Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
This document discusses implicit differentiation and finding the slope of tangent lines using implicit differentiation. It begins with an example problem of finding the slope of the tangent line to the curve x^2 + y^2 = 1 at the point (3/5, -4/5). It then explains how to set up and solve the implicit differentiation problem to find the slope. The document emphasizes that even when a relation is not explicitly a function, it can often be treated as locally functional to apply implicit differentiation and find tangent slopes. It provides another example problem and discusses horizontal and vertical tangent lines.
Implicit differentiation allows us to find slopes of lines tangent to curves that are not graphs of functions. Almost all of the time (yes, that is a mathematical term!) we can assume the curve comprises the graph of a function and differentiate using the chain rule.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
The document is about implicit differentiation and contains the following:
- It introduces implicit differentiation using the example of finding the slope of the curve x^2 + y^2 = 1 at the point (3/5, -4/5).
- It shows solving this problem explicitly by isolating y and taking the derivative, as well as implicitly by treating y as a function f(x) and differentiating the equation x^2 + f(x)^2 = 1.
- The objectives, outline, and motivation for implicit differentiation are provided to set up the key concepts covered in the section.
1. A complex number λ is an eigenvalue of a matrix A if there exists a non-zero vector x such that Ax = λx.
2. If a matrix has complex eigenvalues, it provides important information about the matrix, such as in problems involving vibrations and rotations in space.
3. For a complex eigenvalue λ = a + bi, a is called the real part and b is called the imaginary part. The absolute value |λ| represents the "length" or magnitude of the eigenvalue.
The document discusses solving equations involving squares and square roots. It provides examples of solving equations with squares and square roots, including solving for unknown variables and checking solutions. Key steps shown include taking the square root of both sides of an equation, squaring terms, and isolating the variable. Applications mentioned include physics, engineering, and mechanics.
This document discusses the history and proofs of Newton's binomial formula. It begins by introducing Newton's binomial formula and defining the binomial coefficients. It then provides three proofs of the formula: an induction proof, a combinatorial proof, and a proof using calculus. The document also discusses the mathematicians John Wallis and Isaac Newton, who helped develop and prove the formula. It explores Wallis's work leading up to Newton's further developments. In summary, the document outlines Newton's binomial formula and provides several mathematical proofs of the formula, while also discussing its historical origins and key contributors.
The document discusses using a run test to determine if a sequence is random or not. A run is defined as a sequence of identical symbols or events. The number of runs in a random sequence will follow a normal distribution. For the given sequence of acceptable and damaged glass sculptures, the number of runs is calculated and compared to the expected number of runs for a random sequence to test the randomness of damage at the 0.05 significance level. The number of runs observed is found to not be significantly different than expected, so the damage is determined to occur randomly.
The document defines and discusses differential equations and their solutions. It begins by classifying differential equations as ordinary or partial based on whether they involve one or more independent variables. Ordinary differential equations are then classified as linear or nonlinear based on their form. The order and degree of a differential equation are also defined.
Solutions to differential equations can be either explicit functions that directly satisfy the equation or implicit relations that define functions satisfying the equation. Picard's theorem guarantees a unique solution through each point for first-order equations. The general solution to a first-order equation is a one-parameter family of curves, with a particular solution corresponding to a specific value of the parameter. An initial value problem specifies both a differential equation and
This document summarizes selection algorithms and the 1-center problem.
The selection algorithm uses a prune and search approach. It recursively partitions the dataset into subsets based on the median, pruning away elements that are guaranteed to be outside the desired rank. This results in a linear time complexity of O(n).
The 1-center problem finds the smallest circle enclosing a set of points. A constrained version restricts the center to a given line. The algorithm works by forming point pairs, computing bisectors, and recursively pruning points outside the optimal region.
By tracking the sign of distances to farthest points, the full 2D solution can also be obtained in linear time by recursively considering constrained subproblems on the x
This document is from a Calculus I class at New York University. It provides an overview of functions, including the definition of a function, different ways functions can be represented (formulas, tables, graphs, verbal descriptions), properties of functions like monotonicity and symmetry, and examples of determining domains and ranges of functions. It aims to help students understand functions and their representations as a foundation for calculus.
The document discusses representing complex numbers geometrically using vectors on an Argand diagram. It explains that complex numbers can be represented as vectors, with addition of complex numbers corresponding to placing the vectors head to tail and subtraction corresponding to head to head placement. It also discusses properties like the triangle inequality for complex number operations and representing multiplication as scaling of the vector.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
This document discusses ordinary differential equations (ODEs). It defines ODEs and differentiates them from partial differential equations. ODEs can be classified by type, order, and linearity. Initial value problems involve solving an ODE with initial conditions specified at a point, while boundary value problems involve conditions at boundary points. The document provides examples of solving first- and second-order initial value problems. It also discusses the existence and uniqueness of solutions to initial value problems under certain continuity conditions on the functions defining the ODE.
Matematika ekonomi slide_optimasi_dengan_batasan_persamaanUfik Tweentyfour
The document discusses optimization problems with equality constraints. It explains that constrained optimization is central to economics due to scarcity. Lagrange multipliers allow constrained optimization problems to be solved using first-order conditions. The envelope theorem describes how the optimal value of an objective function changes with parameters. Applications discussed include consumer utility maximization with a budget constraint and deriving Marshallian and Hicksian demand curves.
This document discusses differential equations. It begins by explaining that differential equations are used to model many physical phenomena in areas like economics, engineering, and more. It then provides examples of ordinary and partial differential equations. The rest of the document defines key terms related to differential equations like order, degree, families of curves, and how to derive the differential equation of a family of curves by eliminating parameters. Several examples are provided to illustrate these concepts.
The document discusses the geometric representation of complex numbers using vectors on the Argand diagram. It explains that complex numbers can be represented as vectors, with the real part of the number as the x-coordinate and imaginary part as the y-coordinate of the vector. Addition and subtraction of complex numbers corresponds to placing the vectors head to tail and head to head, respectively. Properties like the parallelogram law and triangle inequality are demonstrated. Multiplication of complex numbers is shown to be equivalent to multiplying the magnitudes and adding the arguments of the vectors.
