The Second Derivative
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The Mean Value Theorem Math 120 Calculus I Fall 2015
The Mean Value Theorem Math 120 Calculus I Fall 2015 The central theorem to much of differential calculus is the Mean Value Theorem, which we'll abbreviate MVT. It is the theoretical tool used to study the first and second derivatives. There is a nice logical sequence of connections here. It starts with the Extreme Value Theorem (EVT) that we looked at earlier when we studied the concept of continuity. It says that any function that is continuous on a closed interval takes on a maximum and a minimum value. A technical lemma. We begin our study with a technical lemma that allows us to relate 0 the derivative of a function at a point to values of the function nearby. Specifically, if f (x0) is positive, then for x nearby but smaller than x0 the values f(x) will be less than f(x0), but for x nearby but larger than x0, the values of f(x) will be larger than f(x0). This says something like f is an increasing function near x0, but not quite. An analogous statement 0 holds when f (x0) is negative. Proof. The proof of this lemma involves the definition of derivative and the definition of limits, but none of the proofs for the rest of the theorems here require that depth. 0 Suppose that f (x0) = p, some positive number. That means that f(x) − f(x ) lim 0 = p: x!x0 x − x0 f(x) − f(x0) So you can make arbitrarily close to p by taking x sufficiently close to x0. -
AP Calculus AB Topic List 1. Limits Algebraically 2. Limits Graphically 3
AP Calculus AB Topic List 1. Limits algebraically 2. Limits graphically 3. Limits at infinity 4. Asymptotes 5. Continuity 6. Intermediate value theorem 7. Differentiability 8. Limit definition of a derivative 9. Average rate of change (approximate slope) 10. Tangent lines 11. Derivatives rules and special functions 12. Chain Rule 13. Application of chain rule 14. Derivatives of generic functions using chain rule 15. Implicit differentiation 16. Related rates 17. Derivatives of inverses 18. Logarithmic differentiation 19. Determine function behavior (increasing, decreasing, concavity) given a function 20. Determine function behavior (increasing, decreasing, concavity) given a derivative graph 21. Interpret first and second derivative values in a table 22. Determining if tangent line approximations are over or under estimates 23. Finding critical points and determining if they are relative maximum, relative minimum, or neither 24. Second derivative test for relative maximum or minimum 25. Finding inflection points 26. Finding and justifying critical points from a derivative graph 27. Absolute maximum and minimum 28. Application of maximum and minimum 29. Motion derivatives 30. Vertical motion 31. Mean value theorem 32. Approximating area with rectangles and trapezoids given a function 33. Approximating area with rectangles and trapezoids given a table of values 34. Determining if area approximations are over or under estimates 35. Finding definite integrals graphically 36. Finding definite integrals using given integral values 37. Indefinite integrals with power rule or special derivatives 38. Integration with u-substitution 39. Evaluating definite integrals 40. Definite integrals with u-substitution 41. Solving initial value problems (separable differential equations) 42. Creating a slope field 43. -
Chapter 9 Optimization: One Choice Variable
RS - Ch 9 - Optimization: One Variable Chapter 9 Optimization: One Choice Variable 1 Léon Walras (1834-1910) Vilfredo Federico D. Pareto (1848–1923) 9.1 Optimum Values and Extreme Values • Goal vs. non-goal equilibrium • In the optimization process, we need to identify the objective function to optimize. • In the objective function the dependent variable represents the object of maximization or minimization Example: - Define profit function: = PQ − C(Q) - Objective: Maximize - Tool: Q 2 1 RS - Ch 9 - Optimization: One Variable 9.2 Relative Maximum and Minimum: First- Derivative Test Critical Value The critical value of x is the value x0 if f ′(x0)= 0. • A stationary value of y is f(x0). • A stationary point is the point with coordinates x0 and f(x0). • A stationary point is coordinate of the extremum. • Theorem (Weierstrass) Let f : S→R be a real-valued function defined on a compact (bounded and closed) set S ∈ Rn. If f is continuous on S, then f attains its maximum and minimum values on S. That is, there exists a point c1 and c2 such that f (c1) ≤ f (x) ≤ f (c2) ∀x ∈ S. 3 9.2 First-derivative test •The first-order condition (f.o.c.) or necessary condition for extrema is that f '(x*) = 0 and the value of f(x*) is: • A relative minimum if f '(x*) changes its sign y from negative to positive from the B immediate left of x0 to its immediate right. f '(x*)=0 (first derivative test of min.) x x* y • A relative maximum if the derivative f '(x) A f '(x*) = 0 changes its sign from positive to negative from the immediate left of the point x* to its immediate right. -
