SlideShare ist ein Scribd-Unternehmen logo
1 von 6
Downloaden Sie, um offline zu lesen
CHAPTER 17: EXPONENTIAL AND LOGARITHM
                    FUNCTIONS


                     1. Exponential Functions
DeïŹnition 1.1. An exponential function is a function of the form
                   f (x) = A · bx   (A, b constants)
               t               r
( or f (t) = Ab or f (r) = Ab . . . etc.) That is, it is a function with a
variable exponent.
Example 1.1. The functions f (x) = 2x , g(x) = 3x , h(x) = 100 · 5x are
exponential functions.
Note the diïŹ€erence between the exponential function f (x) = 2x with ïŹxed
base and variable exponent or power, and the power function f (x) = x2
with ïŹxed exponent and variable base.




Exponential functions occur naturally:
    ‱ Radioactive decay
    ‱ unconstrained growth of a population
Example 1.2. The population of Mexico (1980-1983):
                          year pop. (in millions)
                          1980        67.38
                          1981        69.13
                          1982        70.93
                          1983        72.77
The successsive ratios are (approximately) the same:
                               69.13
                                     ≈ 1.026
                               67.38
                               70.93
                                     ≈ 1.026
                               69.13
                                    1
2                      First Science MATH1200 Calculus

In each year, the population increases by a factor of 1.026.
Let P (t) denote the population at time t (where t is the number of years
since 1980):

            P (0) =   67.38 = P0
            P (1) =   67.38 · (1.026)
            P (2) =   67.38 · (1.026) · (1.026) = 67.38 · (1.026)2
            P (3) =   67.38 · (1.026)3
                  .
                  .
                  .
            P (t) =   67.38 · (1.026)t = P0 bt

Thus the estimated population in 1990 is P (10) = 67.38 · (1.026)10 ≈ 87.1




Note: If f (t) = Abt , we get exponential growth if b > 1 and we get expo-
nential decay if b < 1. (b is called the base of the exponential function.)

Example 1.3. Consider the exponential function f (t) = (1/2)t = 2−t :




This function exhibits exponential decay.

Example 1.4. For any given amount of radioactive potassium ( 44 K ),
                                                                 19
the amount remaining one second later is 99.97%. Find a formula for the
amount at time t. (Let A0 be the amount at time 0).
First Science MATH1200 Calculus              3

Solution:
                        A(t) = amount at time t
                        A(0) = A0
                        A(1) = A0 · (0.9997)
                             .
                             .
                             .
                        A(t) = A0 · (0.9997)t

                              2. Half-Lives
Suppose that A(t) = A0 · bt with b < 1
(i.e., we have exponential decay).
Then there is a number h > 0 with
                                        1
                                   bh =
                                        2
Thus, if t is any time, the amount at time t + h is
                 A(t + h) = A0 · bt+h
                           = A0 · b t · b h
                               1
                           =     A0 b t
                               2
                               1
                           =     A(t)
                               2
                               1
                           =      of the amount at time t
                               2
The number h is called the half-life or 1/2-life of the process.
                                44
Example 2.1. The 1/2-life of    19 K   is 22 mins.
Example 2.2. The 1/2-life of Carbon-14 is 5700 years.
A similar computation shows that if we have exponential growth
                           A(t) = A0 bt       b>1
then bd = 2 for some d > 0.
This number d is called the doubling time for the process.
Example 2.3. The 1/2-life of carbon-14 is 5700 years. Find the formula
for the amount at time t (t in years).
Solution: A(t) = A0 bt . What’s b?
We know
                                             1
                         A(5700) = A0 b5700 = A0
                                             2
So
                                         1
                                 b5700 =
                                         2
4                        First Science MATH1200 Calculus
                                     1
                               1   5700
                =⇒ b =                    = (0.5)0.000175 ≈ 0.999878
                               2

So A(t) ≈ A0 · (0.999878)t .

Example 2.4. For a process of exponential decay

                                     A(t) = A0 bt

determine the half-life.
Solution: We must solve
                                                 1
                                          bh =
                                                 2
for h.
To do this, we need logarithms.


