SlideShare una empresa de Scribd logo
1 de 6
Descargar para leer sin conexión
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

Linear Approximations and Differentials
PROBLEM: Approximate the number

√
4

1.1.

IDEA: We have seen that a curve lies very close to its tangent
line near the point of tangency. This observation is the basis for a
method of finding approximate values of functions.
The idea is that it might be easy to calculate a value f (a) of a func√
tion (in our case 4 1), but difficult (or even impossible) to compute
√
nearby values of f (in our case 4 1.1). So we settle for the easily
computed values of the linear function L whose graph is the tangent
line of f at (a, f (a)).
In other words, we use the tangent line at (a, f (a)) as an approximation to the curve y = f (x)
when x is near a.
Solution: The point-slope equation of the tangent line is
y = f (a) + f ′ (a)(x − a)
therefore

f (x) ≈ f (a) + f ′ (a)(x − a) if x is close to a
√
In particular, if f (x) = 4 x, then
√
4

x≈

√
4
a+

1
(x − a)
4a3/4

1
. Plugging in x = 1.1 and a = 1, we get
4a3/4
√
√
1
1
4
4
1.1 ≈ 1 +
(1.1 − 1) = 1 + (0.1) = 1.025 (the true value is 1.024113689...)
3/4
4·1
4

since f ′ (a) =

The approximation
f (x) ≈ f (a) + f ′ (a)(x − a)

(1)

is called the linear approximation or tangent line approximation of f at a. The linear
function whose graph is this tangent line, that is,
L(x) = f (a) + f ′ (a)(x − a)
is called the linearization of f at a.

1

(2)
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

√
EXAMPLE: Find the linearization of the function f (x) = x + 3 at a = 1 and use it to
√
√
approximate the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates?
√
Solution: The derivative of f (x) = x + 3 is
(
)′ 1
1
1
f ′ (x) = (x + 3)1/2 = (x + 3)1/2−1 = (x + 3)−1/2 = √
2
2
2 x+3
and so we have f (1) = 2 and f ′ (1) = 1 . Putting these values into (2), we see that the lineariza4
tion is
1
7 x
L(x) = f (1) + f ′ (1)(x − 1) = 2 + (x − 1) = +
4
4 4
The corresponding linear approximation (1) is
√
7 x
x+3≈ +
4 4

(when x is near 1)

In particular, we have
√
√
7 0.98
7 1.05
3.98 ≈ +
= 1.995 and
4.05 ≈ +
= 2.0125
4
4
4
4

(3)

The linear approximation is illustrated in the figure below.

We see that, indeed, the tangent line approximation is a good approximation to the given
function when x is near 1. We also see that our approximations are overestimates because the
tangent line lies above the curve.
√
√
Of course, a calculator could give us approximations for 3.98 and 4.05, but the linear
approximation gives an approximation over an entire interval.
In the table above we compare estimates from the obtained linear approximation with the
true values. Notice from this table, and also from the figure above, that the tangent line
approximation gives good estimates when x is close to 1 but the accuracy of the approximation
deteriorates when x is farther away from 1.
√
EXAMPLE: Find the linearization of the function f (x) = x at a = 4 and use it to approximate
√
√
the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates?

2
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

√
EXAMPLE: Find the linearization of the function f (x) = x at a = 4 and use it to approximate
√
√
the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates?
√
Solution: The derivative of f (x) = x is
(
)′ 1
1
1
f ′ (x) = x1/2 = x1/2−1 = x−1/2 = √
2
2
2 x
and so we have f (4) = 2 and f ′ (4) = 1 . Putting these values into (2), we see that the lineariza4
tion is
1
x
L(x) = f (4) + f ′ (4)(x − 4) = 2 + (x − 4) = 1 +
4
4
The corresponding linear approximation (1) is
√
x
x≈1+
4

(when x is near 4)

In particular, we have
√
√
3.98
4.05
3.98 ≈ 1 +
= 1.995 and
4.05 ≈ 1 +
= 2.0125
4
4
Note that we got the same results as in (3) since
1+

3.98
3 + 0.98
3 0.98
7 0.98
=1+
=1+ +
= +
4
4
4
4
4
4

1+

4.05
3 + 1.05
3 1.05
7 1.05
=1+
=1+ +
= +
4
4
4
4
4
4

and

or, in general,

x
3+x−3
3 x−3
7 x−3
=1+
=1+ +
= +
4
4
4
4
4
4
Our approximations are overestimates because the tangent line lies above the curve.
1+

√
EXAMPLE: Find the linearization of the function f (x) = 3 1 + x at a = 0 and use it to
√
√
approximate the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates?