Section 6: Second Derivative and Concavity Second Derivative and Concavity
Chapter 2 The Derivative Applied Calculus 122 Section 6: Second Derivative and Concavity Second Derivative and Concavity Graphically, a function is concave up if its graph is curved with the opening upward (a in the figure). Similarly, a function is concave down if its graph opens downward (b in the figure). This figure shows the concavity of a function at several points. Notice that a function can be concave up regardless of whether it is increasing or decreasing. For example, An Epidemic: Suppose an epidemic has started, and you, as a member of congress, must decide whether the current methods are effectively fighting the spread of the disease or whether more drastic measures and more money are needed. In the figure below, f(x) is the number of people who have the disease at time x, and two different situations are shown. In both (a) and (b), the number of people with the disease, f(now), and the rate at which new people are getting sick, f '(now), are the same. The difference in the two situations is the concavity of f, and that difference in concavity might have a big effect on your decision. In (a), f is concave down at "now", the slopes are decreasing, and it looks as if it’s tailing off. We can say “f is increasing at a decreasing rate.” It appears that the current methods are starting to bring the epidemic under control. In (b), f is concave up, the slopes are increasing, and it looks as if it will keep increasing faster and faster. -
Multivariable and Vector Calculus
Multivariable and Vector Calculus Lecture Notes for MATH 0200 (Spring 2015) Frederick Tsz-Ho Fong Department of Mathematics Brown University Contents 1 Three-Dimensional Space ....................................5 1.1 Rectangular Coordinates in R3 5 1.2 Dot Product7 1.3 Cross Product9 1.4 Lines and Planes 11 1.5 Parametric Curves 13 2 Partial Differentiations ....................................... 19 2.1 Functions of Several Variables 19 2.2 Partial Derivatives 22 2.3 Chain Rule 26 2.4 Directional Derivatives 30 2.5 Tangent Planes 34 2.6 Local Extrema 36 2.7 Lagrange’s Multiplier 41 2.8 Optimizations 46 3 Multiple Integrations ........................................ 49 3.1 Double Integrals in Rectangular Coordinates 49 3.2 Fubini’s Theorem for General Regions 53 3.3 Double Integrals in Polar Coordinates 57 3.4 Triple Integrals in Rectangular Coordinates 62 3.5 Triple Integrals in Cylindrical Coordinates 67 3.6 Triple Integrals in Spherical Coordinates 70 4 Vector Calculus ............................................ 75 4.1 Vector Fields on R2 and R3 75 4.2 Line Integrals of Vector Fields 83 4.3 Conservative Vector Fields 88 4.4 Green’s Theorem 98 4.5 Parametric Surfaces 105 4.6 Stokes’ Theorem 120 4.7 Divergence Theorem 127 5 Topics in Physics and Engineering .......................... 133 5.1 Coulomb’s Law 133 5.2 Introduction to Maxwell’s Equations 137 5.3 Heat Diffusion 141 5.4 Dirac Delta Functions 144 1 — Three-Dimensional Space 1.1 Rectangular Coordinates in R3 Throughout the course, we will use an ordered triple (x, y, z) to represent a point in the three dimensional space. -
Concavity and Points of Inflection We Now Know How to Determine Where a Function Is Increasing Or Decreasing