                                   3. Logarithms
The logarithm function answers the question: What power of the number a
is equal to the number c?

DeïŹnition 3.1. Suppose that a > 0 and ab = c, then we say that b is the
logarithm of c to the base a and write

                                     loga (c) = b

Example 3.1.

                       23 = 8         =⇒         3 = log2 8
                  104 = 10000         =⇒         4 = log10 10000
                           1                                   1
                   10−1 =             =⇒         −1 = log10
                           10                                 10
                         0
                       a =1           =⇒         0 = loga (1)

Thus ‘loga c’ means the power that a must be raised to in order to get c.

Example 3.2. Thus if we ask: What power of 3 is equal to 47? we are
asking for log3 (47). If we ask: what power of 10 is equal to 50?, we are
asking for log10 (50).

Note that the ‘input’ in a logarithm must be a positive number ; i.e., the
domain of the function f (x) = loga (x) is (0, ∞).
Graph of y = log10 x:
First Science MATH1200 Calculus                  5




Notation: In elementary texts (and in this course), log10 x is simply de-
noted log x , and is called the ‘common logarithm’.
Thus log x = y means 10y = x.
Example 3.3. So log(5) = 0.69897 . . . means 100.69897... = 5.

                        4. Properties of Logarithms
Theorem 4.1. Fix a > 0.
  (1) loga (1) = 0
  (2) loga (a) = 1
  (3) loga (c1 · c2 ) = loga (c1 ) + loga (c2 )
              c1
  (4) loga           = loga (c1 ) − loga (c2 )
              c2
  (5) loga (cd ) = d loga (c)
Proof:
  (1) Since a0 = 1.
  (2) Since a1 = a.
  (3) Let b1 = loga (c1 ) and b2 = loga (c2 ). Then ab1 = c1 and ab2 = c2 . So
                         ab1 +b2 = c1 · c2        (First Law )
                 =⇒ loga (c1 c2 ) = b1 + b2 = loga (c1 ) + loga (c2 )
    (4)
                                   c1                   c1
               loga (c1 ) = loga      · c2    = loga          + loga (c2 )
                                   c2                   c2
      (using 3.).
      Now subtract loga (c2 ) from both sides.
  (5) Let b = loga (c). So ab = c.
      Thus abd = (ab )d = cd (Second Law).
      So loga (cd ) = bd = d loga (c)
Note: Taking d = −1 in 5. gives
                                       1
                               loga          = − loga (c)
                                       c
6                       First Science MATH1200 Calculus

Example 4.1. The formula for the amount of radioactive polonium is
                   A(t) = A0 (0.99506)t         (t in days )
What is the half-life?
Solution: We need to solve .99506h = 1/2 for h:
                      log(0.99506h ) = log(1/2)
                    h · log(0.99506) = log(1/2)
                                         log(1/2)
                                   h =
                                       log(0.99506)
                                       ≈ 140 (days )
Thus for a process of exponential decay we have,
                                           log(1/2)
                            half-life =
                                          log(base)
Similarly, for a process of exponential growth,
                                               log(2)
                        doubling time =
                                             log(base)
Example 4.2. Estimate the doubling time of the Mexican population.
Solution:
                          log(2)
                   d=              = 27 (years )
                        log(1.026)
In general, logarithms are useful for solving equations where the unknown
occurs as an exponent:
Example 4.3. Solve 7x = 231 · 5x for x.
Solution: Take logs of both sides:
                           log(7x ) = log(231 · 5x )
                          x log(7) = log(231) + x log(5)
                x(log(7) − log(5)) = log(231)
                                         log 231
                                 x =
                                      log 7 − log 5
                                    ≈ 16.175
         5. Derivatives of logarithms and exponentials
What are
                            d x          d
                              (2 ) or      log(x)?
                           dx           dx
We will not be in a position to answer these questions until we have deïŹned
the natural logarithm function, and to deïŹne the natural logarithm we have
to ïŹrst develop the theory of integration.