3
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

√
EXAMPLE: Find the linearization of the function f (x) = 3 1 + x at a = 0 and use it to
√
√
approximate the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates?
√
Solution: The derivative of f (x) = 3 1 + x is
(
)′ 1
1
1
f ′ (x) = (1 + x)1/3 = (1 + x)1/3−1 = (1 + x)−2/3 = √
3
3
3 3 (1 + x)2
and so we have f (0) = 1 and f ′ (0) = 1 . Putting these values into (2), we see that the lineariza3
tion is
x
L(x) = f (0) + f ′ (0)(x − 0) = 1 +
3
The corresponding linear approximation (1) is
√
x
3
1+x≈1+
(when x is near 0)
3
In particular, we have
√
−0.05
3
0.95 ≈ 1 +
= 0.9833... (the true value is 0.9830475725...)
3
and
√
0.1
3
1.1 ≈ 1 +
= 1.0333... (the true value is 1.032280115...)
3
Our approximations are overestimates because the tangent line lies above the curve.
√
EXAMPLE: Find the linearization of the function f (x) = 3 x at a = 1 and use it to approximate
√
√
the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates?
√
Solution: The derivative of f (x) = 3 x is
(
)′ 1
1
1
f ′ (x) = x1/3 = x1/3−1 = x−2/3 = √
3
3
3
3 x2
and so we have f (1) = 1 and f ′ (1) = 1 . Putting these values into (2), we see that the lineariza3
tion is
x−1
2 x
L(x) = f (1) + f ′ (1)(x − 1) = 1 +
= +
3
3 3
The corresponding linear approximation (1) is
√
2 x
3
x≈ +
(when x is near 1)
3 3
In particular, we have
√
2 0.95
3
= 0.9833... (the true value is 0.9830475725...)
0.95 ≈ +
3
3
and
√
2 1.1
3
= 1.0333... (the true value is 1.032280115...)
1.1 ≈ +
3
3
EXAMPLE: Find the linearization of the function f (x) = sin x at a = 0 and use it to approximate the numbers sin(−0.1) and sin(0.1). Are these approximations overestimates or underestimates?
4
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

EXAMPLE: Find the linearization of the function f (x) = sin x at a = 0 and use it to approximate the numbers sin(−0.1) and sin(0.1). Are these approximations overestimates or underestimates?
Solution: The derivative of f (x) = sin x is
f ′ (x) = (sin x)′ = cos x
and so we have f (0) = 0 and f ′ (0) = 1. Putting these values into (2), we see that the linearization is
L(x) = f (0) + f ′ (0)(x − 0) = 0 + 1 · (x − 0) = x
The corresponding linear approximation (1) is
sin x ≈ x (when x is near 0)

In particular, we have
sin(−0.1) ≈ −0.1 (the true value is -0.09983341665...)
and
sin(0.1) ≈ 0.1 (the true value is 0.09983341665...)
The first approximation is an underestimate because the tangent line lies below the curve when
x is near 0 from the left. The second approximation is an overestimate because the tangent
line lies above the curve when x is near 0 from the right.
EXAMPLE: For what values of x is the linear approximation sin x ≈ x accurate to within 0.1?
Solution: Accuracy to within 0.1 means that the functions should differ by less than 0.1:
| sin x − x| < 0.1

⇐⇒

−0.1 < sin x − x < 0.1

Using a graphing calculator we can conclude that the approximation sin x ≈ x is accurate to
within 0.1 when −0.86 < x < 0.86.

5
Section 2.8 Linear Approximations and Differentials

2010 Kiryl Tsishchanka

Differentials
The ideas behind linear approximations are sometimes formulated in the terminology and notation of differentials. If y = f (x), where f is a differentiable function, then the differential
dx is an independent variable; that is, dx can be given the value of any real number. The
differential dy is then defined in terms of dx by the equation
dy = f ′ (x)dx
So dy is a dependent variable; it depends on the values of x and dx. If dx is given a specific
value and x is taken to be some specific number in the domain of f, then the numerical value
of dy is determined.
Let P (x, f (x)) and Q(x + ∆x, f (x + ∆x)) be points on the graph of f and let dx = ∆x. The
corresponding change in y is
∆y = f (x + ∆x) − f (x)
The slope of the tangent line P R is the derivative f ′ (x). Thus the directed distance from S to
R is f ′ (x)dx = dy. Therefore, dy represents the amount that the tangent line rises or falls (the
change in the linearization), whereas ∆y represents the amount that the curve y = f (x) rises
or falls when x changes by an amount dx. Notice that the approximation ∆y ≈ dy becomes
better as ∆x becomes smaller.