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
X2 T01 09 geometrical representation of complex numbersNigel Simmons
The document discusses representing complex numbers geometrically using vectors on the Argand diagram. It explains that complex numbers can be represented as vectors, with the advantage that vectors can be moved around without changing their modulus (length) or argument (angle from the x-axis). It describes how to perform addition and subtraction of complex numbers by placing the vectors head to tail or head to head, respectively, and discusses properties like the parallelogram law.
This document contains a final exam with 7 multiple part questions testing concepts in statistics, probability, and signal processing. The exam allows standard calculators and requires showing all work. Questions cover topics like distributions, confidence intervals, Monte Carlo integration, random processes, and sampling. Students are advised that reasonable assumptions can be made if they have difficulties with a part of a question. The exam is worth a total of 80 marks plus 4 bonus marks.
1. The document discusses transformation of random variables, where a function g is applied to a random variable X to produce another random variable Y=g(X). It provides methods to find the density or distribution function of Y based on the density of X.
2. It examines two examples that use the distribution function method and density function method to find the density of Y when X has a standard normal distribution and Y is a transformation of X.
3. It introduces the Jacobian technique to generalize the density function method to problems with multiple inputs and outputs. The Jacobian allows transforming joint densities between different variable spaces using a determinant.
The document provides information about integration, which is the reverse process of differentiation. It discusses indefinite integrals, preparing expressions for integration, using integration to solve differential equations, and evaluating definite integrals between limits. Some key points covered include:
- Indefinite integrals find antiderivatives with a constant term added.
- Definite integrals evaluate the area under a curve between two limits.
- Integrals can be used to find general and particular solutions to differential equations.
- Terms in an expression must be multiplied out and fractions converted before integrating.
To summarize:
1) To find the derivative of an implicit function y=y(x) defined by an equation F(x,y)=0, take the derivative of both sides with respect to x.
2) This will give a new equation involving x, y, and dy/dx that can be solved for dy/dx.
3) The examples show applying this process to find derivatives and tangent lines for various implicit equations.
3.2 implicit equations and implicit differentiationmath265
The document discusses implicit equations and implicit differentiation. It begins by explaining the difference between explicit and implicit forms of equations, using the example of y=1/x which can be written explicitly as y=1/x or implicitly as xy=1. It then introduces the concept of implicit differentiation, which involves taking the derivative of an implicit equation with respect to x and solving for the derivative of y with respect to x (y’). This allows one to find the slope of the curve at a point, even if the explicit form of the relation between x and y is difficult to determine from the implicit equation.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
Higher order derivatives for N -body simulationsKeigo Nitadori
This document discusses higher order derivatives that are useful for N-body simulations. It presents formulas for calculating higher order derivatives of power functions like y=xn, and applies this to derivatives of gravitational force f=mr-3. Specifically:
1) It derives recursive formulas for calculating higher order derivatives of power functions y=xn in terms of previous derivatives.
2) It applies these formulas to calculate derivatives of the gravitational force f=mr-3 in terms of derivatives of r and q=r-3/2.
3) It also describes an alternative approach by Le Guyader (1993) for calculating derivatives of r and q in terms of dot products of r with itself.
This document provides information about derivatives and their applications:
1. It defines the derivative as the limit of the difference quotient, and explains how to calculate derivatives using first principles. It also covers rules for finding derivatives of sums, products, quotients, exponentials, and logarithmic functions.
2. Higher order derivatives are introduced, with examples of how to take second and third derivatives.
3. Applications of derivatives like finding velocity and acceleration from a position-time function are demonstrated. Maximum/minimum values and how to find local and absolute extrema are also discussed with an example.
ANURAG TYAGI CLASSES (ATC) is an organisation destined to orient students into correct path to achieve
success in IIT-JEE, AIEEE, PMT, CBSE & ICSE board classes. The organisation is run by a competitive staff comprising of Ex-IITians. Our goal at ATC is to create an environment that inspires students to recognise and explore their own potentials and build up confidence in themselves.ATC was founded by Mr. ANURAG TYAGI on 19 march, 2001.
MEET US AT:
www.anuragtyagiclasses.com
The document discusses using a run test to determine if a sequence is random or not. A run is defined as a sequence of identical symbols or events. The number of runs in a random sequence will follow a normal distribution. For the given sequence of acceptable and damaged glass sculptures, the number of runs is calculated and compared to the expected number of runs for a random sequence to test the randomness of damage at the 0.05 significance level. The number of runs observed is found to not be significantly different than expected, so the damage is determined to occur randomly.
The document defines and discusses differential equations and their solutions. It begins by classifying differential equations as ordinary or partial based on whether they involve one or more independent variables. Ordinary differential equations are then classified as linear or nonlinear based on their form. The order and degree of a differential equation are also defined.
Solutions to differential equations can be either explicit functions that directly satisfy the equation or implicit relations that define functions satisfying the equation. Picard's theorem guarantees a unique solution through each point for first-order equations. The general solution to a first-order equation is a one-parameter family of curves, with a particular solution corresponding to a specific value of the parameter. An initial value problem specifies both a differential equation and
This document summarizes selection algorithms and the 1-center problem.
The selection algorithm uses a prune and search approach. It recursively partitions the dataset into subsets based on the median, pruning away elements that are guaranteed to be outside the desired rank. This results in a linear time complexity of O(n).
The 1-center problem finds the smallest circle enclosing a set of points. A constrained version restricts the center to a given line. The algorithm works by forming point pairs, computing bisectors, and recursively pruning points outside the optimal region.
By tracking the sign of distances to farthest points, the full 2D solution can also be obtained in linear time by recursively considering constrained subproblems on the x
This document is from a Calculus I class at New York University. It provides an overview of functions, including the definition of a function, different ways functions can be represented (formulas, tables, graphs, verbal descriptions), properties of functions like monotonicity and symmetry, and examples of determining domains and ranges of functions. It aims to help students understand functions and their representations as a foundation for calculus.
