Math 111 Calculus I

Total Page:16

File Type:pdf, Size:1020Kb

Math 111 Calculus I Math 111 Calculus I R. Mayer ··· there was far more imagination in the head of Archimedes than in that of Homer. Voltaire[46, page 170] Copyright 2007 by Raymond A. Mayer. Any part of the material protected by this copyright notice may be reproduced in any form for any purpose with- out the permission of the copyright owner. Contents Acknowledgements vii 0 Introduction 1 1 Some Notation for Sets 11 2 Some Area Calculations 19 2.1 The Area Under a Power Function . 19 2.2 Some Summation Formulas . 23 2.3 The Area Under a Parabola . 28 2.4 Finite Geometric Series . 32 1 2.5 Area Under the Curve y = x2 . 36 2.6 ∗Area of a Snowflake. 40 3 Propositions and Functions 51 3.1 Propositions . 51 3.2 Sets Defined by Propositions . 56 3.3 Functions . 58 3.4 Summation Notation . 63 3.5 Mathematical Induction . 65 4 Analytic Geometry 68 4.1 Addition of Points . 68 4.2 Reflections, Rotations and Translations . 73 4.3 The Pythagorean Theorem and Distance. 77 5 Area 83 5.1 Basic Assumptions about Area . 84 5.2 Further Assumptions About Area . 87 iii iv CONTENTS 5.3 Monotonic Functions . 89 5.4 Logarithms. 100 5.5 ∗Brouncker’s Formula For ln(2) . 108 5.6 Computer Calculation of Area . 112 6 Limits of Sequences 116 6.1 Absolute Value . 116 6.2 Approximation . 120 6.3 Convergence of Sequences . 122 6.4 Properties of Limits. 127 6.5 Illustrations of the Basic Limit Properties. 133 6.6 Geometric Series . 141 6.7 Calculation of e . 145 7 Still More Area Calculations 151 7.1 Area Under a Monotonic Function . 151 7.2 Calculation of Area under Power Functions . 154 8 Integrable Functions 160 8.1 Definition of the Integral . 160 8.2 Properties of the Integral . 166 8.3 A Non-integrable Function . 174 8.4 ∗The Ruler Function . 176 8.5 Change of Scale . 179 8.6 Integrals and Area . 183 9 Trigonometric Functions 190 9.1 Properties of Sine and Cosine . 190 9.2 Calculation of π . 200 9.3 Integrals of the Trigonometric Functions . 206 9.4 Indefinite Integrals . 212 10 Definition of the Derivative 219 10.1 Velocity and Tangents . 219 10.2 Limits of Functions . 224 10.3 Definition of the Derivative. 230 CONTENTS v 11 Calculation of Derivatives 237 11.1 Derivatives of Some Special Functions . 237 11.2 Some General Differentiation Theorems. 242 11.3 Composition of Functions . 248 12 Extreme Values of Functions 256 12.1 Continuity . 256 12.2 ∗A Nowhere Differentiable Continuous Function. 259 12.3 Maxima and Minima . 261 12.4 The Mean Value Theorem . 267 13 Applications 272 13.1 Curve Sketching . 272 13.2 Optimization Problems. 277 13.3 Rates of Change . 282 14 The Inverse Function Theorem 287 14.1 The Intermediate Value Property . 287 14.2 Applications . 288 14.3 Inverse Functions . 290 14.4 The Exponential Function . 296 14.5 Inverse Function Theorems . 298 14.6 Some Derivative Calculations . 300 15 The Second Derivative 306 15.1 Higher Order Derivatives . 306 15.2 Acceleration . 310 15.3 Convexity . 313 16 Fundamental Theorem of Calculus 320 17 Antidifferentiation Techniques 328 17.1 The Antidifferentiation Problem . 328 17.2 Basic Formulas . 331 17.3 Integration by Parts . 335 17.4 Integration by Substitution . 340 17.5 Trigonometric Substitution . 345 17.6 Substitution in Integrals . 350 17.7 Rational Functions . 353 vi CONTENTS Bibliography 362 A Hints and Answers 367 B Proofs of Some Area Theorems 372 C Prerequisites 375 C.1 Properties of Real Numbers . 375 C.2 Geometrical Prerequisites . 384 D Some Maple Commands 389 E List of Symbols 393 Index 397 Acknowledgements I would like to thank Joe Buhler, David Perkinson, Jamie Pommersheim, Rao Potluri, Joe Roberts, Jerry Shurman, and Steve Swanson for suggesting nu- merous improvements to earlier drafts of these notes. I would also like to thank Cathy D’Ambrosia for converting my handwritten notes into LATEX. vii Chapter 0 Introduction An Overview of the Course In the first part of these notes we consider the problem of calculating the areas of various plane figures. The technique we use for finding the area of a figure A will be to construct a sequence In of sets contained in A, and a sequence On of sets containing A, such that 1. The areas of In and On are easy to calculate. 