If we let dx = x − a, then x = a + dx and we can rewrite the linear approximation (1)
f (x) ≈ f (a) + f ′ (a)(x − a)
in the notation of differentials:
f (a + dx) ≈ f (a) + dy
√
For instance, for the function f (x) = x + 3 in Example 1, we have
dx
dy = f ′ (x)dx = √
2 x+3
If a = 1 and dx = ∆x = 0.05, then
0.05
= 0.0125
dy = √
2 1+3
and

√
4.05 = f (1.05) ≈ f (1) + dy = 2.0125

just as we found in Example 1.
6

Más contenido relacionado

La actualidad más candente

Interpolation In Numerical Methods.
 Interpolation In Numerical Methods. Interpolation In Numerical Methods.
Interpolation In Numerical Methods.Abu Kaisar
 
Maxima & Minima of Calculus
Maxima & Minima of CalculusMaxima & Minima of Calculus
Maxima & Minima of CalculusArpit Modh
 
CHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONCHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONNikhil Pandit
 
Lesson 11: Limits and Continuity
Lesson 11: Limits and ContinuityLesson 11: Limits and Continuity
Lesson 11: Limits and ContinuityMatthew Leingang
 
Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Matthew Leingang
 
Newton's forward difference
Newton's forward differenceNewton's forward difference
Newton's forward differenceRaj Parekh
 
Limits and continuity[1]
Limits and continuity[1]Limits and continuity[1]
Limits and continuity[1]indu thakur
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minimarouwejan
 
5.4 more areas
5.4 more areas5.4 more areas
5.4 more areasmath265
 
Interpolation with unequal interval
Interpolation with unequal intervalInterpolation with unequal interval
Interpolation with unequal intervalDr. Nirav Vyas
 
Interpolation with Finite differences
Interpolation with Finite differencesInterpolation with Finite differences
Interpolation with Finite differencesDr. Nirav Vyas
 
Introduction to differentiation
Introduction to differentiationIntroduction to differentiation
Introduction to differentiationShaun Wilson
 
Lesson 2: Limits and Limit Laws
Lesson 2: Limits and Limit LawsLesson 2: Limits and Limit Laws
Lesson 2: Limits and Limit LawsMatthew Leingang
 
2.3 Operations that preserve convexity & 2.4 Generalized inequalities
2.3 Operations that preserve convexity & 2.4 Generalized inequalities2.3 Operations that preserve convexity & 2.4 Generalized inequalities
2.3 Operations that preserve convexity & 2.4 Generalized inequalitiesRyotaroTsukada
 
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...RyotaroTsukada
 
19 min max-saddle-points
19 min max-saddle-points19 min max-saddle-points
19 min max-saddle-pointsmath267
 

La actualidad más candente (20)

Interpolation In Numerical Methods.
 Interpolation In Numerical Methods. Interpolation In Numerical Methods.
Interpolation In Numerical Methods.
 
Imc2017 day1-solutions
Imc2017 day1-solutionsImc2017 day1-solutions
Imc2017 day1-solutions
 
Lesson 5: Continuity
Lesson 5: ContinuityLesson 5: Continuity
Lesson 5: Continuity
 
Limits and derivatives
Limits and derivativesLimits and derivatives
Limits and derivatives
 
Maxima & Minima of Calculus
Maxima & Minima of CalculusMaxima & Minima of Calculus
Maxima & Minima of Calculus
 
CHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTIONCHAIN RULE AND IMPLICIT FUNCTION
CHAIN RULE AND IMPLICIT FUNCTION
 
Lesson 11: Limits and Continuity
Lesson 11: Limits and ContinuityLesson 11: Limits and Continuity
Lesson 11: Limits and Continuity
 
Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)Lesson 10: The Chain Rule (slides)
Lesson 10: The Chain Rule (slides)
 
Newton's forward difference
Newton's forward differenceNewton's forward difference
Newton's forward difference
 
Limits and continuity[1]
Limits and continuity[1]Limits and continuity[1]
Limits and continuity[1]
 
Applications of maxima and minima
Applications of maxima and minimaApplications of maxima and minima
Applications of maxima and minima
 
Rules of derivative
Rules of derivativeRules of derivative
Rules of derivative
 
5.4 more areas
5.4 more areas5.4 more areas
5.4 more areas
 
Interpolation with unequal interval
Interpolation with unequal intervalInterpolation with unequal interval
Interpolation with unequal interval
 
Interpolation with Finite differences
Interpolation with Finite differencesInterpolation with Finite differences
Interpolation with Finite differences
 
Introduction to differentiation
Introduction to differentiationIntroduction to differentiation
Introduction to differentiation
 
Lesson 2: Limits and Limit Laws
Lesson 2: Limits and Limit LawsLesson 2: Limits and Limit Laws
Lesson 2: Limits and Limit Laws
 
2.3 Operations that preserve convexity & 2.4 Generalized inequalities
2.3 Operations that preserve convexity & 2.4 Generalized inequalities2.3 Operations that preserve convexity & 2.4 Generalized inequalities
2.3 Operations that preserve convexity & 2.4 Generalized inequalities
 
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...
5.4 Saddle-point interpretation, 5.5 Optimality conditions, 5.6 Perturbation ...
 