The document discusses representing complex numbers geometrically using vectors on an Argand diagram. It explains that complex numbers can be represented as vectors, with addition of complex numbers corresponding to placing the vectors head to tail and subtraction corresponding to head to head placement. It also discusses properties like the triangle inequality for complex number operations and representing multiplication as scaling of the vector.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
This document discusses ordinary differential equations (ODEs). It defines ODEs and differentiates them from partial differential equations. ODEs can be classified by type, order, and linearity. Initial value problems involve solving an ODE with initial conditions specified at a point, while boundary value problems involve conditions at boundary points. The document provides examples of solving first- and second-order initial value problems. It also discusses the existence and uniqueness of solutions to initial value problems under certain continuity conditions on the functions defining the ODE.
Matematika ekonomi slide_optimasi_dengan_batasan_persamaanUfik Tweentyfour
The document discusses optimization problems with equality constraints. It explains that constrained optimization is central to economics due to scarcity. Lagrange multipliers allow constrained optimization problems to be solved using first-order conditions. The envelope theorem describes how the optimal value of an objective function changes with parameters. Applications discussed include consumer utility maximization with a budget constraint and deriving Marshallian and Hicksian demand curves.
This document discusses differential equations. It begins by explaining that differential equations are used to model many physical phenomena in areas like economics, engineering, and more. It then provides examples of ordinary and partial differential equations. The rest of the document defines key terms related to differential equations like order, degree, families of curves, and how to derive the differential equation of a family of curves by eliminating parameters. Several examples are provided to illustrate these concepts.
The document discusses the geometric representation of complex numbers using vectors on the Argand diagram. It explains that complex numbers can be represented as vectors, with the real part of the number as the x-coordinate and imaginary part as the y-coordinate of the vector. Addition and subtraction of complex numbers corresponds to placing the vectors head to tail and head to head, respectively. Properties like the parallelogram law and triangle inequality are demonstrated. Multiplication of complex numbers is shown to be equivalent to multiplying the magnitudes and adding the arguments of the vectors.
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
X2 T01 09 geometrical representation of complex numbersNigel Simmons
The document discusses representing complex numbers geometrically using vectors on the Argand diagram. It explains that complex numbers can be represented as vectors, with the advantage that vectors can be moved around without changing their modulus (length) or argument (angle from the x-axis). It describes how to perform addition and subtraction of complex numbers by placing the vectors head to tail or head to head, respectively, and discusses properties like the parallelogram law.
This document contains a final exam with 7 multiple part questions testing concepts in statistics, probability, and signal processing. The exam allows standard calculators and requires showing all work. Questions cover topics like distributions, confidence intervals, Monte Carlo integration, random processes, and sampling. Students are advised that reasonable assumptions can be made if they have difficulties with a part of a question. The exam is worth a total of 80 marks plus 4 bonus marks.
1. The document discusses transformation of random variables, where a function g is applied to a random variable X to produce another random variable Y=g(X). It provides methods to find the density or distribution function of Y based on the density of X.
2. It examines two examples that use the distribution function method and density function method to find the density of Y when X has a standard normal distribution and Y is a transformation of X.
3. It introduces the Jacobian technique to generalize the density function method to problems with multiple inputs and outputs. The Jacobian allows transforming joint densities between different variable spaces using a determinant.
The document provides information about integration, which is the reverse process of differentiation. It discusses indefinite integrals, preparing expressions for integration, using integration to solve differential equations, and evaluating definite integrals between limits. Some key points covered include:
- Indefinite integrals find antiderivatives with a constant term added.
- Definite integrals evaluate the area under a curve between two limits.
- Integrals can be used to find general and particular solutions to differential equations.
- Terms in an expression must be multiplied out and fractions converted before integrating.
To summarize:
1) To find the derivative of an implicit function y=y(x) defined by an equation F(x,y)=0, take the derivative of both sides with respect to x.
2) This will give a new equation involving x, y, and dy/dx that can be solved for dy/dx.
3) The examples show applying this process to find derivatives and tangent lines for various implicit equations.
3.2 implicit equations and implicit differentiationmath265
The document discusses implicit equations and implicit differentiation. It begins by explaining the difference between explicit and implicit forms of equations, using the example of y=1/x which can be written explicitly as y=1/x or implicitly as xy=1. It then introduces the concept of implicit differentiation, which involves taking the derivative of an implicit equation with respect to x and solving for the derivative of y with respect to x (y’). This allows one to find the slope of the curve at a point, even if the explicit form of the relation between x and y is difficult to determine from the implicit equation.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
Higher order derivatives for N -body simulationsKeigo Nitadori
This document discusses higher order derivatives that are useful for N-body simulations. It presents formulas for calculating higher order derivatives of power functions like y=xn, and applies this to derivatives of gravitational force f=mr-3. Specifically:
1) It derives recursive formulas for calculating higher order derivatives of power functions y=xn in terms of previous derivatives.
2) It applies these formulas to calculate derivatives of the gravitational force f=mr-3 in terms of derivatives of r and q=r-3/2.
3) It also describes an alternative approach by Le Guyader (1993) for calculating derivatives of r and q in terms of dot products of r with itself.
This document provides information about derivatives and their applications:
1. It defines the derivative as the limit of the difference quotient, and explains how to calculate derivatives using first principles. It also covers rules for finding derivatives of sums, products, quotients, exponentials, and logarithmic functions.
2. Higher order derivatives are introduced, with examples of how to take second and third derivatives.
3. Applications of derivatives like finding velocity and acceleration from a position-time function are demonstrated. Maximum/minimum values and how to find local and absolute extrema are also discussed with an example.
ANURAG TYAGI CLASSES (ATC) is an organisation destined to orient students into correct path to achieve
success in IIT-JEE, AIEEE, PMT, CBSE & ICSE board classes. The organisation is run by a competitive staff comprising of Ex-IITians. Our goal at ATC is to create an environment that inspires students to recognise and explore their own potentials and build up confidence in themselves.ATC was founded by Mr. ANURAG TYAGI on 19 march, 2001.
MEET US AT:
www.anuragtyagiclasses.com
The document explains the chain rule, which provides a method for finding the derivative of a composite function. The chain rule states that the derivative of a composite function f(g(x)) is equal to the derivative of the outside function f'(g(x)) multiplied by the derivative of the inside function g'(x). This allows the calculation of derivatives of more complex functions that cannot be solved using basic derivative rules. Several examples are provided to demonstrate how to use the chain rule to calculate derivatives of various composite functions.
