AP Calculus BC Scope & Sequence
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Lecture 9: Partial Derivatives
Math S21a: Multivariable calculus Oliver Knill, Summer 2016 Lecture 9: Partial derivatives ∂ If f(x,y) is a function of two variables, then ∂x f(x,y) is defined as the derivative of the function g(x) = f(x,y) with respect to x, where y is considered a constant. It is called the partial derivative of f with respect to x. The partial derivative with respect to y is defined in the same way. ∂ We use the short hand notation fx(x,y) = ∂x f(x,y). For iterated derivatives, the notation is ∂ ∂ similar: for example fxy = ∂x ∂y f. The meaning of fx(x0,y0) is the slope of the graph sliced at (x0,y0) in the x direction. The second derivative fxx is a measure of concavity in that direction. The meaning of fxy is the rate of change of the slope if you change the slicing. The notation for partial derivatives ∂xf,∂yf was introduced by Carl Gustav Jacobi. Before, Josef Lagrange had used the term ”partial differences”. Partial derivatives fx and fy measure the rate of change of the function in the x or y directions. For functions of more variables, the partial derivatives are defined in a similar way. 4 2 2 4 3 2 2 2 1 For f(x,y)= x 6x y + y , we have fx(x,y)=4x 12xy ,fxx = 12x 12y ,fy(x,y)= − − − 12x2y +4y3,f = 12x2 +12y2 and see that f + f = 0. A function which satisfies this − yy − xx yy equation is also called harmonic. The equation fxx + fyy = 0 is an example of a partial differential equation: it is an equation for an unknown function f(x,y) which involves partial derivatives with respect to more than one variables. -
Differentiation Rules (Differential Calculus)
Differentiation Rules (Differential Calculus) 1. Notation The derivative of a function f with respect to one independent variable (usually x or t) is a function that will be denoted by D f . Note that f (x) and (D f )(x) are the values of these functions at x. 2. Alternate Notations for (D f )(x) d d f (x) d f 0 (1) For functions f in one variable, x, alternate notations are: Dx f (x), dx f (x), dx , dx (x), f (x), f (x). The “(x)” part might be dropped although technically this changes the meaning: f is the name of a function, dy 0 whereas f (x) is the value of it at x. If y = f (x), then Dxy, dx , y , etc. can be used. If the variable t represents time then Dt f can be written f˙. The differential, “d f ”, and the change in f ,“D f ”, are related to the derivative but have special meanings and are never used to indicate ordinary differentiation. dy 0 Historical note: Newton used y,˙ while Leibniz used dx . About a century later Lagrange introduced y and Arbogast introduced the operator notation D. 3. Domains The domain of D f is always a subset of the domain of f . The conventional domain of f , if f (x) is given by an algebraic expression, is all values of x for which the expression is defined and results in a real number. If f has the conventional domain, then D f usually, but not always, has conventional domain. Exceptions are noted below. -
APPM 1350 - Calculus 1
APPM 1350 - Calculus 1 Course Objectives: This class will form the basis for many of the standard skills required in all of Engineering, the Sciences, and Mathematics. Specically, students will: • Understand the concepts, techniques and applications of dierential and integral calculus including limits, rate of change of functions, and derivatives and integrals of algebraic and transcendental functions. • Improve problem solving and critical thinking Textbook: Essential Calculus, 2nd Edition by James Stewart. We will cover Chapters 1-5. You will also need an access code for WebAssign’s online homework system. The access code can also be purchased separately. Schedule and Topics Covered Day Section Topics 1 Appendix A Trigonometry 2 1.1 Functions and Their Representation 3 1.2 Essential Functions 4 1.3 The Limit of a Function 5 1.4 Calculating Limits 6 1.5 Continuity 7 1.5/1.6 Continuity/Limits Involving Innity 8 1.6 Limits Involving Innity 9 2.1 Derivatives and Rates of Change 10 Exam 1 Review Exam 1 Topics 11 2.2 The Derivative as a Function 12 2.3 Basic Dierentiation Formulas 13 2.4 Product and Quotient Rules 14 2.5 Chain Rule 15 2.6 Implicit Dierentiation 16 2.7 Related Rates 17 2.8 Linear Approximation and Dierentials 18 3.1 Max and Min Values 19 3.2 The Mean Value Theorem 20 3.3 Derivatives and Shapes of Graphs 21 3.4 Curve Sketching 22 Exam 2 Review Exam 2 Topics 23 3.4/3.5 Curve Sketching/Optimization Problems 24 3.5 Optimization Problems 25 3.6/3.7 Newton’s Method/Antiderivatives 26 3.7/Appendix B Sigma Notation 27 Appendix B, -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