19 min max-saddle-points
19 min max-saddle-points19 min max-saddle-points
19 min max-saddle-points
 

Destacado

사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토dsefdtgfgrsdgrdfh
 
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토dsefdtgfgrsdgrdfh
 
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토dsefdtgfgrsdgrdfh
 
Oliviamath problem
Oliviamath problemOliviamath problem
Oliviamath problemjbianco9910
 
Maths activity
Maths activity Maths activity
Maths activity gilem488
 
Proving quads are parralelograms
Proving quads are parralelogramsProving quads are parralelograms
Proving quads are parralelogramsjbianco9910
 
Olivia’s math problem2
Olivia’s math problem2Olivia’s math problem2
Olivia’s math problem2jbianco9910
 
3002 a more with parrallel lines and anglesupdated 10 22-13
3002 a  more with parrallel lines and anglesupdated 10 22-133002 a  more with parrallel lines and anglesupdated 10 22-13
3002 a more with parrallel lines and anglesupdated 10 22-13jbianco9910
 
Math project
Math projectMath project
Math projectjnguyen20
 
2d 3d animation and Digital services from Vinformax and Creantt
2d  3d animation and Digital services from Vinformax and Creantt 2d  3d animation and Digital services from Vinformax and Creantt
2d 3d animation and Digital services from Vinformax and Creantt Prabhu Venkatesh Subramanian
 
Minkowski Sum on 2D geometry
Minkowski Sum on 2D geometryMinkowski Sum on 2D geometry
Minkowski Sum on 2D geometryClodéric Mars
 
Congruent figures 2013
Congruent figures 2013Congruent figures 2013
Congruent figures 2013jbianco9910
 
114333628 irisan-kerucut
114333628 irisan-kerucut114333628 irisan-kerucut
114333628 irisan-kerucuthafifa asiah
 
Deductivereasoning and bicond and algebraic proofs
Deductivereasoning and bicond and algebraic proofsDeductivereasoning and bicond and algebraic proofs
Deductivereasoning and bicond and algebraic proofsjbianco9910
 
Symmetry,rotation, reflection,translation
Symmetry,rotation, reflection,translationSymmetry,rotation, reflection,translation
Symmetry,rotation, reflection,translationEbin Santy
 
Graphing inverse functions
Graphing inverse functionsGraphing inverse functions
Graphing inverse functionsTarun Gehlot
 
Transformations of functions
Transformations of functionsTransformations of functions
Transformations of functionsTarun Gehlot
 
2002 more with transformations
2002 more with transformations2002 more with transformations
2002 more with transformationsjbianco9910
 

Destacado (20)

사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
 
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
 
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
사설토토 <&&>∃‰∩kid85⊇∬△ <&&>사설토토 사설토토
 
Oliviamath problem
Oliviamath problemOliviamath problem
Oliviamath problem
 
Maths activity
Maths activity Maths activity
Maths activity
 
Proving quads are parralelograms
Proving quads are parralelogramsProving quads are parralelograms
Proving quads are parralelograms
 
Olivia’s math problem2
Olivia’s math problem2Olivia’s math problem2
Olivia’s math problem2
 
3002 a more with parrallel lines and anglesupdated 10 22-13
3002 a  more with parrallel lines and anglesupdated 10 22-133002 a  more with parrallel lines and anglesupdated 10 22-13
3002 a more with parrallel lines and anglesupdated 10 22-13
 
Math project
Math projectMath project
Math project
 
2d 3d animation and Digital services from Vinformax and Creantt
2d  3d animation and Digital services from Vinformax and Creantt 2d  3d animation and Digital services from Vinformax and Creantt
2d 3d animation and Digital services from Vinformax and Creantt
 
Minkowski Sum on 2D geometry
Minkowski Sum on 2D geometryMinkowski Sum on 2D geometry
Minkowski Sum on 2D geometry
 
Congruent figures 2013
Congruent figures 2013Congruent figures 2013
Congruent figures 2013
 
114333628 irisan-kerucut
114333628 irisan-kerucut114333628 irisan-kerucut
114333628 irisan-kerucut
 
Power series
Power seriesPower series
Power series
 
Deductivereasoning and bicond and algebraic proofs
Deductivereasoning and bicond and algebraic proofsDeductivereasoning and bicond and algebraic proofs
Deductivereasoning and bicond and algebraic proofs
 
Symmetry,rotation, reflection,translation
Symmetry,rotation, reflection,translationSymmetry,rotation, reflection,translation
Symmetry,rotation, reflection,translation
 
Graphing inverse functions
Graphing inverse functionsGraphing inverse functions
Graphing inverse functions
 
Transformations of functions
Transformations of functionsTransformations of functions
Transformations of functions
 
2002 more with transformations
2002 more with transformations2002 more with transformations
2002 more with transformations
 
