This document contains the notes from a calculus class. It provides announcements about the final exam schedule and review sessions. It then discusses the technique of u-substitution for both indefinite and definite integrals. Examples are provided to illustrate how to use u-substitution to evaluate integrals involving trigonometric, polynomial, and other functions. The document emphasizes that u-substitution often makes evaluating integrals much easier than expanding them out directly.
Lesson 27: Integration by Substitution, part II (Section 10 version)Matthew Leingang
The document is the notes for a Calculus I class. It provides announcements for the upcoming class on Monday, which will involve reviewing course material rather than new topics. Examples are given of integration by substitution, including exponential, odd, and even functions. Multiple methods for substitutions are presented. The properties of odd and even functions are defined, and examples are shown graphically. Symmetric functions and their behavior under combinations are discussed.
Integration by substitution is the chain rule in reverse.
NOTE: the final location is section specific. Section 1 (morning) is in SILV 703, Section 11 (afternoon) is in CANT 200
Lesson 27: Integration by Substitution (Section 4 version)Matthew Leingang
The document outlines a calculus lecture on integration by substitution. It provides examples of using u-substitution to find antiderivatives of expressions like √(x^2+1) and tan(x). The key ideas are that if u is a function of x, its derivative du/dx can be used to rewrite the integrand and perform a u-substitution integration.
Lesson 27: Integration by Substitution (Section 10 version)Matthew Leingang
The method of substitution is the chain rule in reverse. At first it looks magical, then logical, and then you realize there's an art to choosing the right substitution. We try to demystify with many worked-out examples.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Lesson 15: Exponential Growth and Decay (slides)Matthew Leingang
Many problems in nature are expressible in terms of a certain differential equation that has a solution in terms of exponential functions. We look at the equation in general and some fun applications, including radioactivity, cooling, and interest.
The document provides a brief refresher on key concepts in calculus, including:
1) The basic rules of differentiation like the power rule, constant rule, sum and difference rule, product rule, and quotient rule.
2) The chain rule for finding derivatives of composite functions.
3) Higher-order derivatives and notation for second and third derivatives.
4) Concepts of absolute extrema and critical points for finding the maximum and minimum values of functions.
Lesson 27: Integration by Substitution, part II (Section 10 version)Matthew Leingang
The document is the notes for a Calculus I class. It provides announcements for the upcoming class on Monday, which will involve reviewing course material rather than new topics. Examples are given of integration by substitution, including exponential, odd, and even functions. Multiple methods for substitutions are presented. The properties of odd and even functions are defined, and examples are shown graphically. Symmetric functions and their behavior under combinations are discussed.
Integration by substitution is the chain rule in reverse.
NOTE: the final location is section specific. Section 1 (morning) is in SILV 703, Section 11 (afternoon) is in CANT 200
Lesson 27: Integration by Substitution (Section 4 version)Matthew Leingang
The document outlines a calculus lecture on integration by substitution. It provides examples of using u-substitution to find antiderivatives of expressions like √(x^2+1) and tan(x). The key ideas are that if u is a function of x, its derivative du/dx can be used to rewrite the integrand and perform a u-substitution integration.
Lesson 27: Integration by Substitution (Section 10 version)Matthew Leingang
The method of substitution is the chain rule in reverse. At first it looks magical, then logical, and then you realize there's an art to choosing the right substitution. We try to demystify with many worked-out examples.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
Lesson 15: Exponential Growth and Decay (slides)Matthew Leingang
Many problems in nature are expressible in terms of a certain differential equation that has a solution in terms of exponential functions. We look at the equation in general and some fun applications, including radioactivity, cooling, and interest.
The document provides a brief refresher on key concepts in calculus, including:
1) The basic rules of differentiation like the power rule, constant rule, sum and difference rule, product rule, and quotient rule.
2) The chain rule for finding derivatives of composite functions.
3) Higher-order derivatives and notation for second and third derivatives.
4) Concepts of absolute extrema and critical points for finding the maximum and minimum values of functions.
This section introduces general and particular solutions to differential equations of the form y' = f(x) through direct integration and evaluation of constants. Examples provided include:
1) Integrating y' = 2x + 1 and applying the initial condition x = 0, y = 3 yields the general solution y(x) = x^2 + x + 3.
2) Integrating y' = (x - 2)^2 and applying x = 2, y = 1 yields y(x) = (1/3)(x - 2)^3.
3) Six more examples of first-order differential equations are worked through to find their general solutions.
The course program includes sessions on discrete models in computer vision, message passing algorithms like dynamic programming and tree-reweighted message passing, quadratic pseudo-boolean optimization, transformation and move-making methods, speed and efficiency of algorithms, and a comparison of inference methods. Recent advances like dual decomposition and higher-order models will also be discussed. All materials from the tutorial will be made available online after the conference.
Stuff You Must Know Cold for the AP Calculus BC Exam!A Jorge Garcia
This document provides a summary of key concepts from AP Calculus that students must know, including:
- Differentiation rules like product rule, quotient rule, and chain rule
- Integration techniques like Riemann sums, trapezoidal rule, and Simpson's rule
- Theorems related to derivatives and integrals like the Mean Value Theorem, Fundamental Theorem of Calculus, and Rolle's Theorem
- Common trigonometric derivatives and integrals
- Series approximations like Taylor series and Maclaurin series
- Calculus topics for polar coordinates, parametric equations, and vectors
This document provides an outline for a calculus lecture on basic differentiation rules. It includes objectives to understand key rules like the constant multiple rule, sum rule, and derivatives of sine and cosine. Examples are worked through to find the derivatives of functions like squaring, cubing, square root, and cube root using the definition of the derivative. Graphs and properties of derived functions are also discussed.
This document provides an introduction to inverse problems and their applications. It summarizes integral equations like Volterra and Fredholm equations of the first and second kind. It also describes inverse problems for partial differential equations, including inverse convection-diffusion, Poisson, and Laplace problems. Applications mentioned include medical imaging, non-destructive testing, and geophysics. Bibliographic references are provided.
The document summarizes the Metropolis-adjusted Langevin algorithm (MALA) for sampling from log-concave probability measures in high dimensions. It introduces MALA and different proposal distributions, including random walk, Ornstein-Uhlenbeck, and Euler proposals. It discusses known results on optimal scaling, diffusion limits, ergodicity, and mixing time bounds. The main result is a contraction property for the MALA transition kernel under appropriate assumptions, implying dimension-independent bounds on mixing times.