CHAPTER 3: Derivatives
CHAPTER 3: Derivatives 3.1: Derivatives, Tangent Lines, and Rates of Change 3.2: Derivative Functions and Differentiability 3.3: Techniques of Differentiation 3.4: Derivatives of Trigonometric Functions 3.5: Differentials and Linearization of Functions 3.6: Chain Rule 3.7: Implicit Differentiation 3.8: Related Rates • Derivatives represent slopes of tangent lines and rates of change (such as velocity). • In this chapter, we will define derivatives and derivative functions using limits. • We will develop short cut techniques for finding derivatives. • Tangent lines correspond to local linear approximations of functions. • Implicit differentiation is a technique used in applied related rates problems. (Section 3.1: Derivatives, Tangent Lines, and Rates of Change) 3.1.1 SECTION 3.1: DERIVATIVES, TANGENT LINES, AND RATES OF CHANGE LEARNING OBJECTIVES • Relate difference quotients to slopes of secant lines and average rates of change. • Know, understand, and apply the Limit Definition of the Derivative at a Point. • Relate derivatives to slopes of tangent lines and instantaneous rates of change. • Relate opposite reciprocals of derivatives to slopes of normal lines. PART A: SECANT LINES • For now, assume that f is a polynomial function of x. (We will relax this assumption in Part B.) Assume that a is a constant. • Temporarily fix an arbitrary real value of x. (By “arbitrary,” we mean that any real value will do). Later, instead of thinking of x as a fixed (or single) value, we will think of it as a “moving” or “varying” variable that can take on different values. The secant line to the graph of f on the interval []a, x , where a < x , is the line that passes through the points a, fa and x, fx. -
Antiderivatives 307
4100 AWL/Thomas_ch04p244-324 8/20/04 9:02 AM Page 307 4.8 Antiderivatives 307 4.8 Antiderivatives We have studied how to find the derivative of a function. However, many problems re- quire that we recover a function from its known derivative (from its known rate of change). For instance, we may know the velocity function of an object falling from an initial height and need to know its height at any time over some period. More generally, we want to find a function F from its derivative ƒ. If such a function F exists, it is called an anti- derivative of ƒ. Finding Antiderivatives DEFINITION Antiderivative A function F is an antiderivative of ƒ on an interval I if F¿sxd = ƒsxd for all x in I. The process of recovering a function F(x) from its derivative ƒ(x) is called antidiffer- entiation. We use capital letters such as F to represent an antiderivative of a function ƒ, G to represent an antiderivative of g, and so forth. EXAMPLE 1 Finding Antiderivatives Find an antiderivative for each of the following functions. (a) ƒsxd = 2x (b) gsxd = cos x (c) hsxd = 2x + cos x Solution (a) Fsxd = x2 (b) Gsxd = sin x (c) Hsxd = x2 + sin x Each answer can be checked by differentiating. The derivative of Fsxd = x2 is 2x. The derivative of Gsxd = sin x is cos x and the derivative of Hsxd = x2 + sin x is 2x + cos x. The function Fsxd = x2 is not the only function whose derivative is 2x. The function x2 + 1 has the same derivative. -
Numerical Differentiation