Chapter 5 day 2
Chapter 5 day 2Chapter 5 day 2
Chapter 5 day 2
 

Similar a Linear Approx and Diffs

Numarical values
Numarical valuesNumarical values
Numarical valuesAmanSaeed11
 
Numarical values highlighted
Numarical values highlightedNumarical values highlighted
Numarical values highlightedAmanSaeed11
 
Project in Calcu
Project in CalcuProject in Calcu
Project in Calcupatrickpaz
 
3.7 applications of tangent lines
3.7 applications of tangent lines3.7 applications of tangent lines
3.7 applications of tangent linesmath265
 
Basic Cal - Quarter 1 Week 1-2.pptx
Basic Cal - Quarter 1 Week 1-2.pptxBasic Cal - Quarter 1 Week 1-2.pptx
Basic Cal - Quarter 1 Week 1-2.pptxjamesvalenzuela6
 
limits and continuity
limits and continuitylimits and continuity
limits and continuityElias Dinsa
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONDhrupal Patel
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life OlooPundit
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Matthew Leingang
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Mel Anthony Pepito
 
The remainder theorem powerpoint
The remainder theorem powerpointThe remainder theorem powerpoint
The remainder theorem powerpointJuwileene Soriano
 
Limit, Continuity and Differentiability for JEE Main 2014
Limit, Continuity and Differentiability for JEE Main 2014Limit, Continuity and Differentiability for JEE Main 2014
Limit, Continuity and Differentiability for JEE Main 2014Ednexa
 
L19 increasing &amp; decreasing functions
L19 increasing &amp; decreasing functionsL19 increasing &amp; decreasing functions
L19 increasing &amp; decreasing functionsJames Tagara
 

Similar a Linear Approx and Diffs (20)

Numarical values
Numarical valuesNumarical values
Numarical values
 
Numarical values highlighted
Numarical values highlightedNumarical values highlighted
Numarical values highlighted
 
Project in Calcu
Project in CalcuProject in Calcu
Project in Calcu
 
3.7 applications of tangent lines
3.7 applications of tangent lines3.7 applications of tangent lines
3.7 applications of tangent lines
 
Calc 3.9a
Calc 3.9aCalc 3.9a
Calc 3.9a
 
Calc 3.9a
Calc 3.9aCalc 3.9a
Calc 3.9a
 
Basic Cal - Quarter 1 Week 1-2.pptx
Basic Cal - Quarter 1 Week 1-2.pptxBasic Cal - Quarter 1 Week 1-2.pptx
Basic Cal - Quarter 1 Week 1-2.pptx
 
limits and continuity
limits and continuitylimits and continuity
limits and continuity
 
APPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATIONAPPLICATION OF PARTIAL DIFFERENTIATION
APPLICATION OF PARTIAL DIFFERENTIATION
 
Quadrature
QuadratureQuadrature
Quadrature
 
Applications of Differential Calculus in real life
Applications of Differential Calculus in real life Applications of Differential Calculus in real life
Applications of Differential Calculus in real life
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)
 
Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)Lesson 2: A Catalog of Essential Functions (slides)
Lesson 2: A Catalog of Essential Functions (slides)
 
1552 limits graphically and nume
1552 limits graphically and nume1552 limits graphically and nume
1552 limits graphically and nume
 
The remainder theorem powerpoint
The remainder theorem powerpointThe remainder theorem powerpoint
The remainder theorem powerpoint
 
Limit, Continuity and Differentiability for JEE Main 2014
Limit, Continuity and Differentiability for JEE Main 2014Limit, Continuity and Differentiability for JEE Main 2014
Limit, Continuity and Differentiability for JEE Main 2014
 
L19 increasing &amp; decreasing functions
L19 increasing &amp; decreasing functionsL19 increasing &amp; decreasing functions
L19 increasing &amp; decreasing functions
 
Derivatie class 12
Derivatie class 12Derivatie class 12
Derivatie class 12
 
Ch 2
Ch 2Ch 2
Ch 2
 
Chapter 3
Chapter 3Chapter 3
Chapter 3
 

Más de Tarun Gehlot

Materials 11-01228
Materials 11-01228Materials 11-01228
Materials 11-01228Tarun Gehlot
 
Continuity and end_behavior
Continuity and  end_behaviorContinuity and  end_behavior
Continuity and end_behaviorTarun Gehlot
 
Factoring by the trial and-error method
Factoring by the trial and-error methodFactoring by the trial and-error method
Factoring by the trial and-error methodTarun Gehlot
 
Introduction to finite element analysis
Introduction to finite element analysisIntroduction to finite element analysis
Introduction to finite element analysisTarun Gehlot
 
Finite elements : basis functions
Finite elements : basis functionsFinite elements : basis functions
Finite elements : basis functionsTarun Gehlot
 