This document is a lecture summary for Calculus I at New York University. It covers the objectives and outline for sections 2.1 and 2.2 on the derivative and rates of change. The summary uses examples and graphics to demonstrate how to find the slope of the tangent line to a curve at a given point, both graphically and numerically using the definition of the derivative.
The document discusses the chain rule, which is used to find the derivative of composite functions. It provides examples of applying the chain rule to functions of the form f(g(x)) by taking the derivative of the outside function with respect to the inside function, and multiplying by the derivative of the inside function with respect to x. The chain rule can be used repeatedly when a function is composed of multiple nested functions. Derivative formulas themselves incorporate the chain rule. The chain rule is essential for finding derivatives and is the most common mistake made by students on tests.
The document discusses higher order derivatives. It defines the nth derivative of a function f(x) as f(n)(x). The first example finds the first five derivatives of f(x)=2x^4 - x^3 - 2. The second example finds the first three derivatives of f(x)=-x^2/3. The third example finds the first four derivatives of f(x)=ln(x) and discusses how derivatives of rational functions become more complicated with higher orders. It also provides examples of finding derivatives of other functions like sin(x).
The document is a lesson on implicit differentiation and related concepts:
1) Implicit differentiation allows one to take the derivative of an implicitly defined relation between x and y, even if y is not explicitly defined as a function of x.
2) Examples are provided to demonstrate implicit differentiation, such as finding the slope of a tangent line to a curve.
3) The van der Waals equation is introduced to describe non-ideal gas properties, and implicit differentiation is used to find the isothermal compressibility of a van der Waals gas.
Higher order derivatives and partial derivatives are discussed. Higher order derivatives refer to taking successive derivatives of a function, such as the second derivative being the derivative of the first derivative. Partial derivatives refer to taking the derivative of a function of multiple variables with respect to one of the variables, holding the other variables constant. Examples are provided of finding higher order derivatives and partial derivatives of various functions.
This document discusses the chain rule for functions of multiple variables. It begins by reviewing the chain rule for single-variable functions, then extends it to functions of more variables. The chain rule is presented for cases where the dependent variable z is a function of intermediate variables x and y, which are themselves functions of independent variables s and t. General formulas are given using partial derivatives. Examples are worked out, such as finding the derivative of a function defined implicitly by an equation. Diagrams are used to illustrate the relationships between variables.
The document discusses the Fibonacci sequence and how it relates to patterns found in nature, such as spirals in pinecones and sunflowers. It provides examples of the golden ratio in art and architecture, including the Parthenon and paintings like the Mona Lisa. The author questions why math, which can explain these natural patterns and ratios, should be considered "ugly" by some.
Lesson 11: Implicit Differentiation (Section 41 slides)Mel Anthony Pepito
This document provides an overview of implicit differentiation. It begins with a motivating example of finding the slope of the tangent line to the curve x^2 + y^2 = 1 at the point (3/5, -4/5). It is shown that while y is not explicitly defined as a function of x for this curve, it can be treated as such locally using implicit differentiation. The key steps are to take the derivative of the equation with respect to x, which introduces a term for dy/dx, and then solve for dy/dx. This reveals that implicit differentiation allows the derivative of implicitly defined functions to be found.
Implicit differentiation allows us to find slopes of lines tangent to curves that are not graphs of functions. Almost all of the time (yes, that is a mathematical term!) we can assume the curve comprises the graph of a function and differentiate using the chain rule.
Implicit differentiation allows us to find slopes of lines tangent to curves that are not graphs of functions. Almost all of the time (yes, that is a mathematical term!) we can assume the curve comprises the graph of a function and differentiate using the chain rule.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
1) The document discusses optical illusions and slope fields, which are used to represent solutions to differential equations graphically through a family of curves.
2) It provides examples of implicit differentiation of equations like x2 + y2 = C2 and xy = C to find the derivative dy/dx.
3) The St. Louis Gateway Arch is described as an optical illusion because its height and width appear different than their true measurements of 630 feet each, explained through a mathematical equation.
1) Implicit differentiation is a method for finding the slope of a curve when the equation is given implicitly rather than explicitly as y = f(x).
2) Examples are given of implicitly defined curves like circles, ellipses, and a 4-leaf clover curve.
3) The process of implicit differentiation takes the derivative of both sides of the implicit equation and solves for the derivative dy/dx.
Using implicit differentiation we can treat relations which are not quite functions like they were functions. In particular, we can find the slopes of lines tangent to curves which are not graphs of functions.
The document is notes for a lesson on tangent planes. It provides definitions of tangent lines and planes, formulas for finding equations of tangent lines and planes, and examples of applying these concepts. Specifically, it defines that the tangent plane to a function z=f(x,y) through the point (x0,y0,z0) has normal vector (f1(x0,y0), f2(x0,y0),-1) and equation f1(x0,y0)(x-x0) + f2(x0,y0)(y-y0) - (z-z0) = 0 or z = f(x0,y0) +
This document contains notes from a Calculus I class lecture on the derivative. The lecture covered the definition of the derivative and examples of how it can be used to model rates of change in various contexts like velocity, population growth, and marginal costs. It also discussed properties of the derivative like how the derivative of a function relates to whether the function is increasing or decreasing over an interval.
This document provides an outline and overview of essential functions and concepts covered in a Calculus I course. It includes sections on modeling, classes of functions (linear, polynomial, power, rational, trigonometric, exponential, logarithmic), transformations of functions, and vertical and horizontal shifts of functions. Examples are provided to illustrate key concepts like writing linear equations to model real-world situations, finding the equation of a parabola through three points, and how transformations affect a function graph.
The document outlines topics to be covered in Calculus I class sessions on the derivative and rates of change, including: defining the derivative at a point and using it to find the slope of the tangent line to a curve at that point; examples of derivatives modeling rates of change; and how to find the derivative function and second derivative of a given function. It provides learning objectives, an outline of topics, and an example problem worked out graphically and numerically to illustrate finding the slope of the tangent line.