2. When n is large then both In and On are in some sense “good approxi- mations” for A. Then by examining the areas of In and On we will determine the area of A. The figure below shows the sorts of sets we might take for In and On in the case where A is the set of points in the first quadrant inside of the circle x2 +y2 = 1. 1 1 1 1 n 1 n 1 n 0 1 0 1 0 1 − 1 In On area(On) area(In)= n 1 2 CHAPTER 0. INTRODUCTION In this example, both of the sets In and On are composed of a finite number 1 of rectangles of width , and from the equation of the circle we can calcu- n late the heights of the rectangles, and hence we can find the areas of In and 1 O . From the third figure we see that area(O )− area(I ) = . Hence if n n n n n = 100000, then either of the numbers area(In) or area(On) will give the area of the quarter-circle with an error of no more than 10−5. This calcula- tion will involve taking many square roots, so you probably would not want to carry it out by hand, but with the help of a computer you could easily find the area of the circle to five decimals accuracy. However no amount of computing power would allow you to get thirty decimals of accuracy from this method in a lifetime, and we will need to develop some theory to get better approximations. In some cases we can find exact areas. For example, we will show that the area of one arch of a sine curve is 2, and the area bounded by the parabola 4 y = x2 and the line y = 1 is . 3 y = 1 A B π 2 4 y = sin(x) area(A)=2 y = x area(B)= 3 However in other cases the areas are not simply expressible in terms of known numbers. In these cases we define certain numbers in terms of areas, for example we will define π = the area of a circle of radius 1, and for all numbers a > 1 we will define ln(a) = the area of the region bounded by the curves y = 0, xy = 1, x = 1, and x = a. 3 We will describe methods for calculating these numbers to any degree of ac- curacy, and then we will√ consider them to be known numbers, just as you probably now think of 2 as being a known number. (Many calculators cal- culate these numbers almost as easily as they calculate square roots.) The numbers ln(a) have many interesting properties which we will discuss, and they have many applications to mathematics and science. Often we consider general classes of figures, in which case we want to find a simple formula giving areas for all of the figures in the class. For example we will express the area of the ellipse bounded by the curve whose equation is x2 y2 + = 1 a2 b2 by means of a simple formula involving a and b. b −a a −b The mathematical tools that we develop for calculating areas, (i.e. the theory of integration) have many applications that seem to have little to do with area. Consider a moving object that is acted upon by a known force F (x) that depends on the position x of the object. (For example, a rocket 4 CHAPTER 0. INTRODUCTION propelled upward from the surface of the moon is acted upon by the moon’s gravitational attraction, which is given by C F (x) = , x2 where x is the distance from the rocket to the center of the moon, and C is some constant that can be calculated in terms of the mass of the rocket and known information.) Then the amount of work needed to move the object from a position x = x0 to a position x = x1 is equal to the area of the region bounded by the lines x = x0, x = x1, y = 0 and y = F (x). y=F(x) R R+H Work is represened by an area In the case of the moon rocket, the work needed to raise the rocket a height H above the surface of the moon is the area bounded by the lines x = R, C x = R + H, y = 0, and y = , where R is the radius of the moon. After we x2 have developed a little bit of machinery, this will be an easy area to calculate. The amount of work here determines the amount of fuel necessary to raise the rocket. Some of the ideas used in the theory of integration are thousands of years old. Quite a few of the technical results in the calculations presented in these notes can be found in the writings of Archimedes(287–212 B.C.), although the way the ideas are presented here is not at all like the way they are presented by Archimedes. In the second part of the notes we study the idea of rate of change.