Finite elements for 2‐d problems
Finite elements  for 2‐d problemsFinite elements  for 2‐d problems
Finite elements for 2‐d problemsTarun Gehlot
 
Error analysis statistics
Error analysis   statisticsError analysis   statistics
Error analysis statisticsTarun Gehlot
 
Introduction to matlab
Introduction to matlabIntroduction to matlab
Introduction to matlabTarun Gehlot
 
Interpolation functions
Interpolation functionsInterpolation functions
Interpolation functionsTarun Gehlot
 
Propeties of-triangles
Propeties of-trianglesPropeties of-triangles
Propeties of-trianglesTarun Gehlot
 
Gaussian quadratures
Gaussian quadraturesGaussian quadratures
Gaussian quadraturesTarun Gehlot
 
Basics of set theory
Basics of set theoryBasics of set theory
Basics of set theoryTarun Gehlot
 
Numerical integration
Numerical integrationNumerical integration
Numerical integrationTarun Gehlot
 
Applications of set theory
Applications of  set theoryApplications of  set theory
Applications of set theoryTarun Gehlot
 
Miscellneous functions
Miscellneous  functionsMiscellneous  functions
Miscellneous functionsTarun Gehlot
 
Dependent v. independent variables
Dependent v. independent variablesDependent v. independent variables
Dependent v. independent variablesTarun Gehlot
 
Intervals of validity
Intervals of validityIntervals of validity
Intervals of validityTarun Gehlot
 
Modelling with first order differential equations
Modelling with first order differential equationsModelling with first order differential equations
Modelling with first order differential equationsTarun Gehlot
 

Más de Tarun Gehlot (20)

Materials 11-01228
Materials 11-01228Materials 11-01228
Materials 11-01228
 
Binary relations
Binary relationsBinary relations
Binary relations
 
Continuity and end_behavior
Continuity and  end_behaviorContinuity and  end_behavior
Continuity and end_behavior
 
Factoring by the trial and-error method
Factoring by the trial and-error methodFactoring by the trial and-error method
Factoring by the trial and-error method
 
Introduction to finite element analysis
Introduction to finite element analysisIntroduction to finite element analysis
Introduction to finite element analysis
 
Finite elements : basis functions
Finite elements : basis functionsFinite elements : basis functions
Finite elements : basis functions
 
Finite elements for 2‐d problems
Finite elements  for 2‐d problemsFinite elements  for 2‐d problems
Finite elements for 2‐d problems
 
Error analysis statistics
Error analysis   statisticsError analysis   statistics
Error analysis statistics
 
Matlab commands
Matlab commandsMatlab commands
Matlab commands
 
Introduction to matlab
Introduction to matlabIntroduction to matlab
Introduction to matlab
 
Interpolation functions
Interpolation functionsInterpolation functions
Interpolation functions
 
Propeties of-triangles
Propeties of-trianglesPropeties of-triangles
Propeties of-triangles
 
Gaussian quadratures
Gaussian quadraturesGaussian quadratures
Gaussian quadratures
 
Basics of set theory
Basics of set theoryBasics of set theory
Basics of set theory
 
Numerical integration
Numerical integrationNumerical integration
Numerical integration
 
Applications of set theory
Applications of  set theoryApplications of  set theory
Applications of set theory
 
Miscellneous functions
Miscellneous  functionsMiscellneous  functions
Miscellneous functions
 
Dependent v. independent variables
Dependent v. independent variablesDependent v. independent variables
Dependent v. independent variables
 
Intervals of validity
Intervals of validityIntervals of validity
Intervals of validity
 
Modelling with first order differential equations
Modelling with first order differential equationsModelling with first order differential equations
Modelling with first order differential equations
 

Último

Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionSafetyChain Software
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Educationpboyjonauth
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxheathfieldcps1
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxRoyAbrique
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfciinovamais
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxNirmalaLoungPoorunde1
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdfSoniaTolstoy
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesFatimaKhan178732
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docxPoojaSen20
 

Último (20)

Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Mastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory InspectionMastering the Unannounced Regulatory Inspection
Mastering the Unannounced Regulatory Inspection
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
Introduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher EducationIntroduction to ArtificiaI Intelligence in Higher Education
Introduction to ArtificiaI Intelligence in Higher Education
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptxContemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
Contemporary philippine arts from the regions_PPT_Module_12 [Autosaved] (1).pptx
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Employee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptxEmployee wellbeing at the workplace.pptx
Employee wellbeing at the workplace.pptx
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdfBASLIQ CURRENT LOOKBOOK  LOOKBOOK(1) (1).pdf
BASLIQ CURRENT LOOKBOOK LOOKBOOK(1) (1).pdf
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and Actinides
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
mini mental status format.docx
mini    mental       status     format.docxmini    mental       status     format.docx
mini mental status format.docx
 