The document outlines topics to be covered in Sections 2.1-2.2 of a Calculus I course, including defining the derivative, finding derivatives of functions, relating the graph of a function to its derivative, and finding tangent lines. It provides examples of how derivatives can model real-world rates of change and outlines the objectives to understand the definition of the derivative and how to apply it to functions.
This document contains lecture notes on optimization problems from a Calculus I class. It provides announcements about upcoming exams and room changes, then outlines examples of optimization problems involving addition with constraints, finding distances, and finding maximal areas of rectangles inscribed in triangles. It reviews methods for finding extrema like the closed interval method, first derivative test, and second derivative test. It then works through the examples in detail, finding critical points and classifying them to determine the optimal solutions.
This document contains notes from a calculus class lecture on optimization problems. It provides examples of optimization problems involving addition with a constraint, finding distance, and finding the maximum area rectangle that can be inscribed in a 3-4-5 right triangle. It also reviews concepts like the closed interval method, first derivative test, and second derivative test for finding extrema of functions.
This document summarizes a calculus lecture on linear approximations. It provides examples of using the tangent line to approximate the sine function at different points. Specifically, it estimates sin(61°) by taking linear approximations about 0 and about 60°. The linear approximation about 0 is x, giving a value of 1.06465. The linear approximation about 60° uses the fact that the sine is √3/2 and the derivative is √3/2 at π/3, giving a better approximation than using 0.
This document discusses using linear approximations to estimate functions. It provides an example estimating sin(61°) using linear approximations about a=0 and a=60°. When approximating about a=0, the estimate is 1.06465. When approximating about a=60°, the estimate is 0.87475, which is closer to the actual value of sin(61°) according to a calculator check. The document teaches that the tangent line provides the best linear approximation near a point, and its equation can be used to estimate function values.
Lesson 12: Linear Approximation and Differentials (Section 41 slides)Mel Anthony Pepito
The line tangent to a curve is also the line which best "fits" the curve near that point. So derivatives can be used for approximating complicated functions with simple linear ones. Differentials are another set of notation for the same problem.
Lesson 12: Linear Approximation and Differentials (Section 41 slides)Matthew Leingang
The line tangent to a curve is also the line which best "fits" the curve near that point. So derivatives can be used for approximating complicated functions with simple linear ones. Differentials are another set of notation for the same problem.
The document discusses techniques for finding the slope of a tangent line to a function at a given point using derivatives. It provides examples of finding the slope for various functions, including polynomials, trigonometric functions, and implicitly defined functions, using the definition of derivative and rules like the product rule. Approximations of slope using finite differences are also covered. Guidelines for performing implicit differentiation are outlined.
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.
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 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
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 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.
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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.
1. Section 2.6
Implicit Differentiation
V63.0121.021, Calculus I
New York University
October 12, 2010
Announcements
Quiz 2 in recitation this week. Covers §§1.5, 1.6, 2.1, 2.2
Midterm next week. Covers §§1.1–2.5
. . . . . .
2. Announcements
Quiz 2 in recitation this
week. Covers §§1.5, 1.6,
2.1, 2.2
Midterm next week.
Covers §§1.1–2.5
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 2 / 34
3. Objectives
Use implicit differentation
to find the derivative of a
function defined implicitly.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 3 / 34
4. Outline
The big idea, by example
Examples
Basic Examples
Vertical and Horizontal Tangents
Orthogonal Trajectories
Chemistry
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 4 / 34
5. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
at the point (3/5, −4/5).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
6. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
at the point (3/5, −4/5).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
7. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
.
at the point (3/5, −4/5).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
8. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
.
at the point (3/5, −4/5).
Solution (Explicit)
√
Isolate: y2 = 1 − x2 =⇒ y = − 1 − x2 . (Why the −?)
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
9. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
.
at the point (3/5, −4/5).
Solution (Explicit)
√
Isolate: y2 = 1 − x2 =⇒ y = − 1 − x2 . (Why the −?)
dy −2x x
Differentiate: =− √ =√
dx 2 1−x 2 1 − x2
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
10. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
.
at the point (3/5, −4/5).
Solution (Explicit)
√
Isolate: y2 = 1 − x2 =⇒ y = − 1 − x2 . (Why the −?)
dy −2x x
Differentiate: =− √ =√
dx 2 1−x 2 1 − x2
dy 3/5 3/5 3
Evaluate: =√ = = .
dx x=3/5 1 − (3/5)2 4/5 4
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
11. Motivating Example
y
.
Problem
Find the slope of the line
which is tangent to the
curve . x
.
x2 + y2 = 1
.
at the point (3/5, −4/5).
Solution (Explicit)
√
Isolate: y2 = 1 − x2 =⇒ y = − 1 − x2 . (Why the −?)
dy −2x x
Differentiate: =− √ =√
dx 2 1−x 2 1 − x2
dy 3/5 3/5 3
Evaluate: =√ = = .
dx x=3/5 1 − (3/5)2 4/5 4
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 5 / 34
12. Motivating Example, another way
We know that x2 + y2 = 1 does not define y as a function of x, but
suppose it did.
Suppose we had y = f(x), so that
x2 + (f(x))2 = 1
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 6 / 34
13. Motivating Example, another way
We know that x2 + y2 = 1 does not define y as a function of x, but
suppose it did.
Suppose we had y = f(x), so that
x2 + (f(x))2 = 1
We could differentiate this equation to get
2x + 2f(x) · f′ (x) = 0
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 6 / 34
14. Motivating Example, another way
We know that x2 + y2 = 1 does not define y as a function of x, but
suppose it did.
Suppose we had y = f(x), so that
x2 + (f(x))2 = 1
We could differentiate this equation to get
2x + 2f(x) · f′ (x) = 0
We could then solve to get
x
f′ (x) = −
f(x)
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 6 / 34
15. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. x
.
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
16. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. x
.
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
17. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. x
.
.
l
.ooks like a function
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
18. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the .
curve resembles the graph
of a function.
. x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
19. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the .
curve resembles the graph
of a function.
. x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
20. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the .
curve resembles the graph
of a function. l
.ooks like a function
. x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
21. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. . x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
22. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. . x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
23. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
. . x
.