Weitere Àhnliche Inhalte

Was ist angesagt?

Mesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsMesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsGabriel Peyré
 
Euler lagrange equation
Euler lagrange equationEuler lagrange equation
Euler lagrange equationmufti195
 
Ps02 cmth03 unit 1
Ps02 cmth03 unit 1Ps02 cmth03 unit 1
Ps02 cmth03 unit 1Prakash Dabhi
 
Learning Sparse Representation
Learning Sparse RepresentationLearning Sparse Representation
Learning Sparse RepresentationGabriel Peyré
 
Geodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and GraphicsGeodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and GraphicsGabriel Peyré
 
Hull White model presentation
Hull White model presentationHull White model presentation
Hull White model presentationStephan Chang
 
16 fft
16 fft16 fft
16 fftabhi5556
 
Dynamical systems solved ex
Dynamical systems solved exDynamical systems solved ex
Dynamical systems solved exMaths Tutoring
 
Supplement to local voatility
Supplement to local voatilitySupplement to local voatility
Supplement to local voatilityIlya Gikhman
 
Applied Calculus Chapter 2 vector valued function
Applied Calculus Chapter  2 vector valued functionApplied Calculus Chapter  2 vector valued function
Applied Calculus Chapter 2 vector valued functionJ C
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Pierre Jacob
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Pierre Jacob
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsMatthew Leingang
 
lecture 4
lecture 4lecture 4
lecture 4sajinsc
 
Parameterized curves in r^3
Parameterized curves in r^3Parameterized curves in r^3
Parameterized curves in r^3Tarun Gehlot
 
Cs229 cvxopt
Cs229 cvxoptCs229 cvxopt
Cs229 cvxoptcerezaso
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsMatthew Leingang
 

Was ist angesagt? (20)

Mesh Processing Course : Geodesics
Mesh Processing Course : GeodesicsMesh Processing Course : Geodesics
Mesh Processing Course : Geodesics
 
Euler lagrange equation
Euler lagrange equationEuler lagrange equation
Euler lagrange equation
 
Ps02 cmth03 unit 1
Ps02 cmth03 unit 1Ps02 cmth03 unit 1
Ps02 cmth03 unit 1
 
Learning Sparse Representation
Learning Sparse RepresentationLearning Sparse Representation
Learning Sparse Representation
 
Geodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and GraphicsGeodesic Method in Computer Vision and Graphics
Geodesic Method in Computer Vision and Graphics
 
Hull White model presentation
Hull White model presentationHull White model presentation
Hull White model presentation
 
16 fft
16 fft16 fft
16 fft
 
Dynamical systems solved ex
Dynamical systems solved exDynamical systems solved ex
Dynamical systems solved ex
 
Supplement to local voatility
Supplement to local voatilitySupplement to local voatility
Supplement to local voatility
 
Applied Calculus Chapter 2 vector valued function
Applied Calculus Chapter  2 vector valued functionApplied Calculus Chapter  2 vector valued function
Applied Calculus Chapter 2 vector valued function
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...
 
Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...Estimation of the score vector and observed information matrix in intractable...
Estimation of the score vector and observed information matrix in intractable...
 
big_oh
big_ohbig_oh
big_oh
 
Lesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite IntegralsLesson 31: Evaluating Definite Integrals
Lesson 31: Evaluating Definite Integrals
 
lecture 4
lecture 4lecture 4
lecture 4
 
Parameterized curves in r^3
Parameterized curves in r^3Parameterized curves in r^3
Parameterized curves in r^3
 
Cs229 cvxopt
Cs229 cvxoptCs229 cvxopt
Cs229 cvxopt
 
Chapter 5
Chapter 5Chapter 5
Chapter 5
 
Lesson 7: Vector-valued functions
Lesson 7: Vector-valued functionsLesson 7: Vector-valued functions
Lesson 7: Vector-valued functions
 
Figures
FiguresFigures
Figures
 

Ähnlich wie Notes 17

MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docxMATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docxandreecapon
 