Jim Lambers MAT 460/560 Fall Semester 2009-10 Lecture 23 Notes These notes correspond to Section 4.1 in the text. Numerical Differentiation We now discuss the other fundamental problem from calculus that frequently arises in scientific applications, the problem of computing the derivative of a given function f(x). Finite Difference Approximations 0 Recall that the derivative of f(x) at a point x0, denoted f (x0), is defined by 0 f(x0 + h) − f(x0) f (x0) = lim : h!0 h 0 This definition suggests a method for approximating f (x0). If we choose h to be a small positive constant, then f(x + h) − f(x ) f 0(x ) ≈ 0 0 : 0 h This approximation is called the forward difference formula. 00 To estimate the accuracy of this approximation, we note that if f (x) exists on [x0; x0 + h], 0 00 2 then, by Taylor's Theorem, f(x0 +h) = f(x0)+f (x0)h+f ()h =2; where 2 [x0; x0 +h]: Solving 0 for f (x0), we obtain f(x + h) − f(x ) f 00() f 0(x ) = 0 0 + h; 0 h 2 so the error in the forward difference formula is O(h). We say that this formula is first-order accurate. 0 The forward-difference formula is called a finite difference approximation to f (x0), because it approximates f 0(x) using values of f(x) at points that have a small, but finite, distance between them, as opposed to the definition of the derivative, that takes a limit and therefore computes the derivative using an “infinitely small" value of h. -
Review of Differentiation and Integration
Schreyer Fall 2018 Review of Differentiation and Integration for Ordinary Differential Equations In this course you will be expected to be able to differentiate and integrate quickly and accurately. Many students take this course after having taken their previous course many years ago, at another institution where certain topics may have been omitted, or just feel uncomfortable with particular techniques. Because understanding this material is so important to being successful in this course, we have put together this brief review packet. In this packet you will find sample questions and a brief discussion of each topic. If you find the material in this pamphlet is not sufficient for you, it may be necessary for you to use additional resources, such as a calculus textbook or online materials. Because this is considered prerequisite material, it is ultimately your responsibility to learn it. The topics to be covered include Differentiation and Integration. 1 Differentiation Exercises: 1. Find the derivative of y = x3 sin(x). ln(x) 2. Find the derivative of y = cos(x) . 3. Find the derivative of y = ln(sin(e2x)). Discussion: It is expected that you know, without looking at a table, the following differentiation rules: d [(kx)n] = kn(kx)n−1 (1) dx d h i ekx = kekx (2) dx d 1 [ln(kx)] = (3) dx x d [sin(kx)] = k cos kx (4) dx d [cos(kx)] = −k sin x (5) dx d [uv] = u0v + uv0 (6) dx 1 d u u0v − uv0 = (7) dx v v2 d [u(v(x))] = u0(v)v0(x): (8) dx We put in the constant k into (1) - (5) because a very common mistake to make is something d e2x like: e2x = (when the correct answer is 2e2x). -
Just the Definitions, Theorem Statements, Proofs On
MATH 3333{INTERMEDIATE ANALYSIS{BLECHER NOTES 55 Abbreviated notes version for Test 3: just the definitions, theorem statements, proofs on the list. I may possibly have made a mistake, so check it. Also, this is not intended as a REPLACEMENT for your classnotes; the classnotes have lots of other things that you may need for your understanding, like worked examples. 6. The derivative 6.1. Differentiation rules. Definition: Let f :(a; b) ! R and c 2 (a; b). If the limit lim f(x)−f(c) exists and is finite, then we say that f is differentiable at x!c x−c 0 df c, and we write this limit as f (c) or dx (c). This is the derivative of f at c, and also obviously equals lim f(c+h)−f(c) by setting x = c + h or h = x − c. If f is h!0 h differentiable at every point in (a; b), then we say that f is differentiable on (a; b). Theorem 6.1. If f :(a; b) ! R is differentiable at at a point c 2 (a; b), then f is continuous at c. Proof. If f is differentiable at c then f(x) − f(c) f(x) = (x − c) + f(c) ! f 0(c)0 + f(c) = f(c); x − c as x ! c. So f is continuous at c. Theorem 6.2. (Calculus I differentiation laws) If f; g :(a; b) ! R is differentiable at a point c 2 (a; b), then (1) f(x) + g(x) is differentiable at c and (f + g)0(c) = f 0(c) + g0(c). -
MATH 162: Calculus II Differentiation