The document discusses evaluating definite integrals. It begins by reviewing the definition of the definite integral as a limit and properties of integrals such as additivity. It then covers estimating integrals using the Midpoint Rule and properties for comparing integrals. Examples are provided of evaluating definite integrals using known formulas or the Midpoint Rule. The integral is discussed as computing the total change, and an outline of future topics like indefinite integrals and computing area is presented.
This document discusses sparse representations and dictionary learning. It introduces the concepts of sparsity, redundant dictionaries, and sparse coding. The goal of sparse coding is to find the sparsest representation of signals using an overcomplete dictionary. Dictionary learning aims to learn an optimized dictionary from exemplar data by alternately solving sparse coding subproblems and dictionary update steps. Patch-based dictionary learning has applications in image denoising and texture synthesis. In contrast to PCA, learned dictionaries contain non-linear atoms adapted to the data.
This document contains notes from a calculus class. It provides the outline and key points about the Fundamental Theorem of Calculus. It discusses the first and second Fundamental Theorems of Calculus, including proofs and examples. It also provides brief biographies of several important mathematicians that contributed to the development of calculus, including the Fundamental Theorem of Calculus, such as Isaac Newton, Gottfried Leibniz, James Gregory, and Isaac Barrow.
This document provides summaries of common derivatives and integrals, including:
- Basic properties and formulas for derivatives and integrals of functions like polynomials, trig functions, inverse trig functions, exponentials/logarithms, and more.
- Standard integration techniques like u-substitution, integration by parts, and trig substitutions.
- How to evaluate integrals of products and quotients of trig functions using properties like angle addition formulas and half-angle identities.
- How to use partial fractions to decompose rational functions for the purpose of integration.
So in summary, this document outlines essential derivatives and integrals for many common functions, along with standard integration strategies and techniques.
This document provides an overview of linear support vector machines (SVMs) for classification. It discusses how SVMs find the optimal separating hyperplane between two classes by maximizing the margin, or distance between the closest data points of each class.
The problem of finding this optimal hyperplane is formulated as a quadratic programming problem that minimizes the norm of the weight vector subject to constraints requiring data points to lie on the correct side of the margin. Alternately, the problem can be formulated as a linear program that minimizes the L1 norm of the weight vector.
Finally, the document outlines the key steps in SVM classification and references further resources on the topic.
This document summarizes a presentation on numerically solving spatiotemporal models from ecology by implementing an implicit finite difference method in C++. It discusses classical population dynamics models, extending these to include continuous spatial position by using reaction-diffusion systems. It describes discretizing the domain and deriving finite-difference schemes, then solving the equations using GMRES with an ILU preconditioner. Questions addressed include convergence rates and modeling plankton dynamics.
The document discusses the multilayer perceptron (MLP), a neural network architecture that can solve nonlinear classification problems. It describes the structure of an MLP with one hidden layer and discusses training an MLP using backpropagation. Backpropagation is a gradient descent algorithm that propagates error signals backward from the output to hidden layers to update weights. The document also introduces the resilient backpropagation (RPROP) algorithm, which uses individual adaptive learning rates, and discusses second-order learning algorithms like the Levenberg-Marquardt algorithm.
This document is from a Calculus I class at New York University and covers basic differentiation rules. It includes announcements about homework and a quiz. The objectives are to understand and use rules for differentiating constant functions, constants multiplied by functions, sums and differences of functions, and sine and cosine functions. Examples are provided of finding the derivatives of squaring, cubing, and square root functions using the definition of the derivative. Graphs and properties of derivatives are also discussed.
Lesson 9: The Product and Quotient Rules (slides)Matthew Leingang
The product rule is generally better because:
- It is more systematic and avoids mistakes from expanding products
- It works for any differentiable functions u and v, not just polynomials
- It provides insight into the structure of the derivative that direct computation does not
So in this example, using the product rule is preferable to direct multiplication.
CVPR2010: higher order models in computer vision: Part 1, 2zukun
This document discusses tractable higher order models in computer vision using random field models. It introduces Markov random fields (MRFs) and factor graphs as graphical models for computer vision problems. Higher order models that include factors over cliques of more than two variables can model problems more accurately but are generally intractable. The document discusses various inference techniques for higher order models such as relaxation, message passing, and decomposition methods. It provides examples of how higher order and global models can be used in problems like segmentation, stereo matching, reconstruction, and denoising.
The document discusses properties and applications of the Z-transform, which is used to analyze linear discrete-time signals. Some key points:
1) The Z-transform plays an important role in analyzing discrete-time signals and is defined as the sum of the signal samples multiplied by a complex variable z raised to the power of the sample's time index.
2) Important properties of the Z-transform include linearity, time-shifting, frequency-shifting, differentiation in the Z-domain, and the convolution theorem.
3) The Z-transform can be used to find the transform of basic sequences like the unit impulse, unit step, exponentials, polynomials, and derivatives of signals.
The document discusses using the Fast Fourier Transform (FFT) algorithm to multiply polynomials in faster than quadratic time. It explains that the FFT represents polynomials in a point-value representation using complex roots of unity, which allows multiplication to be performed pointwise in linear time. The FFT algorithm recursively decomposes the polynomial multiplication problem into smaller subproblems of half the size, using divide and conquer, to compute the discrete Fourier transform in O(n log n) time rather than the naive O(n^2) time. Interpolation can also be performed in similar time to convert back from the point-value representation to coefficients. Overall the FFT provides a faster algorithm for polynomial multiplication and convolution.
Optimization is a killer feature of the derivative. Not only do we often want to optimize some system, nature does as well. We give a procedure and many examples.
An antiderivative of a function is a function whose derivative is the given function. The problem of antidifferentiation is interesting, complicated, and useful, especially when discussing motion.
This document contains lecture notes on the Fundamental Theorem of Calculus. It begins with announcements about the final exam date and current grade distribution. The outline then reviews the Evaluation Theorem and introduces the First and Second Fundamental Theorems of Calculus. It provides examples of how the integral can represent total change in concepts like distance, cost, and mass. Biographies are also included of mathematicians like Gregory, Barrow, Newton, and Leibniz who contributed to the development of calculus.
This section introduces general and particular solutions to differential equations of the form y' = f(x) through direct integration and evaluation of constants. Examples provided include:
1) Integrating y' = 2x + 1 and applying the initial condition x = 0, y = 3 yields the general solution y(x) = x^2 + x + 3.
2) Integrating y' = (x - 2)^2 and applying x = 2, y = 1 yields y(x) = (1/3)(x - 2)^3.
3) Six more examples of first-order differential equations are worked through to find their general solutions.