.
does not look like a
function, but that’s
OK—there are only
two points like this
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
24. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
So f(x) is defined “locally”,
. x
.
almost everywhere and is
differentiable
.
l
.ooks like a function
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
25. Yes, we can!
The beautiful fact (i.e., deep theorem) is that this works!
y
.
“Near” most points on the
curve x2 + y2 = 1, the
curve resembles the graph
of a function.
So f(x) is defined “locally”,
. x
.
almost everywhere and is
differentiable
The chain rule then applies
.
for this local choice.
l
.ooks like a function
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 7 / 34
26. Motivating Example, again, with Leibniz notation
Problem
Find the slope of the line which is tangent to the curve x2 + y2 = 1 at
the point (3/5, −4/5).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 8 / 34
27. Motivating Example, again, with Leibniz notation
Problem
Find the slope of the line which is tangent to the curve x2 + y2 = 1 at
the point (3/5, −4/5).
Solution
dy
Differentiate: 2x + 2y =0
dx
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 8 / 34
28. Motivating Example, again, with Leibniz notation
Problem
Find the slope of the line which is tangent to the curve x2 + y2 = 1 at
the point (3/5, −4/5).
Solution
dy
Differentiate: 2x + 2y
=0
dx
Remember y is assumed to be a function of x!
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 8 / 34
29. Motivating Example, again, with Leibniz notation
Problem
Find the slope of the line which is tangent to the curve x2 + y2 = 1 at
the point (3/5, −4/5).
Solution
dy
Differentiate: 2x + 2y
=0
dx
Remember y is assumed to be a function of x!
dy x
Isolate: =− .
dx y
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 8 / 34
30. Motivating Example, again, with Leibniz notation
Problem
Find the slope of the line which is tangent to the curve x2 + y2 = 1 at
the point (3/5, −4/5).
Solution
dy
Differentiate: 2x + 2y =0
dx
Remember y is assumed to be a function of x!
dy x
Isolate: =− .
dx y
dy 3/5 3
Evaluate: = = .
dx ( 3 ,− 4 ) 4/5 4
5 5
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 8 / 34
31. Summary
If a relation is given between x and y which isn’t a function:
“Most of the time”, i.e., “at y
.
most places” y can be
.
assumed to be a function of x
we may differentiate the . x
.
relation as is
dy
Solving for does give the
dx
slope of the tangent line to the
curve at a point on the curve.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 9 / 34
32. Outline
The big idea, by example
Examples
Basic Examples
Vertical and Horizontal Tangents
Orthogonal Trajectories
Chemistry
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 10 / 34
33. Another Example
Example
Find y′ along the curve y3 + 4xy = x2 + 3.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 11 / 34
34. Another Example
Example
Find y′ along the curve y3 + 4xy = x2 + 3.
Solution
Implicitly differentiating, we have
3y2 y′ + 4(1 · y + x · y′ ) = 2x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 11 / 34
35. Another Example
Example
Find y′ along the curve y3 + 4xy = x2 + 3.
Solution
Implicitly differentiating, we have
3y2 y′ + 4(1 · y + x · y′ ) = 2x
Solving for y′ gives
3y2 y′ + 4xy′ = 2x − 4y
(3y2 + 4x)y′ = 2x − 4y
2x − 4y
=⇒ y′ = 2
3y + 4x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 11 / 34
36. Yet Another Example
Example
Find y′ if y5 + x2 y3 = 1 + y sin(x2 ).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 12 / 34
37. Yet Another Example
Example
Find y′ if y5 + x2 y3 = 1 + y sin(x2 ).
Solution
Differentiating implicitly:
5y4 y′ + (2x)y3 + x2 (3y2 y′ ) = y′ sin(x2 ) + y cos(x2 )(2x)
Collect all terms with y′ on one side and all terms without y′ on the
other:
5y4 y′ + 3x2 y2 y′ − sin(x2 )y′ = −2xy3 + 2xy cos(x2 )
Now factor and divide:
2xy(cos x2 − y2 )
y′ =
5y4 + 3x2 y2 − sin x2
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 12 / 34
38. Finding tangent lines with implicit differentitiation
.
Example
Find the equation of the line
tangent to the curve
.
y2 = x2 (x + 1) = x3 + x2
at the point (3, −6). .
. . . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 13 / 34
39. Finding tangent lines with implicit differentitiation
.
Example
Find the equation of the line
tangent to the curve
.
y2 = x2 (x + 1) = x3 + x2
at the point (3, −6). .
Solution
dy dy 3x2 + 2x
Differentiate: 2y = 3x2 + 2x, so = , and
dx dx 2y
dy 3 · 32 + 2 · 3 33 11
= =− =− .
dx (3,−6) 2(−6) 12 4
. . . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 13 / 34
40. Finding tangent lines with implicit differentitiation
.
Example
Find the equation of the line
tangent to the curve
.
y2 = x2 (x + 1) = x3 + x2
at the point (3, −6). .
Solution
dy dy 3x2 + 2x
Differentiate: 2y = 3x2 + 2x, so = , and
dx dx 2y
dy 3 · 32 + 2 · 3 33 11
= =− =− .
dx (3,−6) 2(−6) 12 4
11
Thus the equation of the tangent line is y + 6 = − (x − 3).
4
. . . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 13 / 34
41. Recall: Line equation forms
slope-intercept form
y = mx + b
where the slope is m and (0, b) is on the line.
point-slope form
y − y0 = m(x − x0 )
where the slope is m and (x0 , y0 ) is on the line.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 14 / 34
42. Horizontal Tangent Lines
Example
Find the horizontal tangent lines to the same curve: y2 = x3 + x2
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 15 / 34
43. Horizontal Tangent Lines
Example
Find the horizontal tangent lines to the same curve: y2 = x3 + x2
Solution
We have to solve these two equations:
.
. 3x2 + 2x
2 3 2 = 0
1 y = x. + x
. [(x, y) is on the curve] 2
.