Recommended publications
  • Some Integral Inequalities for Operator Monotonic Functions on Hilbert Spaces Received May 6, 2020; Accepted June 24, 2020
    Spec. Matrices 2020; 8:172–180 Research Article Open Access Silvestru Sever Dragomir* Some integral inequalities for operator monotonic functions on Hilbert spaces https://doi.org/10.1515/spma-2020-0108 Received May 6, 2020; accepted June 24, 2020 Abstract: Let f be an operator monotonic function on I and A, B 2 SAI (H) , the class of all selfadjoint op- erators with spectra in I. Assume that p : [0, 1] ! R is non-decreasing on [0, 1]. In this paper we obtained, among others, that for A ≤ B and f an operator monotonic function on I, Z1 Z1 Z1 0 ≤ p (t) f ((1 − t) A + tB) dt − p (t) dt f ((1 − t) A + tB) dt 0 0 0 1 ≤ [p (1) − p (0)] [f (B) − f (A)] 4 in the operator order. Several other similar inequalities for either p or f is dierentiable, are also provided. Applications for power function and logarithm are given as well. Keywords: Operator monotonic functions, Integral inequalities, Čebyšev inequality, Grüss inequality, Os- trowski inequality MSC: 47A63, 26D15, 26D10. 1 Introduction Consider a complex Hilbert space (H, h·, ·i). An operator T is said to be positive (denoted by T ≥ 0) if hTx, xi ≥ 0 for all x 2 H and also an operator T is said to be strictly positive (denoted by T > 0) if T is positive and invertible. A real valued continuous function f (t) on (0, ∞) is said to be operator monotone if f (A) ≥ f (B) holds for any A ≥ B > 0. In 1934, K. Löwner [7] had given a denitive characterization of operator monotone functions as follows: Theorem 1.
    [Show full text]
  • Pathological Real-Valued Continuous Functions
    Pathological Real-Valued Continuous Functions by Timothy Miao A project submitted to the Department of Mathematical Sciences in conformity with the requirements for Math 4301 (Honour's Seminar) Lakehead University Thunder Bay, Ontario, Canada copyright c (2013) Timothy Miao Abstract We look at continuous functions with pathological properties, in particular, two ex- amples of continuous functions that are nowhere differentiable. The first example was discovered by K. W. T. Weierstrass in 1872 and the second by B. L. Van der Waerden in 1930. We also present an example of a continuous strictly monotonic function with a vanishing derivative almost everywhere, discovered by Zaanen and Luxemburg in 1963. i Acknowledgements I would like to thank Razvan Anisca, my supervisor for this project, and Adam Van Tuyl, the course coordinator for Math 4301. Without their guidance, this project would not have been possible. As well, all the people I have learned from and worked with from the Department of Mathematical Sciences at Lakehead University have contributed to getting me to where I am today. Finally, I am extremely grateful for the support that my family has given during all of my education. ii Contents Abstract i Acknowledgements ii Chapter 1. Introduction 1 Chapter 2. Continuity and Differentiability 2 1. Preliminaries 2 2. Sequences of functions 4 Chapter 3. Two Continuous Functions that are Nowhere Differentiable 6 1. Example given by Weierstrass (1872) 6 2. Example given by Van der Waerden (1930) 10 Chapter 4. Monotonic Functions and their Derivatives 13 1. Preliminaries 13 2. Some notable theorems 14 Chapter 5. A Strictly Monotone Function with a Vanishing Derivative Almost Everywhere 18 1.