The course program includes sessions on discrete models in computer vision, message passing algorithms like dynamic programming and tree-reweighted message passing, quadratic pseudo-boolean optimization, transformation and move-making methods, speed and efficiency of algorithms, and a comparison of inference methods. Recent advances like dual decomposition and higher-order models will also be discussed. All materials from the tutorial will be made available online after the conference.
Stuff You Must Know Cold for the AP Calculus BC Exam!A Jorge Garcia
This document provides a summary of key concepts from AP Calculus that students must know, including:
- Differentiation rules like product rule, quotient rule, and chain rule
- Integration techniques like Riemann sums, trapezoidal rule, and Simpson's rule
- Theorems related to derivatives and integrals like the Mean Value Theorem, Fundamental Theorem of Calculus, and Rolle's Theorem
- Common trigonometric derivatives and integrals
- Series approximations like Taylor series and Maclaurin series
- Calculus topics for polar coordinates, parametric equations, and vectors
This document provides an outline for a calculus lecture on basic differentiation rules. It includes objectives to understand key rules like the constant multiple rule, sum rule, and derivatives of sine and cosine. Examples are worked through to find the derivatives of functions like squaring, cubing, square root, and cube root using the definition of the derivative. Graphs and properties of derived functions are also discussed.
This document provides an introduction to inverse problems and their applications. It summarizes integral equations like Volterra and Fredholm equations of the first and second kind. It also describes inverse problems for partial differential equations, including inverse convection-diffusion, Poisson, and Laplace problems. Applications mentioned include medical imaging, non-destructive testing, and geophysics. Bibliographic references are provided.
The document summarizes the Metropolis-adjusted Langevin algorithm (MALA) for sampling from log-concave probability measures in high dimensions. It introduces MALA and different proposal distributions, including random walk, Ornstein-Uhlenbeck, and Euler proposals. It discusses known results on optimal scaling, diffusion limits, ergodicity, and mixing time bounds. The main result is a contraction property for the MALA transition kernel under appropriate assumptions, implying dimension-independent bounds on mixing times.
The document discusses evaluating definite integrals. It begins by reviewing the definition of the definite integral as a limit and properties of integrals such as additivity. It then covers estimating integrals using the Midpoint Rule and properties for comparing integrals. Examples are provided of evaluating definite integrals using known formulas or the Midpoint Rule. The integral is discussed as computing the total change, and an outline of future topics like indefinite integrals and computing area is presented.
This document discusses sparse representations and dictionary learning. It introduces the concepts of sparsity, redundant dictionaries, and sparse coding. The goal of sparse coding is to find the sparsest representation of signals using an overcomplete dictionary. Dictionary learning aims to learn an optimized dictionary from exemplar data by alternately solving sparse coding subproblems and dictionary update steps. Patch-based dictionary learning has applications in image denoising and texture synthesis. In contrast to PCA, learned dictionaries contain non-linear atoms adapted to the data.
This document contains notes from a calculus class. It provides the outline and key points about the Fundamental Theorem of Calculus. It discusses the first and second Fundamental Theorems of Calculus, including proofs and examples. It also provides brief biographies of several important mathematicians that contributed to the development of calculus, including the Fundamental Theorem of Calculus, such as Isaac Newton, Gottfried Leibniz, James Gregory, and Isaac Barrow.
This document provides summaries of common derivatives and integrals, including:
- Basic properties and formulas for derivatives and integrals of functions like polynomials, trig functions, inverse trig functions, exponentials/logarithms, and more.
- Standard integration techniques like u-substitution, integration by parts, and trig substitutions.
- How to evaluate integrals of products and quotients of trig functions using properties like angle addition formulas and half-angle identities.
- How to use partial fractions to decompose rational functions for the purpose of integration.
So in summary, this document outlines essential derivatives and integrals for many common functions, along with standard integration strategies and techniques.
This document provides an overview of linear support vector machines (SVMs) for classification. It discusses how SVMs find the optimal separating hyperplane between two classes by maximizing the margin, or distance between the closest data points of each class.
The problem of finding this optimal hyperplane is formulated as a quadratic programming problem that minimizes the norm of the weight vector subject to constraints requiring data points to lie on the correct side of the margin. Alternately, the problem can be formulated as a linear program that minimizes the L1 norm of the weight vector.
Finally, the document outlines the key steps in SVM classification and references further resources on the topic.
This document summarizes a presentation on numerically solving spatiotemporal models from ecology by implementing an implicit finite difference method in C++. It discusses classical population dynamics models, extending these to include continuous spatial position by using reaction-diffusion systems. It describes discretizing the domain and deriving finite-difference schemes, then solving the equations using GMRES with an ILU preconditioner. Questions addressed include convergence rates and modeling plankton dynamics.
The document discusses the multilayer perceptron (MLP), a neural network architecture that can solve nonlinear classification problems. It describes the structure of an MLP with one hidden layer and discusses training an MLP using backpropagation. Backpropagation is a gradient descent algorithm that propagates error signals backward from the output to hidden layers to update weights. The document also introduces the resilient backpropagation (RPROP) algorithm, which uses individual adaptive learning rates, and discusses second-order learning algorithms like the Levenberg-Marquardt algorithm.
This document is from a Calculus I class at New York University and covers basic differentiation rules. It includes announcements about homework and a quiz. The objectives are to understand and use rules for differentiating constant functions, constants multiplied by functions, sums and differences of functions, and sine and cosine functions. Examples are provided of finding the derivatives of squaring, cubing, and square root functions using the definition of the derivative. Graphs and properties of derivatives are also discussed.
Lesson 9: The Product and Quotient Rules (slides)Matthew Leingang
The product rule is generally better because:
- It is more systematic and avoids mistakes from expanding products
- It works for any differentiable functions u and v, not just polynomials
- It provides insight into the structure of the derivative that direct computation does not
So in this example, using the product rule is preferable to direct multiplication.
CVPR2010: higher order models in computer vision: Part 1, 2zukun
This document discusses tractable higher order models in computer vision using random field models. It introduces Markov random fields (MRFs) and factor graphs as graphical models for computer vision problems. Higher order models that include factors over cliques of more than two variables can model problems more accurately but are generally intractable. The document discusses various inference techniques for higher order models such as relaxation, message passing, and decomposition methods. It provides examples of how higher order and global models can be used in problems like segmentation, stereo matching, reconstruction, and denoising.