2y
[tangent line
is horizontal]
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 15 / 34
44. Solution, continued
Solving the second equation gives
3x2 + 2x
= 0 =⇒ 3x2 + 2x = 0 =⇒ x(3x + 2) = 0
2y
(as long as y ̸= 0). So x = 0 or 3x + 2 = 0.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 16 / 34
45. Solution, continued
Solving the second equation gives
3x2 + 2x
= 0 =⇒ 3x2 + 2x = 0 =⇒ x(3x + 2) = 0
2y
(as long as y ̸= 0). So x = 0 or 3x + 2 = 0.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 16 / 34
46. Solution, continued
Solving the second equation gives
3x2 + 2x
= 0 =⇒ 3x2 + 2x = 0 =⇒ x(3x + 2) = 0
2y
(as long as y ̸= 0). So x = 0 or 3x + 2 = 0.
Substituting x = 0 into the first equation gives
y2 = 03 + 02 = 0 =⇒ y = 0
which we’ve disallowed. So no horizontal tangents down that road.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 16 / 34
47. Solution, continued
Solving the second equation gives
3x2 + 2x
= 0 =⇒ 3x2 + 2x = 0 =⇒ x(3x + 2) = 0
2y
(as long as y ̸= 0). So x = 0 or 3x + 2 = 0.
Substituting x = 0 into the first equation gives
y2 = 03 + 02 = 0 =⇒ y = 0
which we’ve disallowed. So no horizontal tangents down that road.
Substituting x = −2/3 into the first equation gives
( ) ( )
2 3 2 2 4 2
y = −
2
+ − = =⇒ y = ± √ ,
3 3 27 3 3
so there are two horizontal tangents.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 16 / 34
50. Example
Find the vertical tangent lines to the same curve: y2 = x3 + x2
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 18 / 34
51. Example
Find the vertical tangent lines to the same curve: y2 = x3 + x2
Solution
dx
Tangent lines are vertical when = 0.
dy
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 18 / 34
52. Example
Find the vertical tangent lines to the same curve: y2 = x3 + x2
Solution
dx
Tangent lines are vertical when = 0.
dy
Differentiating x implicitly as a function of y gives
dx dx dx 2y
2y = 3x2 + 2x , so = 2 (notice this is the
dy dy dy 3x + 2x
reciprocal of dy/dx).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 18 / 34
53. Example
Find the vertical tangent lines to the same curve: y2 = x3 + x2
Solution
dx
Tangent lines are vertical when = 0.
dy
Differentiating x implicitly as a function of y gives
dx dx dx 2y
2y = 3x2 + 2x , so = 2 (notice this is the
dy dy dy 3x + 2x
reciprocal of dy/dx).
We must solve
. .
2y
y2 = x3 + x2 =0
3x2 + 2x
1
. [(x, y). is on
the curve]
2
. [tangent line
. . .
is vertical]
. . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 18 / 34
54. Solution, continued
Solving the second equation gives
2y
= 0 =⇒ 2y = 0 =⇒ y = 0
3x2 + 2x
(as long as 3x2 + 2x ̸= 0).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 19 / 34
55. Solution, continued
Solving the second equation gives
2y
= 0 =⇒ 2y = 0 =⇒ y = 0
3x2 + 2x
(as long as 3x2 + 2x ̸= 0).
Substituting y = 0 into the first equation gives
0 = x3 + x2 = x2 (x + 1)
So x = 0 or x = −1.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 19 / 34
56. Solution, continued
Solving the second equation gives
2y
= 0 =⇒ 2y = 0 =⇒ y = 0
3x2 + 2x
(as long as 3x2 + 2x ̸= 0).
Substituting y = 0 into the first equation gives
0 = x3 + x2 = x2 (x + 1)
So x = 0 or x = −1.
x = 0 is not allowed by the first equation, but
dx
= 0,
dy (−1,0)
so here is a vertical tangent.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 19 / 34
58. Examples
Example
Show that the families of curves
xy = c x2 − y2 = k
are orthogonal, that is, they intersect at right angles.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 21 / 34
59. Orthogonal Families of Curves
y
.
.xy
=
1
xy = c
x2 − y2 = k . x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
60. Orthogonal Families of Curves
y
.
.xy
=
.xy
2
=
1
xy = c
x2 − y2 = k . x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
61. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
1
xy = c
x2 − y2 = k . x
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
62. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
1
xy = c
x2 − y2 = k . x
.
1
−
=
.xy
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
63. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
1
xy = c
x2 − y2 = k . x
.
1
−
2
=
−
.xy
=
.xy
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
64. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
1
xy = c
x2 − y2 = k . x
.
.xy = −1
= −2
.xy =
.xy
3
−
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
65. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
= −2
x
.xy =
.xy
3
−
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
66. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 2
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
= −2
x
x
.xy =
.xy
3
−
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
67. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 3
. − y2 = 2
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
x2
= −2
x
x
.xy =
.xy
3
−
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
68. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 3
. − y2 = 2
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
x2
= −2
x
x
. 2 − y2 = −1
.xy =
x
.xy
3
−
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
69. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 3
. − y2 = 2
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
x2
= −2
x
x
. 2 − y2 = −1
.xy =
x
.xy
3
. 2 − y2 = −2
−
x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
70. Orthogonal Families of Curves
y
.
.xy
.xy
=
3
=
.xy
2
=
. 2 − y2 = 3
. − y2 = 2
. 2 − y2 = 1
1
xy = c
x2 − y2 = k . x
.
.xy = −1
x2
= −2
x
x
. 2 − y2 = −1
.xy =
x
.xy
3
. 2 − y2 = −2
−
x2
. − y2 = −3
x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 22 / 34
71. Examples
Example
Show that the families of curves
xy = c x2 − y2 = k
are orthogonal, that is, they intersect at right angles.
Solution
y
In the first curve, y + xy′ = 0 =⇒ y′ = −
x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 23 / 34
72. Examples
Example
Show that the families of curves
xy = c x2 − y2 = k
are orthogonal, that is, they intersect at right angles.
Solution
y
In the first curve, y + xy′ = 0 =⇒ y′ = −
x
x
In the second curve, 2x − 2yy′ = 0 =⇒ y′ =
y
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 23 / 34
73. Examples
Example
Show that the families of curves
xy = c x2 − y2 = k
are orthogonal, that is, they intersect at right angles.