    [Show full text]
  • The Fixed-Point Theorem 8 March 2013 Lecturer: Andrew Myers
    CS 6110 Lecture 21 The Fixed-Point Theorem 8 March 2013 Lecturer: Andrew Myers We saw that the semantics of the while command are a fixed point. We also saw that intuitively, the semantics are the limit of a series of approximations capturing a finite number of iterations of the loop, and giving a result of ? for greater numbers of iterations. In order to take a limit, we need greater structure, which led us to define partial orders. But ordering is not enough. 1 Complete partial orders (CPOs) Least upper bounds Given a partial order (S; v), and a subset B ⊆ S, y is an upper bound of B iff 8x 2 B:x v y. In addition, y is a least upper bound iff y is an upper bound and y v z for all upper bounds z of B. We may abbreviate “least upper bound” as LUB or lub. We notate the LUB of a subset B as F B. We may also make this an infix operator, F F writing i21::m xi = x1 t ::: t xm = fxigi21::m. This is also known as the join of elements x1; : : : ; xm. Chains A chain is a pairwise comparable sequence of elements from a partial order (i.e., elements x0; x1; x2 ::: F such that x0 v x1 v x2 v :::). For any finite chain, its LUB is its last element (e.g., xi = xn). Infinite chains (!-chains, i.e. indexed by the natural numbers) may also have LUBs. Complete partial orders A complete partial order (CPO)1 is a partial order in which every chain has a least upper bound.
    [Show full text]
  • Fixpoints and Applications
    H250: Honors Colloquium – Introduction to Computation Fixpoints and Applications Marius Minea [email protected] Fixpoint Definition Given f : X → X, a fixpoint (fixed point) of f is a value c with f (c) = c. A function may have 0, 1, or many fixpoints. Examples: fixpoint for a real-valued function: intersection with y = x fixpoint for a permutation of 1 .. n Think of the function as a transformation (which may be repeated) Fixpoint: no change Intuitive Examples All-pairs shortest paths in graph Simpler: Path relation in graph Questions: When do such computations terminate? How to express this with fixpoints? Partially Ordered Sets Recall: partial order on a set: reflexive antisymmetric transitive A set A together with a partial order v on A is called a partially ordered set (poset) hA, vi Lattices Many familiar partial orders have additional properties: - any two elements have a unique least upper bound (join t) least x with a v x and b v x - any two elements have a unique greatest lower bound (meet u) A poset with these properties is called a lattice. Inductively: any finite set has a least upper / greatest lower bound. Complete lattice: any subset has least upper/greatest lower bound. =⇒ lattice has a top > and bottom ⊥ element. Iterating a function Define f n(x) = f ◦ f ◦ ... ◦ f (x). | {z } n times On a finite set, iteration will - close a cycle, or - reach a fixpoint (particular case) For an infinite set, iteration may be infinite (none of the above) Monotonic Functions Given a poset hS, vi, a function f : S → S is monotonic if it is either - increasing, ∀x∀y : x v y → f (x) v f (y) - decreasing, ∀x∀y : x v y → f (x) w f (y) Knaster-Tarski Fixpoint Theorem A monotonic function on a complete lattice has a least fixpoint and a greatest fixpoint.
    [Show full text]
  • Monotonic Restrictions
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF MATHEnlATICAL ANALYSIS AND APPLICATIONS 41, 384-390 (1973) Monotonic Restrictions CATHERINE V. SMALLWOOD Department of Mathematics, Polytechnic of North London, London, England Submitted by Leon Mirsky 1. In 1935 Erdijs and Szekeres [3] proved that every real sequence of ns + 1 terms possessesa monotonic subsequenceof 71+ 1 terms. (In fact, this result is best possible.) The theorem was subsequently proved very simply by Seidenberg [5] and, more recently, by Blackwell [2]. It implies that every real sequenceof n terms has a monotonic subsequenceof at least dn terms. Thus, every finite sequence of real numbers contains a comparatively long monotonic subsequence.The question naturally arises whether a similar result holds for infinite sequences.Although, of course, every infinite sequence of real numbers contains an infinite monotonic subsequence,we shall see that there are sequencesall of whose monotonic subsequencesare very sparse, with “density function” much less than l/dn. In Section 2 we investigate an analogous problem for real valued functions on an interval. We again find that these may possessonly very small “sets of monotonicity”, though the precise formulation of results depends on the class of functions considered. THEOREM 1. Let fi , fi , f. ,... be a sequence of positive functions on (1,2, 3,...) such that, for each r, fr(n) + co as n -+ co. Then there exists a sequence S of real numberswith the property that, ;f T is any monotonic sub- sequence of S and VT(n) is the number of elements of T amongthejrst n elements of S, w-+0 as n+c0 f An) for I = 1,2,..