The document discusses properties and applications of the Z-transform, which is used to analyze linear discrete-time signals. Some key points:
1) The Z-transform plays an important role in analyzing discrete-time signals and is defined as the sum of the signal samples multiplied by a complex variable z raised to the power of the sample's time index.
2) Important properties of the Z-transform include linearity, time-shifting, frequency-shifting, differentiation in the Z-domain, and the convolution theorem.
3) The Z-transform can be used to find the transform of basic sequences like the unit impulse, unit step, exponentials, polynomials, and derivatives of signals.
The document discusses using the Fast Fourier Transform (FFT) algorithm to multiply polynomials in faster than quadratic time. It explains that the FFT represents polynomials in a point-value representation using complex roots of unity, which allows multiplication to be performed pointwise in linear time. The FFT algorithm recursively decomposes the polynomial multiplication problem into smaller subproblems of half the size, using divide and conquer, to compute the discrete Fourier transform in O(n log n) time rather than the naive O(n^2) time. Interpolation can also be performed in similar time to convert back from the point-value representation to coefficients. Overall the FFT provides a faster algorithm for polynomial multiplication and convolution.
Optimization is a killer feature of the derivative. Not only do we often want to optimize some system, nature does as well. We give a procedure and many examples.
An antiderivative of a function is a function whose derivative is the given function. The problem of antidifferentiation is interesting, complicated, and useful, especially when discussing motion.
This document contains lecture notes on the Fundamental Theorem of Calculus. It begins with announcements about the final exam date and current grade distribution. The outline then reviews the Evaluation Theorem and introduces the First and Second Fundamental Theorems of Calculus. It provides examples of how the integral can represent total change in concepts like distance, cost, and mass. Biographies are also included of mathematicians like Gregory, Barrow, Newton, and Leibniz who contributed to the development of calculus.
The document is a lecture on derivatives and the shapes of curves. It covers the mean value theorem, testing for monotonicity using the first derivative test, and finding intervals of monotonicity. It also discusses concavity and the second derivative test. Examples are provided to demonstrate how to find the intervals where a function is increasing or decreasing using the first derivative test.
This document is the outline for a calculus class. It discusses the final exam date and review sessions, and outlines the topics of substitution for indefinite integrals and substitution for definite integrals. It provides an example of using substitution to find the integral of the square root of x^2 + 1 by letting u = x^2 + 1, and expresses this using both standard notation and Leibniz notation. It states the theorem of substitution rule.
Lesson 25: Areas and Distances; The Definite IntegralMatthew Leingang
Thus begins the second "half" of calculus—in which we attempt to find areas of curved regions. Like with derivatives, we use a limiting process starting from things we know (areas of rectangles) and finer and finer approximations.
The document provides steps for graphing the cubic function f(x) = 2x^3 - 3x^2 - 12x. Step 1 analyzes the monotonicity of the function by examining the sign chart of its derivative f'(x). Step 2 analyzes concavity by examining the sign chart of the second derivative f''(x). Step 3 combines these analyses into a single sign chart summarizing the function's properties over its domain. The goal is to completely graph the function, indicating zeros, asymptotes, critical points, maxima/minima, and inflection points.
The Mean Value Theorem is the most important theorem in calculus! It allows us to infer information about a function from information about its derivative. Such as: a function whose derivative is zero must be a constant function.
The closed interval method tells us how to find the extreme values of a continuous function defined on a closed, bounded interval: we check the end points and the critical points.
Docutils is my method for producing web pages in multiple formats, notable html and pdf. These are slides I gave for a 15-minute talk at the Joint Mathematics meetings in San Francisco in January 2009.
The document provides an overview of functions including definitions, examples, and properties. It defines a function as a relation that assigns each element in the domain to a single element in the range. Examples of functions expressed by formulas, numerically, graphically, and verbally are given. Properties like monotonicity, symmetry, evenness, and oddness are defined and illustrated with examples. The document aims to introduce the fundamental concepts of functions to readers.
We discuss the ideas of monotonicity (increasing or decreasing) and concavity (up or down) of a function. Because of the Mean Value Theorem, we can determine these characteristics using derivatives.
The document discusses evaluating definite integrals. It begins by reviewing the definition of the definite integral as a limit. It then discusses estimating integrals using the midpoint rule and properties of integrals such as integrals of nonnegative functions being nonnegative and integrals being "increasing" if one function is greater than another. An example is worked out using the midpoint rule to estimate an integral. The document provides an outline of topics and notation for integrals.
An antiderivative of a function is a function whose derivative is the given function. The problem of antidifferentiation is interesting, complicated, and useful, especially when discussing motion.
The Mean Value Theorem is the most important theorem in calculus! It allows us to infer information about a function from information about its derivative. Such as: a function whose derivative is zero must be a constant function.
The document discusses the concept of a limit in calculus. It provides an outline that covers heuristics, errors and tolerances, examples, pathologies, and a precise definition of a limit. It introduces the error-tolerance game used to determine if a limit exists and illustrates this through examples evaluating the limits of x^2 as x approaches 0 and |x|/x as x approaches 0. It also discusses one-sided limits and evaluates the limit of 1/x as x approaches 0 from the right.
Optimization is a killer feature of the derivative. Not only do we often want to optimize some system, nature does as well. We give a procedure and many examples.
Lesson 25: Areas and Distances; The Definite IntegralMatthew Leingang
Thus begins the second "half" of calculus—in which we attempt to find areas of curved regions. Like with derivatives, we use a limiting process starting from things we know (areas of rectangles) and finer and finer approximations.
The document is a summary of lecture notes for a Calculus I class. It discusses integration by substitution, providing theory, examples, and objectives. Key points covered include the substitution rule for indefinite integrals, working through examples like finding the integral of √x2+1 dx, and noting substitution can transform integrals into simpler forms. Definite integrals using substitution are also briefly mentioned.
Lesson 27: Integration by Substitution (Section 041 slides)Matthew Leingang
The document contains notes from a Calculus I class at New York University on December 13, 2010. It discusses using the substitution method for indefinite and definite integrals. Examples are provided to demonstrate how to use substitutions to evaluate integrals involving trigonometric, exponential, and polynomial functions. The key steps are to make a substitution for the variable in terms of a new variable, determine the differential of the substitution, and substitute into the integral to transform it into an integral involving only the new variable.