MATH 162: Calculus II Framework for Mon., Jan. 29 Review of Differentiation and Integration Differentiation Definition of derivative f 0(x): f(x + h) − f(x) f(y) − f(x) lim or lim . h→0 h y→x y − x Differentiation rules: 1. Sum/Difference rule: If f, g are differentiable at x0, then 0 0 0 (f ± g) (x0) = f (x0) ± g (x0). 2. Product rule: If f, g are differentiable at x0, then 0 0 0 (fg) (x0) = f (x0)g(x0) + f(x0)g (x0). 3. Quotient rule: If f, g are differentiable at x0, and g(x0) 6= 0, then 0 0 0 f f (x0)g(x0) − f(x0)g (x0) (x0) = 2 . g [g(x0)] 4. Chain rule: If g is differentiable at x0, and f is differentiable at g(x0), then 0 0 0 (f ◦ g) (x0) = f (g(x0))g (x0). This rule may also be expressed as dy dy du = . dx x=x0 du u=u(x0) dx x=x0 Implicit differentiation is a consequence of the chain rule. For instance, if y is really dependent upon x (i.e., y = y(x)), and if u = y3, then d du du dy d (y3) = = = (y3)y0(x) = 3y2y0. dx dx dy dx dy Practice: Find d x d √ d , (x2 y), and [y cos(xy)]. dx y dx dx MATH 162—Framework for Mon., Jan. 29 Review of Differentiation and Integration Integration The definite integral • the area problem • Riemann sums • definition Fundamental Theorem of Calculus: R x I: Suppose f is continuous on [a, b]. -
ATMS 310 Math Review Differential Calculus the First Derivative of Y = F(X)
ATMS 310 Math Review Differential Calculus dy The first derivative of y = f(x) = = f `(x) dx d2 x The second derivative of y = f(x) = = f ``(x) dy2 Some differentiation rules: dyn = nyn-1 dx d() uv dv du = u( ) + v( ) dx dx dx ⎛ u ⎞ ⎛ du dv ⎞ d⎜ ⎟ ⎜v − u ⎟ v dx dx ⎝ ⎠ = ⎝ ⎠ dx v2 dy ⎛ dy dz ⎞ Chain Rule If y = f(z) and z = f(x), = ⎜ *⎟ dx ⎝ dz dx ⎠ Total vs. Partial Derivatives The rate of change of something following a fluid element (e.g. change in temperature in the atmosphere) is called the Langrangian rate of change: d / dt or D / Dt The rate of change of something at a fixed point is called the Eulerian (oy – layer’ – ian) rate of change: ∂/∂t, ∂/∂x, ∂/∂y, ∂/∂z The total rate of change of a variable is the sum of the local rate of change plus the 3- dimensional advection of that variable: d() ∂( ) ∂( ) ∂( ) ∂( ) = + u + v + w dt∂ t ∂x ∂y ∂z (1) (2) (3) (4) Where (1) is the Eulerian rate of change, and terms (2) – (4) is the 3-dimensional advection of the variable (u = zonal wind, v = meridional wind, w = vertical wind) Vectors A scalar only has a magnitude (e.g. temperature). A vector has magnitude and direction (e.g. wind). Vectors are usually represented in bold font. The wind vector is specified as V or r sometimes as V i, j, and k known as unit vectors. They have a magnitude of 1 and point in the x (i), y (j), and z (k) directions. -
A Gallery of Visualization DEMOS for Related Rate Problems The
A Gallery of Visualization DEMOS for Related Rate Problems The following is a gallery of demos for visualizing common related rate situations. These animations can be used by instructors in a classroom setting or by students to aid in acquiring a visualization background relating to the steps for solving related problems. Two file formats, gif and mov (QuickTime) are available. 1. The gif animations should run on most systems and the file sizes are relatively small. 2. The mov animations require the QuickTime Player which is a free download available by clicking here; these file are also small. (The mov files may not execute properly in old versions of QuickTime.) 3. The collection of animations in gif and mov format can be downloaded; see the 'bulk' zipped download category at the bottom of the following table. General problem Click to view the Animation Sample Click to view the gif file. description. QuickTime file. Click to see gif Click to see mov Filling a conical tank. animation animation Searchlight rotates to Click to see gif Click to see mov follow a walker. animation animation Filling a cylindrical tank, Click to see gif Click to see mov two ways. animation animation Airplane & observer. Click to see gif Click to see mov animation animation Click to see gif Click to see mov Conical sand pile. animation animation Click to see gif Click to see mov Sliding ladder. animation animation Click to see gif Click to see mov Raising ladder. animation animation Shadow of a walking Click to see gif Click to see mov figure.