    [Show full text]
  • Some Properties of the Set of Discontinuity Points of a Monotonic Function of Several Variables
    Folia Mathematica Acta Universitatis Lodziensis Vol. 11, No. 1, pp. 53–57 c 2004 for University ofL´od´zPress SOME PROPERTIES OF THE SET OF DISCONTINUITY POINTS OF A MONOTONIC FUNCTION OF SEVERAL VARIABLES WANDA LINDNER‡ Abstract. In the note [1] the neccesary and sufficient condition for a set E ⊂ R2 to be the set of discontinuity points for some nondecreasing function f : R2 → R has been proved. The problem of the similar characterization for the function of more than two variables is still open. This paper provides the neccesary condition for the set E ⊂ Rn to be the set of discontinuity points of some nondecreasing function. 1. Introduction n Let x = (x1,x2,...,xn), y = (y1,y2,...,yn) ∈ R . Definition 1. We say, that x ≺ y iff (xi <yi) for every i ∈ {1, 2,...,n}. Definition 2. We say, that x y iff (xi ≤ yi) for every i ∈ {1, 2,...,n}. If one of the following relations occurs: x y or y x we say, that this points are comparable. Definition 3. The function f : Rn → R is called nondecreasing iff for every x, y ∈ Rn x y ⇒ f(x) ≤ f(y). Definition 4. The function f : Rn → R is called increasing iff for every x, y ∈ Rn (x 6= y) ∧ (x y) ⇒ f(x) <f(y). Proposition 1. The function f : Rn → R is nondecreasing (increasing) iff it is nondecreasing (increasing) as the function of one variable, with the others fixed. ‡Technical University ofL´od´z, Institute of Mathematics, Al. Politechniki 11, 90–924 L´od´z. Poland.
    [Show full text]
  • Continuous Random Variables If X Is a Random Variable (Abbreviated to R.V.) Then Its Cumulative Distribution Function (Abbreviated to C.D.F.) F Is Defined By
    MTH4106 Introduction to Statistics Notes 7 Spring 2012 Continuous random variables If X is a random variable (abbreviated to r.v.) then its cumulative distribution function (abbreviated to c.d.f.) F is defined by F(x) = P(X 6 x) for x in R. We write FX (x) if we need to emphasize the random variable X. (Take care to distinguish between X and x in your writing.) We say that X is a continuous random variable if its cdf F is a continuous function. In this case, F is differentiable almost everywhere, and the probability density function (abbreviated to p.d.f.) f of F is defined by dF f (x) = for x in . dx R Again, we write fX (x) if we need to emphasize X. If a < b and X is continuous then Z b P(a < X 6 b) = F(b) − F(a) = f (x)dx a = P(a 6 X 6 b) = P(a < X < b) = P(a 6 X < b). Moreover, Z x F(x) = f (t)dt −∞ and Z ∞ f (t)dt = 1. −∞ The support of a continuous random variable X is {x ∈ R : fX (x) 6= 0}. 1 The median of X is the solution of F(x) = 2 ; the lower quartile of X is the solution 1 3 of F(x) = 4 ; and the upper quartile of X is the solution of F(x) = 4 . The top decile 1 of X is the solution of F(x) = 9/10; this is the point that separates the top tenth of the distribution from the lower nine tenths.