Lesson 27: Integration by Substitution (Section 041 slides)Mel Anthony Pepito
The document provides notes from a Calculus I class at New York University on December 13, 2010. It includes announcements about upcoming review sessions and the final exam. It then outlines the objectives and topics to be covered, which is integration by substitution. Examples are provided to demonstrate how to use u-substitution to evaluate indefinite integrals. The key steps are to let u equal an expression involving x, find du in terms of dx, and then substitute into the integral.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
The document discusses the Fundamental Theorem of Calculus, which has two parts. The first part states that if a function f is continuous on an interval, then the derivative of the integral of f is equal to f. This is proven using Riemann sums. The second part relates the integral of a function f to the integral of its derivative F'. Examples are provided to illustrate how the area under a curve relates to these concepts.
Lesson 26: The Fundamental Theorem of Calculus (slides)Mel Anthony Pepito
The document discusses the Fundamental Theorem of Calculus, which has two parts. The first part states that if a function f is continuous, then the derivative of the integral of f is equal to f. This is proven using Riemann sums. The second part relates the integral of a function f to the anti-derivative F of f. Examples are provided to illustrate how to use the Fundamental Theorem to find derivatives and integrals.
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
g(x) represents the area under the curve of f(t) between 0 and x.
.
x
What can you say about g? 2 4 6 8 10f
The First Fundamental Theorem of Calculus
Theorem (First Fundamental Theorem of Calculus)
Let f be a con nuous func on on [a, b]. Define the func on F on [a, b] by
∫ x
F(x) = f(t) dt
a
Then F is con nuous on [a, b] and differentiable on (a, b) and for all x in (a, b),
F′(x
The document provides examples of derivatives and their corresponding anti-derivatives (indefinite integrals) for various functions. It also demonstrates rules for taking the anti-derivative of functions using u-substitution. Some key rules covered include adding +c to account for constants and applying power rules for integrals involving terms like 4x, 3x^2, or other polynomial functions. Examples are worked through step-by-step to illustrate properly applying u-substitution and integrating more complex expressions.
1) The document discusses integration as the reverse process of differentiation, and provides examples of integrating logarithmic and exponential functions.
2) Key points covered include integrating 1/x as ln(x), and exponential functions like e3x by using u-substitution and dividing the "tail" by the exponent.
3) The document also demonstrates integrating logarithmic expressions using u-substitution, letting the term in the denominator equal u and finding du.
This document discusses differentiating functions involving logarithmic and exponential terms. It covers:
1) The derivatives of ln(x) and e^x as 1/x and e^x, respectively.
2) Using product, quotient, and chain rules to differentiate functions containing ln(x) or e^x, including examples of differentiating expressions with both ln(x) and e^x terms.
3) A caution about confusing the power rule and exponential functions when taking derivatives.
The document provides examples of differentiating various logarithmic and exponential expressions and encourages practicing differentiating expressions involving both ln(x) and e^x.
The document discusses various techniques for finding antiderivatives (indefinite integrals). It covers:
1) Using the power rule to find antiderivatives by increasing the exponent by 1 and dividing by the new exponent.
2) Rewriting expressions with rational or negative exponents before taking the antiderivative.
3) Expanding expressions, like using FOIL, before taking the antiderivative term by term.
4) Simplifying expressions, like factoring, before taking the antiderivative.
5) Setting up and solving word problems involving antiderivatives to find functions for quantities like profit, distance, and rate of change.
Lesson 26: The Fundamental Theorem of Calculus (Section 10 version)Matthew Leingang
The First Fundamental Theorem of Calculus looks at the area function and its derivative. It so happens that the derivative of the area function is the original integrand.
The document discusses various techniques for evaluating indefinite integrals (antiderivatives), including:
1) Using power rules to evaluate basic integrals like ∫ 4x3 dx = x4 + C
2) Expanding rational or negative exponents before integrating
3) Expanding expressions before integrating term by term
4) Simplifying rational expressions by factoring and canceling before integrating
5) Setting up word problems involving integrals to find related functions like total cost, revenue, distance over time.
This document discusses implicit differentiation, which is a technique for taking the derivative of equations that cannot be solved explicitly for y as a function of x. It explains that when differentiating terms involving both x and y, the derivative of the y term is dy/dx. As an example, it shows the differentiation of xy using the product rule, which yields y + x*dy/dx. The document concludes by applying this technique to differentiate the equation y4 + xy = x3 - x + 2 implicitly with respect to x.
Lesson 26: The Fundamental Theorem of Calculus (Section 4 version)Matthew Leingang
The First Fundamental Theorem of Calculus looks at the area function and its derivative. It so happens that the derivative of the area function is the original integrand.
This document discusses the indefinite integral and antiderivatives. It defines an antiderivative as a function whose derivative is the original function, and notes that there are infinitely many antiderivatives that differ by a constant. The process of finding antiderivatives is called indefinite integration or antidifferentiation. Initial conditions can be used to determine a unique particular solution by solving for the constant of integration.
This document discusses the indefinite integral and antiderivatives. It defines an antiderivative as a function whose derivative is the original function, and notes that there are infinitely many antiderivatives that differ by a constant. The process of finding antiderivatives is called indefinite integration or antidifferentiation. Initial conditions can be used to determine a unique particular solution by solving for the constant of integration.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Mel Anthony Pepito
This document provides an overview of the key points covered in a calculus lecture on derivatives of logarithmic and exponential functions:
1) It discusses the derivatives of exponential functions with any base, as well as the derivatives of logarithmic functions with any base.
2) It covers using the technique of logarithmic differentiation to find derivatives of functions involving products, quotients, and/or exponentials.
3) The document provides examples of finding derivatives of various logarithmic and exponential functions.
The document discusses the chain rule for derivatives. It begins by defining function composition and provides examples of composing linear functions. It then states the chain rule theorem, which says that the derivative of a composition is the product of the individual function derivatives evaluated at the same point. Several examples are worked out applying the chain rule to find the derivative of various compositions of functions.
Similar to Lesson 29: Integration by Substition (worksheet solutions) (20)
This document provides guidance on developing effective lesson plans for calculus instructors. It recommends starting by defining specific learning objectives and assessments. Examples should be chosen carefully to illustrate concepts and engage students at a variety of levels. The lesson plan should include an introductory problem, definitions, theorems, examples, and group work. Timing for each section should be estimated. After teaching, the lesson can be improved by analyzing what was effective and what needs adjustment for the next time. Advanced preparation is key to looking prepared and ensuring students learn.
