x3. At x = 0, f ‗(x)= 0. Also, f ―(x) = 0 at x = 0. Such point is called point of inflection, where 2nd derivative is zero. Consider another function f(x) = sin x, f ―(x)= – sin x. Now, f ―(x)= 0 when x = nπ, then this points are called point of inflection.

        At point of inflection

        1. It is not necessary that 1st derivative is zero.
        2. 2nd derivative must be zero or 2nd derivative changes sign in the neighbourhood of point of inflection.

        Concept of Global Maximum/Minimum

        • Let y = f(x) be a given function with domain D.
        • Let [a, b] ⊆ D, then global maximum/minimum of f(x) in [a, b] is basically the greatest/least value of f(x) in [a, b].
        • Global maxima/minima in [a, b] would always occur at critical points of f(x) with in [a, b] or at end points of the interval.

         

        Global Maximum/Minimum in [a, b]

        In order to find the global maximum and minimum of f(x) in [a, b], find out all critical points of f(x) in [a, b] (i.e., all points at which f ‗(x)= 0) and let f(c1), f(c2) ,…, f(n) be the values of the function at these points.

        Then, M1 → Global maxima or greatest value. and M1 → Global minima or least value.
        where M1 = max { f(a), f(c1), f(c1) ,…, f(cn), f(b)} and M1 = min { f(a), f(c1), f(c2) ,…, f(cn), f(b)}

        Then, M1 is the greatest value or global maxima in [a, b] and M1 is the least value or global minima in [a, b].

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