    [Show full text]
  • B, Be Two Real Numbers and Consider a Function F: La,B[ + ~, T + F(T) and a Point to E La,B[
    APPENDIX I DINI DERIVATIVES AND MONOTONIC FUNCTIONS 1. The Dini Derivatives 1.1. Let a, b, a < b, be two real numbers and consider a function f: la,b[ + ~, t + f(t) and a point to E la,b[. We assume that the reader is familiar with the notions of lim sup f (t) and lim inf f (t) . t .... to t + to The same concepts of lim sup and inf, but for t + to + mean simply that one considers, in the limiting processes, only the values of t > to' A similar meaning is attached to Remember that, without regularity assumptions on f, any lim sup or inf exists if we accept the possible values + 00 or - 00. The extended real line will be designated here­ after by !Ji. Thus~ =!Ji' U {_oo} U {+oo}. The four Dini deriv~tives of f at to are now de­ fined by the following equations: + f (t) - f(tO) D f(tO) lim sup t + t o+ t - to f (t) - f(tO) D+f(t ) lim inf O t - t + t o+ to f (t) - f (to) lim sup t _ t t + t o- 0 346 APPENDIX I: DINI DERIVATIVES AND MONOTONIC FUNCTIONS f(t) - f(tO) D_f (to) = lim inf ____--101- t + t o- t - to They are called respectively the upper right, lower right, upper left and lower left derivatives of f at to' Further, the function t + D+f(t) on ]a,b[ into ~ is called the upper right derivative of f on the interval ]a,b[, and similarly for D+, D- and D.
    [Show full text]
  • Several Identities Involving the Falling And
    Acta Univ. Sapientiae, Mathematica, 8, 2 (2016) 282{297 DOI: 10.1515/ausm-2016-0019 Several identities involving the falling and rising factorials and the Cauchy, Lah, and Stirling numbers Feng Qi Institute of Mathematics, Henan Polytechnic University, China College of Mathematics, Inner Mongolia University for Nationalities, China Department of Mathematics, College of Science, Tianjin Polytechnic University, China email: [email protected] Xiao-Ting Shi Fang-Fang Liu Department of Mathematics, Department of Mathematics, College of Science, College of Science, Tianjin Polytechnic University, China Tianjin Polytechnic University, China email: [email protected] email: [email protected] Abstract. In the paper, the authors find several identities, including a new recurrence relation for the Stirling numbers of the first kind, in- volving the falling and rising factorials and the Cauchy, Lah, and Stirling numbers. 1 Notation and main results It is known that, for x 2 R, the quantities x(x - 1) ··· (x - n + 1); n ≥ 1 n-1 Γ(x + 1) hxin = = (x - `) = 1; n = 0 Γ(x - n + 1) `=0 Y 2010 Mathematics Subject Classification: 11B73, 11B83 Key words and phrases: identity, Cauchy number, Lah number, Stirling number, falling factorial, rising factorial, Fa`adi Bruno formula 282 Identities involving Cauchy, Lah, and Stirling numbers 283 and x(x + 1) ··· (x + n - 1); n ≥ 1 n-1 Γ(x + n) (x)n = = (x + `) = 1; n = 0 Γ(x) `=0 Y are respectively called the falling and rising factorials, where n!nz Γ(z) = lim n ; z 2 n f0; -1; -2; : : : g n C k=0(z + k) is the classical gamma!1 function,Q see [1, p.
    [Show full text]
  • A Note on the Main Theorem for Absolutely Monotonic Functions
    On the correctness of the main theorem for absolutely monotonic functions Sitnik S.M. Abstract We prove that the main theorem for absolutely monotonic functions on (0, ∞) from the book Mitrinovi´cD.S., Peˇcari´cJ.E., Fink A.M. "Classical And New Inequalities In Analysis", Kluwer Academic Publishers, 1993, is not valid without additional restrictions. In the classical book [1], chapter XIII, page 365, there is a definition of absolutely monotonic on (0, ∞) functions. Definition. A function f(x) is said to be absolutely monotonic on (0, ∞) if it has derivatives of all orders and k f ( )(x) > 0, x ∈ (0, ∞), k =0, 1, 2,.... (1) For absolutely monotonic functions the next integral representation is essen- tial: ∞ xt f(x)= e dσ(t), (2) Z0 where σ(t) is bounded and nondecreasing and the integral converges for all x ∈ (0, ∞). Also the basic set of inequalities is considered. Let f(x) be an absolutely monotonic function on (0, ∞). Then k k k 2 f ( )(x)f ( +2)(x) > f ( +1)(x) , k =0, 1, 2,.... (3) After this definition in the book [1] the result which we classify as the main theorem for absolutely monotonic functions on (0, ∞) is formulated (theorem 1, page 366). arXiv:1202.1210v2 [math.CA] 15 Aug 2014 The main theorem for absolutely monotonic functions. The above definition (1), integral representation (2) and basic set of inequal- ities (3) are equivalent. It means: (1) ⇔ (2) ⇔ (3). (4) In the book [1] for (1) ⇔ (2) the reference is given to [2], and an equivalence (2) ⇔ (3) is proved, it is in fact a consequence of Chebyschev inequality.