Chapter 4 | Applications of Derivatives 401 4.17 3 Use the first derivative test to find all local extrema for f (x) = x − 1. Concavity and Points of Inflection We now know how to determine where a function is increasing or decreasing. However, there is another issue to consider regarding the shape of the graph of a function. If the graph curves, does it curve upward or curve downward? This notion is called the concavity of the function. Figure 4.34(a) shows a function f with a graph that curves upward. As x increases, the slope of the tangent line increases. Thus, since the derivative increases as x increases, f ′ is an increasing function. We say this function f is concave up. Figure 4.34(b) shows a function f that curves downward. As x increases, the slope of the tangent line decreases. Since the derivative decreases as x increases, f ′ is a decreasing function. We say this function f is concave down. Definition Let f be a function that is differentiable over an open interval I. If f ′ is increasing over I, we say f is concave up over I. If f ′ is decreasing over I, we say f is concave down over I. Figure 4.34 (a), (c) Since f ′ is increasing over the interval (a, b), we say f is concave up over (a, b). (b), (d) Since f ′ is decreasing over the interval (a, b), we say f is concave down over (a, b). 402 Chapter 4 | Applications of Derivatives In general, without having the graph of a function f , how can we determine its concavity? By definition, a function f is concave up if f ′ is increasing. -
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 -
Matrix Calculus
Appendix D Matrix Calculus From too much study, and from extreme passion, cometh madnesse. Isaac Newton [205, §5] − D.1 Gradient, Directional derivative, Taylor series D.1.1 Gradients Gradient of a differentiable real function f(x) : RK R with respect to its vector argument is defined uniquely in terms of partial derivatives→ ∂f(x) ∂x1 ∂f(x) , ∂x2 RK f(x) . (2053) ∇ . ∈ . ∂f(x) ∂xK while the second-order gradient of the twice differentiable real function with respect to its vector argument is traditionally called the Hessian; 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂x1 ∂x1∂x2 ··· ∂x1∂xK 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 2 K f(x) , ∂x2∂x1 ∂x2 ··· ∂x2∂xK S (2054) ∇ . ∈ . .. 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂xK ∂x1 ∂xK ∂x2 ∂x ··· K interpreted ∂f(x) ∂f(x) 2 ∂ ∂ 2 ∂ f(x) ∂x1 ∂x2 ∂ f(x) = = = (2055) ∂x1∂x2 ³∂x2 ´ ³∂x1 ´ ∂x2∂x1 Dattorro, Convex Optimization Euclidean Distance Geometry, Mεβoo, 2005, v2020.02.29. 599 600 APPENDIX D. MATRIX CALCULUS The gradient of vector-valued function v(x) : R RN on real domain is a row vector → v(x) , ∂v1(x) ∂v2(x) ∂vN (x) RN (2056) ∇ ∂x ∂x ··· ∂x ∈ h i while the second-order gradient is 2 2 2 2 , ∂ v1(x) ∂ v2(x) ∂ vN (x) RN v(x) 2 2 2 (2057) ∇ ∂x ∂x ··· ∂x ∈ h i Gradient of vector-valued function h(x) : RK RN on vector domain is → ∂h1(x) ∂h2(x) ∂hN (x) ∂x1 ∂x1 ··· ∂x1 ∂h1(x) ∂h2(x) ∂hN (x) h(x) , ∂x2 ∂x2 ··· ∂x2 ∇ . -
Chapter 8 Optimal Points
Samenvatting Essential Mathematics for Economics Analysis 14-15 Chapter 8 Optimal Points Extreme points The extreme points of a function are where it reaches its largest and its smallest values, the maximum and minimum points. Formally, • is the maximum point for for all • is the minimum point for for all Where the derivative of the function equals zero, , the point x is called a stationary point or critical point . For some point to be the maximum or minimum of a function, it has to be such a stationary point. This is called the first-order condition . It is a necessary condition for a differentiable function to have a maximum of minimum at a point in its domain. Stationary points can be local or global maxima or minima, or an inflection point. We can find the nature of stationary points by using the first derivative. The following logic should hold: • If for and for , then is the maximum point for . • If for and for , then is the minimum point for . Is a function concave on a certain interval I, then the stationary point in this interval is a maximum point for the function. When it is a convex function, the stationary point is a minimum. Economic Applications Take the following example, when the price of a product is p, the revenue can be found by . What price maximizes the revenue? 1. Find the first derivative: 2. Set the first derivative equal to zero, , and solve for p. 3. The result is . Before we can conclude whether revenue is maximized at 2, we need to check whether 2 is indeed a maximum point. -
Concavity and Inflection Points. Extreme Values and the Second
