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Lesson-2:

Fundamental ofDifferentiation

Edited:

Surjatin Wiriadidjaja

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1.IntroductionObjectives:

understand the basics of differentiation, relate the slopes of the secant line and tangent line to the derivative of a function,

find derivatives of polynomial, trigonometric and transcendental functions,

use rules of differentiation to differentiate functions,

find maxima and minima of a function, and

apply concepts of differentiation to real world problems.

The concepts of differentiation to be described are applicable in learning about numerical

methods.

These include the concepts of the secant line, the slope of a tangent line as a background to

solving nonlinear equations using the Newton-Raphson method, finding maxima and minima offunctions as a means of optimization, the use of the Taylor series to approximate functions, etc.

The derivative of a function represents the rate of change of a variable with respect to another

variable. For example, the velocity of a body is defined as the rate of change of the location of

the body with respect to time. The location is the dependent variable while time is the

independent variable. Now if we measure the rate of change of velocity with respect to time,

we get the acceleration of the body. In this case, the velocity is the dependent variable while

time is the independent variable.

Whenever differentiation is introduced to a student, two concepts of the secant line and

tangent line (Figure 1) are revisited.

Let P and Q be two points on the curve as shown in Figure 1. The secant line is the straight line

drawn through P and Q .

Q

P

f(x)

x

secant line

tangent line

Figure 1 Function curve with tangent and secant lines.

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The slope of the secant line (Figure 2) is then given as

aha

afhafmPQ

)(

)()(secant,

h

afhaf )()(

As Q moves closer and closer to P, the limiting portion is called the tangent line. The slope of the

tangent line tangent,PQm then is the limiting value of secant,PQm as 0h .

h

afhafm

hPQ

)()(lim

0tangent,

Derivative of a FunctionRecall from calculus, the derivative of a function )(xf at ax is defined as

h

afhafaf

h

)()(lim)(

0

)(xf

P

Q

))(,( afa

a

))(,( xfx

ax

)()( afxf

x

Figure 5 Graph showing the second definition of the

x

P

Q

a a+h

)(xf

Figure 2 Calculation of the secant line.

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Second Definition of DerivativesThere is another form of the definition of the derivative of a function. The derivative of the function

)(xf at ax is defined as

ax

afxfaf

ax

)()(lim)(

As ax , the definition is nothing but the slope of the tangent line at P.

Finding equations of a tangent lineOne of the numerical methods used to solve a nonlinear equation is called the Newton-Raphson

method. This method is based on the knowledge of finding the tangent line to a curve at a point. Let us

look at an example to illustrate finding the equation of the tangent line to a curve.

Other Notations of DerivativesDerivates can be denoted in several ways. For the first derivative, the notations are

dx

dyandyxf

dx

dxf ,),(),(

For the second derivative, the notations are

2

2

2

2

,),(),(dx

ydandyxf

dx

dxf

For the thn derivative, the notations are

n

nn

n

nn

dx

ydyxf

dx

dxf ,),(),(

)()(

Theorems of DifferentiationSeveral theorems of differentiation are given to show how one can find the derivative of different

functions.

Theorem 1The derivative of a constant is zero. If kxf )( , where k is a constant, 0)( xf .

Theorem 2

The derivative ofnxxf )( , where 0n is

1)(

nnxxf .

Theorem 3

The derivative of )()( xkgxf , where k is a constant is )()( xgkxf .

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Theorem 4

The derivative of )()()( xvxuxf is )()()( xvxuxf .

Theorem 5The derivative of

)()()( xvxuxf

is

)()()()()( xudx

dxvxv

dx

dxuxf . (Product Rule)

Theorem 6The derivative of

)(

)()(

xv

xuxf

is

2))((

)()()()(

)(xv

xvdx

dxuxu

dx

dxv

xf

(Quotient Rule)

Table of Derivatives

,

)(xf )(xf

0, nxn 1nnx

nkx 0n 1nknx

)(xsin )(xcos

)(xcos )(xsin

)(xtan )(2 xsec

)(xsinh )(xcosh

)(xcosh )(xsinh

)(xtanh )(1 2 xtanh

)(1

xsin

21

1

x

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1

||

22

xx

x

1

||

22xx

x

Chain Rule of DifferentiationSometimes functions that need to be differentiated do not fall in the form of simple functions or the

forms described previously. Such functions can be differentiated using the chain rule if they are of the

form ))(( xgf . The chain rule states

)())(())((( xgxgfxgfdx

d

For example, to find )(xf of42 )23()( xxxf , one could use the chain rule.