    [Show full text]
  • Integration with Respect to Functions of Unbounded
    INTEGRATION WITH RESPECT TO FUNCTIONS OF UNBOUNDED VARIATION J.K. Brooks - D.Candeloro 31/12/2001 1 Introduction R b In this paper we shall study the problem of constructing an integral (C) a fdg, where f and g are real valued functions defined on an interval [a, b] and g is a continuous function, not necessarily of bounded variation. Of special interest is the case when B is normalized Brownian motion on [0,T ] and g is the continuous Brown- ian sample path B(·, ω). One of our main results is that if f is continuous, then R T f(B(·, ω)) is B(·, ω)−integrable (Definition 5.5) and (C) 0 f(B(t, ω))dB(t, ω) is equal to ((S) R T −0f(B)dB)(ω), the Stratonovitch stochastic integral, for each path ω. Thus for such integrands, a pathwise integration is possible for the Stratonovitch stochastic integral. As a result, if f ∈ C1, then by means of Itˆo’s formula, a pathwise integration R t can be given for (I) 0 f(B)dB, the Itˆo stochastic integral (section 2). The (C) appearing before the above integrals is in honor of R. Caccioppoli, who studied in [1] an integration theory with respect to continuous functions of un- bounded variation. Although Caccioppoli’s paper has serious gaps, he formulated an ingenious approach involving an approximating sequence (gn) of continuous func- tions of bounded variation which converges to g. In our paper we shall present a construction of a “generating sequence” of functions (gn) which occur in [1].
    [Show full text]
  • Single Crossing Differences on Distributions
    Single-Crossing Differences on Distributions∗ Navin Kartiky SangMok Leez Daniel Rappoportx November 2, 2018 Abstract We study when choices among lotteries are monotonic (with respect to some or- der) in an expected-utility agent’s preference parameter or type. The requisite property turns out to be that the expected utility difference between any pair of lotteries is single- crossing in the agent’s type. We characterize the set of utility functions that have this property; we identify the orders over lotteries that generate choice monotonicity for any such utility function. We discuss applications to cheap-talk games, costly signal- ing games, and collective choice problems. Our analysis provides some new results on monotone comparative statics and aggregating single-crossing functions. Keywords: monotone comparative statics, choice under uncertainty, interval equilibria ∗We thank Nageeb Ali, Federico Echenique, Mira Frick, Ben Golub, Ryota Iijima, Ian Jewitt, Alexey Kush- nir, Shuo Liu, Daniele Pennesi, Jacopo Perego, Lones Smith, Bruno Strulovici, and various audiences for helpful comments. yDepartment of Economics, Columbia University. E-mail: [email protected] zDepartment of Economics, Washington University in St. Louis. E-mail: [email protected] xBooth School of Business, University of Chicago. E-mail: [email protected] 1 Contents 1. Introduction3 1.1. Overview.......................................3 1.2. Related Literature..................................6 2. Main Results8 2.1. Single-Crossing Expectational Differences....................8 2.2. Strict Single-Crossing Expectational Differences................ 17 2.3. Monotone Comparative Statics.......................... 17 3. Applications 20 3.1. Cheap Talk with Uncertain Receiver Preferences................ 20 3.2. Collective Choice.................................. 23 3.3. Costly Signaling................................... 25 4. Extensions 28 4.1.
    [Show full text]