Calculus 1 Lia Vas Concavity and Inflection Points. Extreme Values and The Second Derivative Test. Consider the following two increasing functions. While they are both increasing, their concavity distinguishes them. The first function is said to be concave up and the second to be concave down. More generally, a function is said to be concave up on an interval if the graph of the function is above the tangent at each point of the interval. A function is said to be concave down on an interval if the graph of the function is below the tangent at each point of the interval. Concave up Concave down In case of the two functions above, their concavity relates to the rate of the increase. While the first derivative of both functions is positive since both are increasing, the rate of the increase distinguishes them. The first function increases at an increasing rate (see how the tangents become steeper as x-values increase) because the slope of the tangent line becomes steeper and steeper as x values increase. So, the first derivative of the first function is increasing. Thus, the derivative of the first derivative, the second derivative is positive. Note that this function is concave up. The second function increases at an decreasing rate (see how it flattens towards the right end of the graph) so that the first derivative of the second function is decreasing because the slope of the 1 tangent line becomes less and less steep as x values increase. So, the derivative of the first derivative, second derivative is negative. -
440 Geophysics: Brief Notes on Math Thorsten Becker, University of Southern California, 02/2005
440 Geophysics: Brief notes on math Thorsten Becker, University of Southern California, 02/2005 Linear algebra Linear (vector) algebra is discussed in the appendix of Fowler and in your favorite math textbook. I only make brief comments here, to clarify and summarize a few issues that came up in class. At places, these are not necessarily mathematically rigorous. Dot product We have made use of the dot product, which is defined as n c =~a ·~b = ∑ aibi, (1) i=1 where ~a and~b are vectors of dimension n (n-dimensional, pointed objects like a velocity) and the n outcome of this operation is a scalar (a regular number), c. In eq. (1), ∑i=1 means “sum all that follows while increasing the index i from the lower limit, i = 1, in steps of of unity, to the upper limit, i = n”. In the examples below, we will assume a typical, spatial coordinate system with n = 3 so that ~a ·~b = a1b1 + a2b2 + a3b3, (2) where 1, 2, 3 refer to the vectors components along x, y, and z axis, respectively. When we write out the vector components, we put them on top of each other a1 ax ~a = a2 = ay (3) a3 az or in a list, maybe with curly brackets, like so: ~a = {a1,a2,a3}. On the board, I usually write vectors as a rather than ~a, because that’s easier. You may also see vectors printed as bold face letters, like so: a. We can write the amplitude or length of a vector as s n q q 2 2 2 2 2 2 2 |~a| = ∑ai = a1 + a2 + a3 = ax + ay + az . -
3.4 AP Calculus Concavity and the Second Derivative.Notebook November 07, 2016
3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 3.4 Concavity & The Second Derivative Learning Targets 1. Determine intervals on which a function is concave upward or concave downward 2. Find inflection points of a function. 3. Find relative extrema of a function using Second Derivative Test. *no make‐up Monday today Intro/Warmup 1 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 Nov 78:41 AM 2 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫ Concave Upward or Concave Downward If f' is increasing on an interval, then the function is concave upward on that interval If f' is decreasing on an interval, then the function is concave downward on that interval. note: concave upward means the graph lies above its tangent lines, and concave downwards means the graph lies below its tangent lines. I.concave upward or concave downward 3 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫ Concave Upward or Concave Downward continued Test For Concavity: Let f be a function whose second derivative exists on an open interval I. 1. If f''(x) > 0 for all x in I, then the graph of f is concave upward on I. 2. If f''(x) < 0 for all x in I, then the graph of f is concave downward on I. 1. Determine the open intervals on which the graph of is concave upward or downward. I.Concave Upward or Downward cont 4 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫∫∫ Second Derivative Test Second Derivative Test Let f be a function such that f'(c) = 0 and the second derivative of f exists on an open interval containing c.