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
 

Linear Approx and Diffs

  • 1. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka Linear Approximations and Differentials PROBLEM: Approximate the number √ 4 1.1. IDEA: We have seen that a curve lies very close to its tangent line near the point of tangency. This observation is the basis for a method of finding approximate values of functions. The idea is that it might be easy to calculate a value f (a) of a func√ tion (in our case 4 1), but difficult (or even impossible) to compute √ nearby values of f (in our case 4 1.1). So we settle for the easily computed values of the linear function L whose graph is the tangent line of f at (a, f (a)). In other words, we use the tangent line at (a, f (a)) as an approximation to the curve y = f (x) when x is near a. Solution: The point-slope equation of the tangent line is y = f (a) + f ′ (a)(x − a) therefore f (x) ≈ f (a) + f ′ (a)(x − a) if x is close to a √ In particular, if f (x) = 4 x, then √ 4 x≈ √ 4 a+ 1 (x − a) 4a3/4 1 . Plugging in x = 1.1 and a = 1, we get 4a3/4 √ √ 1 1 4 4 1.1 ≈ 1 + (1.1 − 1) = 1 + (0.1) = 1.025 (the true value is 1.024113689...) 3/4 4·1 4 since f ′ (a) = The approximation f (x) ≈ f (a) + f ′ (a)(x − a) (1) is called the linear approximation or tangent line approximation of f at a. The linear function whose graph is this tangent line, that is, L(x) = f (a) + f ′ (a)(x − a) is called the linearization of f at a. 1 (2)
  • 2. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka √ EXAMPLE: Find the linearization of the function f (x) = x + 3 at a = 1 and use it to √ √ approximate the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates? √ Solution: The derivative of f (x) = x + 3 is ( )′ 1 1 1 f ′ (x) = (x + 3)1/2 = (x + 3)1/2−1 = (x + 3)−1/2 = √ 2 2 2 x+3 and so we have f (1) = 2 and f ′ (1) = 1 . Putting these values into (2), we see that the lineariza4 tion is 1 7 x L(x) = f (1) + f ′ (1)(x − 1) = 2 + (x − 1) = + 4 4 4 The corresponding linear approximation (1) is √ 7 x x+3≈ + 4 4 (when x is near 1) In particular, we have √ √ 7 0.98 7 1.05 3.98 ≈ + = 1.995 and 4.05 ≈ + = 2.0125 4 4 4 4 (3) The linear approximation is illustrated in the figure below. We see that, indeed, the tangent line approximation is a good approximation to the given function when x is near 1. We also see that our approximations are overestimates because the tangent line lies above the curve. √ √ Of course, a calculator could give us approximations for 3.98 and 4.05, but the linear approximation gives an approximation over an entire interval. In the table above we compare estimates from the obtained linear approximation with the true values. Notice from this table, and also from the figure above, that the tangent line approximation gives good estimates when x is close to 1 but the accuracy of the approximation deteriorates when x is farther away from 1. √ EXAMPLE: Find the linearization of the function f (x) = x at a = 4 and use it to approximate √ √ the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates? 2
  • 3. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka √ EXAMPLE: Find the linearization of the function f (x) = x at a = 4 and use it to approximate √ √ the numbers 3.98 and 4.05. Are these approximations overestimates or underestimates? √ Solution: The derivative of f (x) = x is ( )′ 1 1 1 f ′ (x) = x1/2 = x1/2−1 = x−1/2 = √ 2 2 2 x and so we have f (4) = 2 and f ′ (4) = 1 . Putting these values into (2), we see that the lineariza4 tion is 1 x L(x) = f (4) + f ′ (4)(x − 4) = 2 + (x − 4) = 1 + 4 4 The corresponding linear approximation (1) is √ x x≈1+ 4 (when x is near 4) In particular, we have √ √ 3.98 4.05 3.98 ≈ 1 + = 1.995 and 4.05 ≈ 1 + = 2.0125 4 4 Note that we got the same results as in (3) since 1+ 3.98 3 + 0.98 3 0.98 7 0.98 =1+ =1+ + = + 4 4 4 4 4 4 1+ 4.05 3 + 1.05 3 1.05 7 1.05 =1+ =1+ + = + 4 4 4 4 4 4 and or, in general, x 3+x−3 3 x−3 7 x−3 =1+ =1+ + = + 4 4 4 4 4 4 Our approximations are overestimates because the tangent line lies above the curve. 1+ √ EXAMPLE: Find the linearization of the function f (x) = 3 1 + x at a = 0 and use it to √ √ approximate the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates? 3