Streamlining assessment, feedback, and archival with auto-multiple-choiceMatthew Leingang
Auto-multiple-choice (AMC) is an open-source optical mark recognition software package built with Perl, LaTeX, XML, and sqlite. I use it for all my in-class quizzes and exams. Unique papers are created for each student, fixed-response items are scored automatically, and free-response problems, after manual scoring, have marks recorded in the same process. In the first part of the talk I will discuss AMC’s many features and why I feel it’s ideal for a mathematics course. My contributions to the AMC workflow include some scripts designed to automate the process of returning scored papers
back to students electronically. AMC provides an email gateway, but I have written programs to return graded papers via the DAV protocol to student’s dropboxes on our (Sakai) learning management systems. I will also show how graded papers can be archived, with appropriate metadata tags, into an Evernote notebook.
This document discusses electronic grading of paper assessments using PDF forms. Key points include:
- Various tools for creating fillable PDF forms using LaTeX packages or desktop software.
- Methods for stamping completed forms onto scanned documents including using pdftk or overlaying in TikZ.
- Options for grading on tablets or desktops including GoodReader, PDFExpert, Adobe Acrobat.
- Extracting data from completed forms can be done in Adobe Acrobat or via command line with pdftk.
Lesson 27: Integration by Substitution (handout)Matthew Leingang
This document contains lecture notes on integration by substitution from a Calculus I class. It introduces the technique of substitution for both indefinite and definite integrals. For indefinite integrals, the substitution rule is presented, along with examples of using substitutions to evaluate integrals involving polynomials, trigonometric, exponential, and other functions. For definite integrals, the substitution rule is extended and examples are worked through both with and without first finding the indefinite integral. The document emphasizes that substitution often simplifies integrals and makes them easier to evaluate.
Lesson 26: The Fundamental Theorem of Calculus (handout)Matthew Leingang
1) The document discusses lecture notes on Section 5.4: The Fundamental Theorem of Calculus from a Calculus I course. 2) It covers stating and explaining the Fundamental Theorems of Calculus and using the first fundamental theorem to find derivatives of functions defined by integrals. 3) The lecture outlines the first fundamental theorem, which relates differentiation and integration, and gives examples of applying it.
This document contains notes from a calculus class lecture on evaluating definite integrals. It discusses using the evaluation theorem to evaluate definite integrals, writing derivatives as indefinite integrals, and interpreting definite integrals as the net change of a function over an interval. The document also contains examples of evaluating definite integrals, properties of integrals, and an outline of the key topics covered.
This document contains lecture notes from a Calculus I class covering Section 5.3 on evaluating definite integrals. The notes discuss using the Evaluation Theorem to calculate definite integrals, writing derivatives as indefinite integrals, and interpreting definite integrals as the net change of a function over an interval. Examples are provided to demonstrate evaluating definite integrals using the midpoint rule approximation. Properties of integrals such as additivity and the relationship between definite and indefinite integrals are also outlined.
Lesson 24: Areas and Distances, The Definite Integral (handout)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
Lesson 24: Areas and Distances, The Definite Integral (slides)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
This document contains lecture notes from a Calculus I class discussing optimization problems. It begins with announcements about upcoming exams and courses the professor is teaching. It then presents an example problem about finding the rectangle of a fixed perimeter with the maximum area. The solution uses calculus techniques like taking the derivative to find the critical points and determine that the optimal rectangle is a square. The notes discuss strategies for solving optimization problems and summarize the key steps to take.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few good examples.
The document discusses curve sketching of functions by analyzing their derivatives. It provides:
1) A checklist for graphing a function which involves finding where the function is positive/negative/zero, its monotonicity from the first derivative, and concavity from the second derivative.
2) An example of graphing the cubic function f(x) = 2x^3 - 3x^2 - 12x through analyzing its derivatives.
3) Explanations of the increasing/decreasing test and concavity test to determine monotonicity and concavity from a function's derivatives.
The document contains lecture notes on curve sketching from a Calculus I class. It discusses using the first and second derivative tests to determine properties of a function like monotonicity, concavity, maxima, minima, and points of inflection in order to sketch the graph of the function. It then provides an example of using these tests to sketch the graph of the cubic function f(x) = 2x^3 - 3x^2 - 12x.
Lesson 20: Derivatives and the Shapes of Curves (slides)Matthew Leingang
This document contains lecture notes on derivatives and the shapes of curves from a Calculus I class taught by Professor Matthew Leingang at New York University. The notes cover using derivatives to determine the intervals where a function is increasing or decreasing, classifying critical points as maxima or minima, using the second derivative to determine concavity, and applying the first and second derivative tests. Examples are provided to illustrate finding intervals of monotonicity for various functions.
Lesson 20: Derivatives and the Shapes of Curves (handout)Matthew Leingang
This document contains lecture notes on calculus from a Calculus I course. It covers determining the monotonicity of functions using the first derivative test. Key points include using the sign of the derivative to determine if a function is increasing or decreasing over an interval, and using the first derivative test to classify critical points as local maxima, minima, or neither. Examples are provided to demonstrate finding intervals of monotonicity for various functions and applying the first derivative test.
The Mean Value Theorem is the most important theorem in calculus. It is the first theorem which allows us to infer information about a function from information about its derivative. From the MVT we can derive tests for the monotonicity (increase or decrease) and concavity of a function.
There are various reasons why we would want to find the extreme (maximum and minimum values) of a function. Fermat's Theorem tells us we can find local extreme points by looking at critical points. This process is known as the Closed Interval Method.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Lesson 29: Integration by Substition (worksheet solutions)
1. Section 5.5
Integration by Substitution
V63.0121.034, Calculus I
December 9, 2009
Announcements
Final Exam: Friday 12/18, 2:00-3:50pm, Tisch UC50
Practice finals on the website. Solutions Friday
. . . . . .
2. Schedule for next week
Monday, class: review, evaluations, movie!
Tuesday, 8:00am: Review session for all students with 8:00
recitations (Tuesday or Thursday) in CIWW 109
Tuesday, 9:30am: Review session for all students with 9:30
recitations (Tuesday or Thursday) in CIWW 109
Office Hours continue
Friday, 2:00pm: final in Tisch UC50
. . . . . .
3. Resurrection Policy
If your final score beats your midterm score, we will add 10% to
its weight, and subtract 10% from the midterm weight.
.
.
Image credit: Scott Beale / Laughing Squid
. . . . . .
4. Outline
Last Time: The Fundamental Theorem(s) of Calculus
Substitution for Indefinite Integrals
Theory
Examples
Substitution for Definite Integrals
Theory
Examples
. . . . . .
5. Differentiation and Integration as reverse processes
Theorem (The Fundamental Theorem of Calculus)
1. Let f be continuous on [a, b]. Then
∫ x
d
f(t) dt = f(x)
dx a
2. Let f be continuous on [a, b] and f = F′ for some other
function F. Then
∫ b
f(x) dx = F(b) − F(a).
a
. . . . . .