Sect2 1
Sect2 1Sect2 1
Sect2 1inKFUPM
 
Fourier series
Fourier seriesFourier series
Fourier seriesTarun Gehlot
 
Future CMB Experiments
Future CMB ExperimentsFuture CMB Experiments
Future CMB ExperimentsCosmoAIMS Bassett
 
Amth250 octave matlab some solutions (1)
Amth250 octave matlab some solutions (1)Amth250 octave matlab some solutions (1)
Amth250 octave matlab some solutions (1)asghar123456
 
Differential Equations 4th Edition Blanchard Solutions Manual
Differential Equations 4th Edition Blanchard Solutions ManualDifferential Equations 4th Edition Blanchard Solutions Manual
Differential Equations 4th Edition Blanchard Solutions Manualqyfuc
 
Complex roots of the characteristic equation
Complex roots of the characteristic equationComplex roots of the characteristic equation
Complex roots of the characteristic equationTarun Gehlot
 
Local Volatility 1
Local Volatility 1Local Volatility 1
Local Volatility 1Ilya Gikhman
 
03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdfROCIOMAMANIALATA1
 
logarithmic, exponential, trigonometric functions and their graphs.ppt
logarithmic, exponential, trigonometric functions and their graphs.pptlogarithmic, exponential, trigonometric functions and their graphs.ppt
logarithmic, exponential, trigonometric functions and their graphs.pptYohannesAndualem1
 
Fundamentals of Transport Phenomena ChE 715
Fundamentals of Transport Phenomena ChE 715Fundamentals of Transport Phenomena ChE 715
Fundamentals of Transport Phenomena ChE 715HelpWithAssignment.com
 
Markov Tutorial CDC Shanghai 2009
Markov Tutorial CDC Shanghai 2009Markov Tutorial CDC Shanghai 2009
Markov Tutorial CDC Shanghai 2009Sean Meyn
 
Cutoff frequency
Cutoff frequencyCutoff frequency
Cutoff frequencysarahgolfinger
 
torsionbinormalnotes
torsionbinormalnotestorsionbinormalnotes
torsionbinormalnotesJeremy Lane
 

Ähnlich wie Notes 17 (20)

Assignment6
Assignment6Assignment6
Assignment6
 
Solved problems
Solved problemsSolved problems
Solved problems
 
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docxMATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
MATLAB sessions Laboratory 6MAT 275 Laboratory 6Forced .docx
 
Sect2 1
Sect2 1Sect2 1
Sect2 1
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Future CMB Experiments
Future CMB ExperimentsFuture CMB Experiments
Future CMB Experiments
 
Amth250 octave matlab some solutions (1)
Amth250 octave matlab some solutions (1)Amth250 octave matlab some solutions (1)
Amth250 octave matlab some solutions (1)
 
Differential Equations 4th Edition Blanchard Solutions Manual
Differential Equations 4th Edition Blanchard Solutions ManualDifferential Equations 4th Edition Blanchard Solutions Manual
Differential Equations 4th Edition Blanchard Solutions Manual
 
Complex roots of the characteristic equation
Complex roots of the characteristic equationComplex roots of the characteristic equation
Complex roots of the characteristic equation
 
Local Volatility 1
Local Volatility 1Local Volatility 1
Local Volatility 1
 
03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf
 
logarithmic, exponential, trigonometric functions and their graphs.ppt
logarithmic, exponential, trigonometric functions and their graphs.pptlogarithmic, exponential, trigonometric functions and their graphs.ppt
logarithmic, exponential, trigonometric functions and their graphs.ppt
 
chapter3.ppt
chapter3.pptchapter3.ppt
chapter3.ppt
 
D021018022
D021018022D021018022
D021018022
 
Fundamentals of Transport Phenomena ChE 715
Fundamentals of Transport Phenomena ChE 715Fundamentals of Transport Phenomena ChE 715
Fundamentals of Transport Phenomena ChE 715
 
Markov Tutorial CDC Shanghai 2009
Markov Tutorial CDC Shanghai 2009Markov Tutorial CDC Shanghai 2009
Markov Tutorial CDC Shanghai 2009
 