  • 4. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka √ EXAMPLE: Find the linearization of the function f (x) = 3 1 + x at a = 0 and use it to √ √ approximate the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates? √ Solution: The derivative of f (x) = 3 1 + x is ( )′ 1 1 1 f ′ (x) = (1 + x)1/3 = (1 + x)1/3−1 = (1 + x)−2/3 = √ 3 3 3 3 (1 + x)2 and so we have f (0) = 1 and f ′ (0) = 1 . Putting these values into (2), we see that the lineariza3 tion is x L(x) = f (0) + f ′ (0)(x − 0) = 1 + 3 The corresponding linear approximation (1) is √ x 3 1+x≈1+ (when x is near 0) 3 In particular, we have √ −0.05 3 0.95 ≈ 1 + = 0.9833... (the true value is 0.9830475725...) 3 and √ 0.1 3 1.1 ≈ 1 + = 1.0333... (the true value is 1.032280115...) 3 Our approximations are overestimates because the tangent line lies above the curve. √ EXAMPLE: Find the linearization of the function f (x) = 3 x at a = 1 and use it to approximate √ √ the numbers 3 0.95 and 3 1.1. Are these approximations overestimates or underestimates? √ Solution: The derivative of f (x) = 3 x is ( )′ 1 1 1 f ′ (x) = x1/3 = x1/3−1 = x−2/3 = √ 3 3 3 3 x2 and so we have f (1) = 1 and f ′ (1) = 1 . Putting these values into (2), we see that the lineariza3 tion is x−1 2 x L(x) = f (1) + f ′ (1)(x − 1) = 1 + = + 3 3 3 The corresponding linear approximation (1) is √ 2 x 3 x≈ + (when x is near 1) 3 3 In particular, we have √ 2 0.95 3 = 0.9833... (the true value is 0.9830475725...) 0.95 ≈ + 3 3 and √ 2 1.1 3 = 1.0333... (the true value is 1.032280115...) 1.1 ≈ + 3 3 EXAMPLE: Find the linearization of the function f (x) = sin x at a = 0 and use it to approximate the numbers sin(−0.1) and sin(0.1). Are these approximations overestimates or underestimates? 4
  • 5. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka EXAMPLE: Find the linearization of the function f (x) = sin x at a = 0 and use it to approximate the numbers sin(−0.1) and sin(0.1). Are these approximations overestimates or underestimates? Solution: The derivative of f (x) = sin x is f ′ (x) = (sin x)′ = cos x and so we have f (0) = 0 and f ′ (0) = 1. Putting these values into (2), we see that the linearization is L(x) = f (0) + f ′ (0)(x − 0) = 0 + 1 · (x − 0) = x The corresponding linear approximation (1) is sin x ≈ x (when x is near 0) In particular, we have sin(−0.1) ≈ −0.1 (the true value is -0.09983341665...) and sin(0.1) ≈ 0.1 (the true value is 0.09983341665...) The first approximation is an underestimate because the tangent line lies below the curve when x is near 0 from the left. The second approximation is an overestimate because the tangent line lies above the curve when x is near 0 from the right. EXAMPLE: For what values of x is the linear approximation sin x ≈ x accurate to within 0.1? Solution: Accuracy to within 0.1 means that the functions should differ by less than 0.1: | sin x − x| < 0.1 ⇐⇒ −0.1 < sin x − x < 0.1 Using a graphing calculator we can conclude that the approximation sin x ≈ x is accurate to within 0.1 when −0.86 < x < 0.86. 5
  • 6. Section 2.8 Linear Approximations and Differentials 2010 Kiryl Tsishchanka Differentials The ideas behind linear approximations are sometimes formulated in the terminology and notation of differentials. If y = f (x), where f is a differentiable function, then the differential dx is an independent variable; that is, dx can be given the value of any real number. The differential dy is then defined in terms of dx by the equation dy = f ′ (x)dx So dy is a dependent variable; it depends on the values of x and dx. If dx is given a specific value and x is taken to be some specific number in the domain of f, then the numerical value of dy is determined. Let P (x, f (x)) and Q(x + ∆x, f (x + ∆x)) be points on the graph of f and let dx = ∆x. The corresponding change in y is ∆y = f (x + ∆x) − f (x) The slope of the tangent line P R is the derivative f ′ (x). Thus the directed distance from S to R is f ′ (x)dx = dy. Therefore, dy represents the amount that the tangent line rises or falls (the change in the linearization), whereas ∆y represents the amount that the curve y = f (x) rises or falls when x changes by an amount dx. Notice that the approximation ∆y ≈ dy becomes better as ∆x becomes smaller. If we let dx = x − a, then x = a + dx and we can rewrite the linear approximation (1) f (x) ≈ f (a) + f ′ (a)(x − a) in the notation of differentials: f (a + dx) ≈ f (a) + dy √ For instance, for the function f (x) = x + 3 in Example 1, we have dx dy = f ′ (x)dx = √ 2 x+3 If a = 1 and dx = ∆x = 0.05, then 0.05 = 0.0125 dy = √ 2 1+3 and √ 4.05 = f (1.05) ≈ f (1) + dy = 2.0125 just as we found in Example 1. 6