Solution
y
In the first curve, y + xy′ = 0 =⇒ y′ = −
x
x
In the second curve, 2x − 2yy′ = 0 =⇒ y′ =
y
The product is −1, so the tangent lines are perpendicular
wherever they intersect.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 23 / 34
74. Music Selection
“The Curse of Curves” by Cute is What We Aim For . . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 24 / 34
75. Ideal gases
The ideal gas law relates
temperature, pressure, and
volume of a gas:
PV = nRT
(R is a constant, n is the
amount of gas in moles)
.
Ima
.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 25 / 34
76. Compressibility
Definition
The isothermic compressibility of a fluid is defined by
dV 1
β=−
dP V
with temperature held constant.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 26 / 34
77. Compressibility
Definition
The isothermic compressibility of a fluid is defined by
dV 1
β=−
dP V
with temperature held constant.
Approximately we have
∆V dV ∆V
≈ = −βV =⇒ ≈ −β∆P
∆P dP V
The smaller the β, the “harder” the fluid.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 26 / 34
78. Compressibility of an ideal gas
Example
Find the isothermic compressibility of an ideal gas.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 27 / 34
79. Compressibility of an ideal gas
Example
Find the isothermic compressibility of an ideal gas.
Solution
If PV = k (n is constant for our purposes, T is constant because of the
word isothermic, and R really is constant), then
dP dV dV V
·V+P = 0 =⇒ =−
dP dP dP P
So
1 dV 1
β=−
· =
V dP P
Compressibility and pressure are inversely related.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 27 / 34
80. Nonideal gasses
Not that there's anything wrong with that
Example
The van der Waals equation H
..
makes fewer simplifications:
( ) O .
. xygen . .
H
n2 .
P + a 2 (V − nb) = nRT,
V H
..
where P is the pressure, V the O .
. xygen H
. ydrogen bonds
volume, T the temperature, n H
..
.
the number of moles of the gas,
R a constant, a is a measure of O .
. xygen . .
H
attraction between particles of
the gas, and b a measure of H
..
particle size.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 28 / 34
81. Nonideal gasses
Not that there's anything wrong with that
Example
The van der Waals equation
makes fewer simplifications:
( )
n2
P + a 2 (V − nb) = nRT,
V
.
where P is the pressure, V the
volume, T the temperature, n
the number of moles of the gas,
R a constant, a is a measure of
attraction between particles of
the gas, and b a measure of
particle size.
. . ikimedia Commons
W
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 28 / 34
82. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
83. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
so
1 dV V2 (V − nb)
β=− =
V dP 2abn3 − an2 V + PV3
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
84. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
so
1 dV V2 (V − nb)
β=− =
V dP 2abn3 − an2 V + PV3
Question
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
85. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
so
1 dV V2 (V − nb)
β=− =
V dP 2abn3 − an2 V + PV3
Question
What if a = b = 0?
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
86. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
so
1 dV V2 (V − nb)
β=− =
V dP 2abn3 − an2 V + PV3
Question
What if a = b = 0?
dβ
Without taking the derivative, what is the sign of ?
db
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
87. Compressibility of a van der Waals gas
Differentiating the van der Waals equation by treating V as a function
of P gives
( ) ( )
an2 dV 2an2 dV
P+ 2 + (V − bn) 1 − 3 = 0,
V dP V dP
so
1 dV V2 (V − nb)
β=− =
V dP 2abn3 − an2 V + PV3
Question
What if a = b = 0?
dβ
Without taking the derivative, what is the sign of ?
db
dβ
Without taking the derivative, what is the sign of ?
da
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 29 / 34
88. Nasty derivatives
dβ (2abn3 − an2 V + PV3 )(nV2 ) − (nbV2 − V3 )(2an3 )
=−
db (2abn3 − an2 V + PV3 )2
( )
nV3 an2 + PV2
= −( )2 < 0
PV3 + an2 (2bn − V)
dβ n2 (bn − V)(2bn − V)V2
=( )2 > 0
da 3 2 (2bn − V)
PV + an
(as long as V > 2nb, and it’s probably true that V ≫ 2nb).
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 30 / 34
89. Outline
The big idea, by example
Examples
Basic Examples
Vertical and Horizontal Tangents
Orthogonal Trajectories
Chemistry
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 31 / 34
90. Using implicit differentiation to find derivatives
Example
dy √
Find if y = x.
dx
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 32 / 34
91. Using implicit differentiation to find derivatives
Example
dy √
Find if y = x.
dx
Solution
√
If y = x, then
y2 = x,
so
dy dy 1 1
2y = 1 =⇒ = = √ .
dx dx 2y 2 x
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 32 / 34
92. The power rule for rational powers
Theorem
p p/q−1
If y = xp/q , where p and q are integers, then y′ = x .
q
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 33 / 34
93. The power rule for rational powers
Theorem
p p/q−1
If y = xp/q , where p and q are integers, then y′ = x .
q
Proof.
First, raise both sides to the qth power:
y = xp/q =⇒ yq = xp
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 33 / 34
94. The power rule for rational powers
Theorem
p p/q−1
If y = xp/q , where p and q are integers, then y′ = x .
q
Proof.
First, raise both sides to the qth power:
y = xp/q =⇒ yq = xp
Now, differentiate implicitly:
dy dy p xp−1
qyq−1 = pxp−1 =⇒ = · q−1
dx dx q y
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 33 / 34
95. The power rule for rational powers
Theorem
p p/q−1
If y = xp/q , where p and q are integers, then y′ = x .
q
Proof.
First, raise both sides to the qth power:
y = xp/q =⇒ yq = xp
Now, differentiate implicitly:
dy dy p xp−1
qyq−1 = pxp−1 =⇒ = · q−1
dx dx q y
Simplify: yq−1 = x(p/q)(q−1) = xp−p/q so
xp−1 xp−1
= p−p/q = xp−1−(p−p/q) = xp/q−1
. . . . . .
V63.0121.021, Calculus I
yq−1
(NYU)
x Section 2.6 Implicit Differentiation October 12, 2010 33 / 34
96. Summary
Implicit Differentiation allows us to pretend that a relation
describes a function, since it does, locally, “almost everywhere.”
The Power Rule was established for powers which are rational
numbers.
. . . . . .
V63.0121.021, Calculus I (NYU) Section 2.6 Implicit Differentiation October 12, 2010 34 / 34