6. Techniques of antidifferentiation?
So far we know only a few rules for antidifferentiation. Some are
general, like
∫ ∫ ∫
[f(x) + g(x)] dx = f(x) dx + g(x) dx
. . . . . .
7. Techniques of antidifferentiation?
So far we know only a few rules for antidifferentiation. Some are
general, like
∫ ∫ ∫
[f(x) + g(x)] dx = f(x) dx + g(x) dx
Some are pretty particular, like
∫
1
√ dx = arcsec x + C.
x x2 − 1
. . . . . .
8. Techniques of antidifferentiation?
So far we know only a few rules for antidifferentiation. Some are
general, like
∫ ∫ ∫
[f(x) + g(x)] dx = f(x) dx + g(x) dx
Some are pretty particular, like
∫
1
√ dx = arcsec x + C.
x x2 − 1
What are we supposed to do with that?
. . . . . .
10. So far we don’t have any way to find
∫
2x
√ dx
x2 + 1
or ∫
tan x dx.
Luckily, we can be smart and use the “anti” version of one of the
most important rules of differentiation: the chain rule.
. . . . . .
11. Outline
Last Time: The Fundamental Theorem(s) of Calculus
Substitution for Indefinite Integrals
Theory
Examples
Substitution for Definite Integrals
Theory
Examples
. . . . . .
13. Substitution for Indefinite Integrals
Example
Find ∫
x
√ dx.
x 2+1
Solution
Stare at this long enough and you notice the the integrand is the
√
derivative of the expression 1 + x2 .
. . . . . .
19. Leibnizian notation FTW
Solution (Same technique, new notation)
√ √
Let u = x2 + 1. Then du = 2x dx and 1 + x2 = u. So the
integrand becomes completely transformed into
∫ ∫ 1 ∫
√
x dx 2 du
√
1
√ du
= =
x2 + 1 u 2 u
. . . . . .
20. Leibnizian notation FTW
Solution (Same technique, new notation)
√ √
Let u = x2 + 1. Then du = 2x dx and 1 + x2 = u. So the
integrand becomes completely transformed into
∫ ∫ 1 ∫
√
x dx 2 du
√
1
√ du
= =
x2 + 1 ∫ u 2 u
1 −1/2
= 2u du
. . . . . .
21. Leibnizian notation FTW
Solution (Same technique, new notation)
√ √
Let u = x2 + 1. Then du = 2x dx and 1 + x2 = u. So the
integrand becomes completely transformed into
∫ ∫ 1 ∫
√
x dx 2 du
√
1
√ du
= =
x2 + 1 ∫ u 2 u
1 −1/2
= 2u du
√ √
= u+C= 1 + x2 + C.
. . . . . .
22. Useful but unsavory variation
Solution (Same technique, new notation, more idiot-proof)
√ √
Let u = x2 + 1. Then du = 2x dx and 1 + x2 = u. “Solve for
dx:”
du
dx =
2x
So the integrand becomes completely transformed into
∫ ∫ ∫
x x du 1
√ dx = √ · = √ du
x2 + 1 u 2x 2 u
∫
1 −1/2
= 2u du
√ √
= u + C = 1 + x2 + C .
Mathematicians have serious issues with mixing the x and u like
this. However, I can’t deny that it works.
. . . . . .
23. Theorem of the Day
Theorem (The Substitution Rule)
If u = g(x) is a differentiable function whose range is an interval I
and f is continuous on I, then
∫ ∫
f(g(x))g′ (x) dx = f(u) du
That is, if F is an antiderivative for f, then
∫
f(g(x))g′ (x) dx = F(g(x))
In Leibniz notation:
∫ ∫
du
f(u) dx = f(u) du
dx
. . . . . .
24. A polynomial example
Example ∫
Use the substitution u = x2 + 3 to find (x2 + 3)3 4x dx.
. . . . . .
25. A polynomial example
Example ∫
Use the substitution u = x2 + 3 to find (x2 + 3)3 4x dx.
Solution
If u = x2 + 3, then du = 2x dx, and 4x dx = 2 du. So
∫ ∫ ∫
(x + 3) 4x dx = u 2du = 2 u3 du
2 3 3
1 4 1 2
= u = (x + 3)4
2 2
. . . . . .
26. A polynomial example, by brute force
Compare this to multiplying it out:
∫ ∫
2 3
( 6 )
(x + 3) 4x dx = x + 9x4 + 27x2 + 27 4x dx
∫
( 7 )
= 4x + 36x5 + 108x3 + 108x dx
1 8
= x + 6x6 + 27x4 + 54x2
2
. . . . . .
27. A polynomial example, by brute force
Compare this to multiplying it out:
∫ ∫
2 3
( 6 )
(x + 3) 4x dx = x + 9x4 + 27x2 + 27 4x dx
∫
( 7 )
= 4x + 36x5 + 108x3 + 108x dx
1 8
= x + 6x6 + 27x4 + 54x2
2
Which would you rather do?
. . . . . .
28. A polynomial example, by brute force
Compare this to multiplying it out:
∫ ∫
2 3
( 6 )
(x + 3) 4x dx = x + 9x4 + 27x2 + 27 4x dx
∫
( 7 )
= 4x + 36x5 + 108x3 + 108x dx
1 8
= x + 6x6 + 27x4 + 54x2
2
Which would you rather do?
It’s a wash for low powers
But for higher powers, it’s much easier to do substitution.
. . . . . .
29. Compare
We have the substitution method, which, when multiplied out,
gives
∫
1
(x2 + 3)3 4x dx = (x2 + 3)4
2
1( 8 )
= x + 12x6 + 54x4 + 108x2 + 81
2
1 81
= x8 + 6x6 + 27x4 + 54x2 +
2 2
and the brute force method
∫
1
(x2 + 3)3 4x dx = x8 + 6x6 + 27x4 + 54x2
2
Is this a problem?
. . . . . .
30. Compare
We have the substitution method, which, when multiplied out,
gives
∫
1
(x2 + 3)3 4x dx = (x2 + 3)4 + C
2
1( 8 )
= x + 12x6 + 54x4 + 108x2 + 81 + C
2
1 81
= x8 + 6x6 + 27x4 + 54x2 + +C
2 2
and the brute force method
∫
1
(x2 + 3)3 4x dx = x8 + 6x6 + 27x4 + 54x2 + C
2
Is this a problem? No, that’s what +C means!