Cutoff frequency
Cutoff frequencyCutoff frequency
Cutoff frequency
 
torsionbinormalnotes
torsionbinormalnotestorsionbinormalnotes
torsionbinormalnotes
 
3.pdf
3.pdf3.pdf
3.pdf
 
C slides 11
C slides 11C slides 11
C slides 11
 

KĂŒrzlich hochgeladen

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWERMadyBayot
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDropbox
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxRemote DBA Services
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityWSO2
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamUiPathCommunity
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...DianaGray10
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontologyjohnbeverley2021
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024The Digital Insurer
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesrafiqahmad00786416
 

KĂŒrzlich hochgeladen (20)

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWEREMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Vector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptxVector Search -An Introduction in Oracle Database 23ai.pptx
Vector Search -An Introduction in Oracle Database 23ai.pptx
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital AdaptabilityPlatformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
 
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 AmsterdamDEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal OntologySix Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering DevelopersWSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 

Notes 17

  • 1. CHAPTER 17: EXPONENTIAL AND LOGARITHM FUNCTIONS 1. Exponential Functions DeïŹnition 1.1. An exponential function is a function of the form f (x) = A · bx (A, b constants) t r ( or f (t) = Ab or f (r) = Ab . . . etc.) That is, it is a function with a variable exponent. Example 1.1. The functions f (x) = 2x , g(x) = 3x , h(x) = 100 · 5x are exponential functions. Note the diïŹ€erence between the exponential function f (x) = 2x with ïŹxed base and variable exponent or power, and the power function f (x) = x2 with ïŹxed exponent and variable base. Exponential functions occur naturally: ‱ Radioactive decay ‱ unconstrained growth of a population Example 1.2. The population of Mexico (1980-1983): year pop. (in millions) 1980 67.38 1981 69.13 1982 70.93 1983 72.77 The successsive ratios are (approximately) the same: 69.13 ≈ 1.026 67.38 70.93 ≈ 1.026 69.13 1
  • 2. 2 First Science MATH1200 Calculus In each year, the population increases by a factor of 1.026. Let P (t) denote the population at time t (where t is the number of years since 1980): P (0) = 67.38 = P0 P (1) = 67.38 · (1.026) P (2) = 67.38 · (1.026) · (1.026) = 67.38 · (1.026)2 P (3) = 67.38 · (1.026)3 . . . P (t) = 67.38 · (1.026)t = P0 bt Thus the estimated population in 1990 is P (10) = 67.38 · (1.026)10 ≈ 87.1 Note: If f (t) = Abt , we get exponential growth if b > 1 and we get expo- nential decay if b < 1. (b is called the base of the exponential function.) Example 1.3. Consider the exponential function f (t) = (1/2)t = 2−t : This function exhibits exponential decay. Example 1.4. For any given amount of radioactive potassium ( 44 K ), 19 the amount remaining one second later is 99.97%. Find a formula for the amount at time t. (Let A0 be the amount at time 0).