)23()(2

xxxg

26)( xxg

3))((4))(( xgxgf

)(1 xcos 2

1

1

x

)(1 xtan

21

1

x

)(xcsc )()( xcotxcsc

)(xsec )()( xtanxsec

)(xcot )(2 xcsc

)(xcsch )()( xcschxcoth

)(xsech )()( xsechxtanh

)(xcoth )(1 2 xcoth

)(

1

xcsc

)(1xsec

)(1 xcot

21

1

x

xa

xaaln )(

)(xlnx

1

)(xloga)(

1axln

xe

xe

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)26()23(4))23(( 3242 xxxxxdx

d

Implicit DifferentiationSometimes, the function to be differentiated is not given explicitly as an expression of the

independent variable. In such cases, how do we find the derivatives? We will discuss this via examples.

Higher order derivativesSo far, we have limited our discussion to calculating first derivative, )(xf of a function )(xf . What if

we are asked to calculate higher order derivatives of )(xf .

A simple example of this is finding acceleration of a body from a function that gives the location of the

body as a function of time. The derivative of the location with respect to time is the velocity of the

body, followed by the derivative of velocity with respect to time being the acceleration. Hence, the

second derivative of the location function gives the acceleration function of the body.

Finding maximum and minimum of a functionThe knowledge of first derivative and second derivative of a function is used to find the

minimum and maximum of a function. First, let us define what the maximum and minimum of a

function are. Let )(xf be a function in domain D , then

(A) )(af is the maximum of the function if )()( xfaf for all values of x in the domain D .(B) )(af is the minimum of the function if )()( xfaf for all values of x in the domain D .

The minimum and maximum of a function are also the critical values of a function. An extreme value

can occur in the interval

at end points .

a point in where .

a point in where does not exist.

These critical points can be the local maximas and minimas of the function (See Figure 8).

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f(x)

x

Figure 8The plot shows critical points of )(xf in ],[ dc .

Absolute Minimum

Local Minimum

Local

Minimum

Local Maximum

(f (x) does not exist)

Absolute Maximum

Local Maximum

c d

maximum

minimum

x

Figure 7 Graph illustrating the concepts of maximum and minimum.

Domain = [c,d]

c d

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Figure 11 shows the maximum of the function occurring at a singular point. The function )(xf has a

sharp corner at ax .

)(xf

x

)(af

ax

Figure 11 Graph demonstrates the concept of a singular point with

discontinuous slope at ax

x

xxf /1)(

)(xf

Figure 10 Function that has no maximum or minimum.

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2.Differentiation of

Continuous FunctionsAfter reading this chapter, you should be able to:

derive formulas for approximating the first derivative of a function,

derive formulas for approximating derivatives from Taylor series,

derive finite difference approximations for higher order derivatives, and

use the developed formulas in examples to find derivatives of a function.

The derivative of a function at x is defined as

To be able to find a derivative numerically, one could make x finite to give,

Knowing the value of x at which you want to find the derivative of xf , we choose a value of x to

find the value of xf . To estimate the value of xf , three such approximations are suggested as

follows.

Forward Difference Approximation of the First Derivative

From differential calculus, we know

For a finite x ,

The above is the forward divided difference approximation of the first derivative. It is called forward

because you are taking a point ahead of x . To find the value of xf at ixx , we may choose

another point x ahead as 1 ixx . This gives

where

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Figure 1 Graphical representation of forward difference approximation of first derivative.

Backward Difference Approximation of the First DerivativeWe know

x

xfxxfxf

x

0lim

For a finite x ,

x

xfxxfxf

If x is chosen as a negative number,

x

xfxxfxf

xxxfxf

This is a backward difference approximation as you are taking a point backward from x . To find the

value of xf at ixx , we may choose another point x behind as 1 ixx . This gives

where 1 ii xxx

)(xf

xx x x

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Figure 2 Graphical representation of backward difference approximation of first derivative.

Forward Difference Approximation from Taylor Series

Taylors theorem says that if you know the value of a function )(xf at a point ix and all its derivatives

at that point, provided the derivatives are continuous between ix and 1ix , then

2

111!2

iii

iiiii xxxf

xxxfxfxf

Substituting for convenience ii xxx 1

2

1 !2

xxf

xxfxfxf iiii

x

xf

x

xfxfxf iiii

!2

1

xO

x

xfxfxf iii

1

The xO term shows that the error in the approximation is of the order of x .