. . . . . .
32. A slick example
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
. . . . . .
33. A slick example
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
Let u = cos x. Then du = − sin x dx.
. . . . . .
34. A slick example
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
Let u = cos x. Then du = − sin x dx. So
∫ ∫ ∫
sin x 1
tan x dx = dx = − du
cos x u
. . . . . .
35. A slick example
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
Let u = cos x. Then du = − sin x dx. So
∫ ∫ ∫
sin x 1
tan x dx = dx = − du
cos x u
= − ln |u| + C
. . . . . .
36. A slick example
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
Let u = cos x. Then du = − sin x dx. So
∫ ∫ ∫
sin x 1
tan x dx = dx = − du
cos x u
= − ln |u| + C
= − ln | cos x| + C = ln | sec x| + C
. . . . . .
38. Can you do it another way?
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
du
Let u = sin x. Then du = cos x dx and so dx = .
cos x
. . . . . .
39. Can you do it another way?
Example
∫
sin x
Find tan x dx. (Hint: tan x = )
cos x
Solution
du
Let u = sin x. Then du = cos x dx and so dx = .
cos x
∫ ∫ ∫
sin x u du
tan x dx = dx =
cos x cos x cos x
∫ ∫ ∫
u du u du u du
= = 2
=
cos2 x 1 − sin x 1 − u2
At this point, although it’s possible to proceed, we should
probably back up and see if the other way works quicker (it
does).
. . . . . .
40. For those who really must know all
Solution (Continued, with algebra help)
∫ ∫ ∫ ( )
u du 1 1 1
tan x dx = = − du
1 − u2 2 1−u 1+u
1 1
= − ln |1 − u| − ln |1 + u| + C
2 2
1 1
= ln √ + C = ln √ +C
(1 − u)(1 + u) 1 − u2
1
= ln + C = ln |sec x| + C
|cos x|
. . . . . .
41. Outline
Last Time: The Fundamental Theorem(s) of Calculus
Substitution for Indefinite Integrals
Theory
Examples
Substitution for Definite Integrals
Theory
Examples
. . . . . .
43. Theorem (The Substitution Rule for Definite Integrals)
If g′ is continuous and f is continuous on the range of u = g(x),
then ∫ ∫
b g(b)
f(g(x))g′ (x) dx = f(u) du.
a g (a )
Why the change in the limits?
The integral on the left happens in “x-land”
The integral on the right happens in “u-land”, so the limits
need to be u-values
To get from x to u, apply g
. . . . . .
44. Example ∫
π
Compute cos2 x sin x dx.
0
. . . . . .
45. Example ∫
π
Compute cos2 x sin x dx.
0
Solution (Slow Way) ∫
First compute the indefinite integral cos2 x sin x dx and then
evaluate.
. . . . . .
46. Example ∫
π
Compute cos2 x sin x dx.
0
Solution (Slow Way) ∫
First compute the indefinite integral cos2 x sin x dx and then
evaluate. Let u = cos x. Then du = − sin x dx and
∫ ∫
cos x sin x dx = − u2 du
2
= − 1 u3 + C = − 1 cos3 x + C.
3 3
Therefore
∫ π
1 π
1( ) 2
cos2 x sin x dx = − cos3 x =− (−1)3 − 13 = .
0 3 0 3 3
. . . . . .
50. Solution (Fast Way)
Do both the substitution and the evaluation at the same time. Let
u = cos x. Then du = − sin x dx, u(0) = 1 and u(π) = −1. So
∫ π ∫ −1
cos2 x sin x dx = −u2 du
0 1
∫ 1
= u2 du
−1
1
1 1( ) 2
= u3 = 1 − (−1) =
3 −1 3 3
The advantage to the “fast way” is that you completely
transform the integral into something simpler and don’t have
to go back to the original x variable.
But the slow way is just as reliable.
. . . . . .
57. Another way to skin that cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
Let u = e2x + 1, so that du = 2e2x dx.
. . . . . .
58. Another way to skin that cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
Let u = e2x + 1, so that du = 2e2x dx. Then
∫ √ ∫
ln 8 √ 1 9√
2x
√ e e2x + 1 dx = u du
ln 3 2 4
. . . . . .
59. Another way to skin that cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
Let u = e2x + 1, so that du = 2e2x dx. Then
∫ √ ∫
ln 8 √ 1 9√
2x
√ e e2x + 1 dx = u du
ln 3 2 4
9
1 3/2
= u
3 4
. . . . . .
60. Another way to skin that cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
Let u = e2x + 1, so that du = 2e2x dx. Then
∫ √ ∫
ln 8 √ 1 9√
2x
√ e e2x + 1 dx = u du
ln 3 2 4
1 3/2 9
= u
3 4
1 19
= (27 − 8) =
3 3
. . . . . .
61. A third skinned cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
√
Let u = e2x + 1, so that
u2 = e2x + 1
. . . . . .
62. A third skinned cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
√
Let u = e2x + 1, so that
u2 = e2x + 1 =⇒ 2u du = 2e2x dx
. . . . . .
63. A third skinned cat
Example√
∫ ln 8 √
Find √ e2x e2x + 1 dx
ln 3
Solution
√
Let u = e2x + 1, so that
u2 = e2x + 1 =⇒ 2u du = 2e2x dx
Thus ∫ √ ∫
ln 8 3 3
1 3 19
√ = u · u du = u =
ln 3 2 3 2 3
. . . . . .
65. Example
Find ∫ ( ) ( )
3π/2
5 θ 2 θ
cot sec dθ.
π 6 6
Before we dive in, think about:
What “easy” substitutions might help?
Which of the trig functions suggests a substitution?
. . . . . .
70. Graphs
∫ 3π/2 ( ) ( ) ∫ π/4
θ 2 θ
. 5
cot sec dθ . 6 cot5 φ sec2 φ dφ
π 6 6 π/6
y
. y
.
. . . .
θ . . .
φ
.
π 3π ππ
. . .
2 64
The areas of these two regions are the same. . . . . . .
71. Graphs
∫ π/4 ∫ 1
. 6 cot5 φ sec2 φ dφ . √ 6u−5 du
π/6 1/ 3
y
. y
.
. . . .
φ . ..
u
ππ 1 .1
. . .√
64 3 The areas
of these two regions are the same.
. . . . . .
72. Final Thoughts
Antidifferentiation is a “nonlinear” problem that needs
practice, intuition, and perserverance
Worksheet in recitation (also to be posted)
The whole antidifferentiation story is in Chapter 6
. . . . . .