  • 3. First Science MATH1200 Calculus 3 Solution: A(t) = amount at time t A(0) = A0 A(1) = A0 · (0.9997) . . . A(t) = A0 · (0.9997)t 2. Half-Lives Suppose that A(t) = A0 · bt with b < 1 (i.e., we have exponential decay). Then there is a number h > 0 with 1 bh = 2 Thus, if t is any time, the amount at time t + h is A(t + h) = A0 · bt+h = A0 · b t · b h 1 = A0 b t 2 1 = A(t) 2 1 = of the amount at time t 2 The number h is called the half-life or 1/2-life of the process. 44 Example 2.1. The 1/2-life of 19 K is 22 mins. Example 2.2. The 1/2-life of Carbon-14 is 5700 years. A similar computation shows that if we have exponential growth A(t) = A0 bt b>1 then bd = 2 for some d > 0. This number d is called the doubling time for the process. Example 2.3. The 1/2-life of carbon-14 is 5700 years. Find the formula for the amount at time t (t in years). Solution: A(t) = A0 bt . What’s b? We know 1 A(5700) = A0 b5700 = A0 2 So 1 b5700 = 2
  • 4. 4 First Science MATH1200 Calculus 1 1 5700 =⇒ b = = (0.5)0.000175 ≈ 0.999878 2 So A(t) ≈ A0 · (0.999878)t . Example 2.4. For a process of exponential decay A(t) = A0 bt determine the half-life. Solution: We must solve 1 bh = 2 for h. To do this, we need logarithms. 3. Logarithms The logarithm function answers the question: What power of the number a is equal to the number c? DeïŹnition 3.1. Suppose that a > 0 and ab = c, then we say that b is the logarithm of c to the base a and write loga (c) = b Example 3.1. 23 = 8 =⇒ 3 = log2 8 104 = 10000 =⇒ 4 = log10 10000 1 1 10−1 = =⇒ −1 = log10 10 10 0 a =1 =⇒ 0 = loga (1) Thus ‘loga c’ means the power that a must be raised to in order to get c. Example 3.2. Thus if we ask: What power of 3 is equal to 47? we are asking for log3 (47). If we ask: what power of 10 is equal to 50?, we are asking for log10 (50). Note that the ‘input’ in a logarithm must be a positive number ; i.e., the domain of the function f (x) = loga (x) is (0, ∞). Graph of y = log10 x:
  • 5. First Science MATH1200 Calculus 5 Notation: In elementary texts (and in this course), log10 x is simply de- noted log x , and is called the ‘common logarithm’. Thus log x = y means 10y = x. Example 3.3. So log(5) = 0.69897 . . . means 100.69897... = 5. 4. Properties of Logarithms Theorem 4.1. Fix a > 0. (1) loga (1) = 0 (2) loga (a) = 1 (3) loga (c1 · c2 ) = loga (c1 ) + loga (c2 ) c1 (4) loga = loga (c1 ) − loga (c2 ) c2 (5) loga (cd ) = d loga (c) Proof: (1) Since a0 = 1. (2) Since a1 = a. (3) Let b1 = loga (c1 ) and b2 = loga (c2 ). Then ab1 = c1 and ab2 = c2 . So ab1 +b2 = c1 · c2 (First Law ) =⇒ loga (c1 c2 ) = b1 + b2 = loga (c1 ) + loga (c2 ) (4) c1 c1 loga (c1 ) = loga · c2 = loga + loga (c2 ) c2 c2 (using 3.). Now subtract loga (c2 ) from both sides. (5) Let b = loga (c). So ab = c. Thus abd = (ab )d = cd (Second Law). So loga (cd ) = bd = d loga (c) Note: Taking d = −1 in 5. gives 1 loga = − loga (c) c
  • 6. 6 First Science MATH1200 Calculus Example 4.1. The formula for the amount of radioactive polonium is A(t) = A0 (0.99506)t (t in days ) What is the half-life? Solution: We need to solve .99506h = 1/2 for h: log(0.99506h ) = log(1/2) h · log(0.99506) = log(1/2) log(1/2) h = log(0.99506) ≈ 140 (days ) Thus for a process of exponential decay we have, log(1/2) half-life = log(base) Similarly, for a process of exponential growth, log(2) doubling time = log(base) Example 4.2. Estimate the doubling time of the Mexican population. Solution: log(2) d= = 27 (years ) log(1.026) In general, logarithms are useful for solving equations where the unknown occurs as an exponent: Example 4.3. Solve 7x = 231 · 5x for x. Solution: Take logs of both sides: log(7x ) = log(231 · 5x ) x log(7) = log(231) + x log(5) x(log(7) − log(5)) = log(231) log 231 x = log 7 − log 5 ≈ 16.175 5. Derivatives of logarithms and exponentials What are d x d (2 ) or log(x)? dx dx We will not be in a position to answer these questions until we have deïŹned the natural logarithm function, and to deïŹne the natural logarithm we have to ïŹrst develop the theory of integration.