Can you now derive from the Taylor series the formula for the backward divided difference

approximation of the first derivative?

As you can see, both forward and backward divided difference approximations of the first

derivative are accurate on the order of xO . Can we get better approximations? Yes, another

method to approximate the first derivative is called the central difference approximation of the

first derivative.

From the Taylor series

)(xf

xxx x

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32

1 !3

!2

xxf

xxf

xxfxfxf iiiii (1)

and

32

1

!3

!2

x

xf

x

xf

xxfxfxf

ii

iii(2)

Subtracting Equation (2) from Equation (1)

3

11 !3

22 x

xfxxfxfxf iiii

211

!32

xxf

x

xfxfxf iii

i

211

2xO

x

xfxf ii

hence showing that we have obtained a more accurate formula as the error is of the order of 2xO .

Figure 3 Graphical representation of central difference approximation of first derivative.

Finite Difference Approximation of Higher DerivativesOne can also use the Taylor series to approximate a higher order derivative. For example, to

approximate xf , the Taylor series is

)(xf

xx xxx x

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32

2 2!3

2!2

2 xxf

xxf

xxfxfxf iiiii (3)

where

xxxii 22

321

!3!2x

xfx

xfxxfxfxf ii

iii

(4)

where

xxx ii 1

Subtracting 2 times Equation (4) from Equation (3) gives

32

12 2 xxfxxfxfxfxf iiiii

xxf

x

xfxfxfxf i

iiii

22

12

xO

x

xfxfxfxf iiii

2

12 2 (5)

The formula given by Equation (5) is a forward difference approximation of the second derivative and

has an error of the order of xO . Can we get a formula that has a better accuracy? Yes, we can

derive the central difference approximation of the second derivative.

The Taylor series is

...!4!3!2

432

1

xxf

xxf

xxf

xxfxfxf iiiiii (6)

where

xxx ii 1

432

1!4!3!2

xxf

xxf

xxf

xxfxfxf iiiiii

(7)

where

xxx ii 1

Adding Equations (6) and (7), gives

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...12

2

42

11

xxfxxfxfxfxf iiiii

...

12

22

2

11

xxf

x

xfxfxfxf iiiii

2

2

11 2 xOx

xfxfxf iii

3.Differentiation ofDiscrete Functions

After reading this chapter, you should be able to:

find approximate values of the first derivative of functions that are given at discrete data points, and

use Lagrange polynomial interpolation to find derivatives of discrete functions.

To find the derivatives of functions that are given at discrete points, several methods are available.

Although these methods are mainly used when the data is spaced unequally, they can be used for data

that is spaced equally as well.

Forward Difference Approximation of the First DerivativeWe know

x

xfxxfxf

x

0lim

For a finite x ,

x

xfxxf

xf

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Figure 1 Graphical representation of forward difference approximation of first derivative.

So given 1n data points nn yxyxyxyx ,,,,,,,, 221100 , the value of )(xf for 1 ii xxx ,1,...,0 ni , is given by

ii

iii

xx

xfxfxf

1

1

Direct Fit Polynomials

In this method, given 1n data points nn yxyxyxyx ,,,,,,,, 221100 , one can fit a thn orderpolynomial given by

nnn

nn xaxaxaaxP

1

110

To find the first derivative,

12121 12)(

n

n

n

n

n

n xnaxanxaadx

xdPxP

Similarly, other derivatives can also be found.

Lagrange Polynomial

In this method, given nn yxyx ,,,, 00 , one can fit a thn order Lagrangian polynomial given by

n

i

iin xfxLxf0

)()()(

)(xf

xx x x

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where n in )(xfn stands for theth

n order polynomial that approximates the function )(xfy and

n

ijj ji

j

ixx

xxxL

0

)(

)(xLi is a weighting function that includes a product of 1n terms with terms of ij omitted.

Then to find the first derivative, one can differentiate xfn once, and so on for other derivatives.

For example, the second order Lagrange polynomial passing through 221100 ,and,,,, yxyxyx is

21202

101

2101

200

2010

212 xf

xxxx

xxxxxf

xxxx

xxxxxf

xxxx

xxxxxf

Differentiating the above equation gives

2

1202

101

2101

200

2010

212

222xf

xxxx

xxxxf

xxxx

xxxxf

xxxx

xxxxf

Differentiating again would give the second derivative as

21202

1

2101

0

2010

2

222xf

xxxxxf

xxxxxf

xxxxxf