Continuous Functions
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Infinitesimal and Tangent to Polylogarithmic Complexes For
AIMS Mathematics, 4(4): 1248–1257. DOI:10.3934/math.2019.4.1248 Received: 11 June 2019 Accepted: 11 August 2019 http://www.aimspress.com/journal/Math Published: 02 September 2019 Research article Infinitesimal and tangent to polylogarithmic complexes for higher weight Raziuddin Siddiqui* Department of Mathematical Sciences, Institute of Business Administration, Karachi, Pakistan * Correspondence: Email: [email protected]; Tel: +92-213-810-4700 Abstract: Motivic and polylogarithmic complexes have deep connections with K-theory. This article gives morphisms (different from Goncharov’s generalized maps) between |-vector spaces of Cathelineau’s infinitesimal complex for weight n. Our morphisms guarantee that the sequence of infinitesimal polylogs is a complex. We are also introducing a variant of Cathelineau’s complex with the derivation map for higher weight n and suggesting the definition of tangent group TBn(|). These tangent groups develop the tangent to Goncharov’s complex for weight n. Keywords: polylogarithm; infinitesimal complex; five term relation; tangent complex Mathematics Subject Classification: 11G55, 19D, 18G 1. Introduction The classical polylogarithms represented by Lin are one valued functions on a complex plane (see [11]). They are called generalization of natural logarithms, which can be represented by an infinite series (power series): X1 zk Li (z) = = − ln(1 − z) 1 k k=1 X1 zk Li (z) = 2 k2 k=1 : : X1 zk Li (z) = for z 2 ¼; jzj < 1 n kn k=1 The other versions of polylogarithms are Infinitesimal (see [8]) and Tangential (see [9]). We will discuss group theoretic form of infinitesimal and tangential polylogarithms in x 2.3, 2.4 and 2.5 below. -
Analysis of Functions of a Single Variable a Detailed Development
ANALYSIS OF FUNCTIONS OF A SINGLE VARIABLE A DETAILED DEVELOPMENT LAWRENCE W. BAGGETT University of Colorado OCTOBER 29, 2006 2 For Christy My Light i PREFACE I have written this book primarily for serious and talented mathematics scholars , seniors or first-year graduate students, who by the time they finish their schooling should have had the opportunity to study in some detail the great discoveries of our subject. What did we know and how and when did we know it? I hope this book is useful toward that goal, especially when it comes to the great achievements of that part of mathematics known as analysis. I have tried to write a complete and thorough account of the elementary theories of functions of a single real variable and functions of a single complex variable. Separating these two subjects does not at all jive with their development historically, and to me it seems unnecessary and potentially confusing to do so. On the other hand, functions of several variables seems to me to be a very different kettle of fish, so I have decided to limit this book by concentrating on one variable at a time. Everyone is taught (told) in school that the area of a circle is given by the formula A = πr2: We are also told that the product of two negatives is a positive, that you cant trisect an angle, and that the square root of 2 is irrational. Students of natural sciences learn that eiπ = 1 and that sin2 + cos2 = 1: More sophisticated students are taught the Fundamental− Theorem of calculus and the Fundamental Theorem of Algebra. -
Calculus for the Life Sciences I Lecture Notes – Limits, Continuity, and the Derivative
Limits Continuity Derivative Calculus for the Life Sciences I Lecture Notes – Limits, Continuity, and the Derivative Joseph M. Mahaffy, [email protected] Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research Center San Diego State University San Diego, CA 92182-7720 http://www-rohan.sdsu.edu/∼jmahaffy Spring 2013 Lecture Notes – Limits, Continuity, and the Deriv Joseph M. Mahaffy, [email protected] — (1/24) Limits Continuity Derivative Outline 1 Limits Definition Examples of Limit 2 Continuity Examples of Continuity 3 Derivative Examples of a derivative Lecture Notes – Limits, Continuity, and the Deriv Joseph M. Mahaffy, [email protected] — (2/24) Limits Definition Continuity Examples of Limit Derivative Introduction Limits are central to Calculus Lecture Notes – Limits, Continuity, and the Deriv Joseph M. Mahaffy, [email protected] — (3/24) Limits Definition Continuity Examples of Limit Derivative Introduction Limits are central to Calculus Present definitions of limits, continuity, and derivative Lecture Notes – Limits, Continuity, and the Deriv Joseph M. Mahaffy, [email protected] — (3/24) Limits Definition Continuity Examples of Limit Derivative Introduction Limits are central to Calculus Present definitions of limits, continuity, and derivative Sketch the formal mathematics for these definitions Lecture Notes – Limits, Continuity, and the Deriv Joseph M. Mahaffy, [email protected] — (3/24) Limits Definition Continuity Examples of Limit Derivative Introduction Limits -
Hegel on Calculus
HISTORY OF PHILOSOPHY QUARTERLY Volume 34, Number 4, October 2017 HEGEL ON CALCULUS Ralph M. Kaufmann and Christopher Yeomans t is fair to say that Georg Wilhelm Friedrich Hegel’s philosophy of Imathematics and his interpretation of the calculus in particular have not been popular topics of conversation since the early part of the twenti- eth century. Changes in mathematics in the late nineteenth century, the new set-theoretical approach to understanding its foundations, and the rise of a sympathetic philosophical logic have all conspired to give prior philosophies of mathematics (including Hegel’s) the untimely appear- ance of naïveté. The common view was expressed by Bertrand Russell: The great [mathematicians] of the seventeenth and eighteenth cen- turies were so much impressed by the results of their new methods that they did not trouble to examine their foundations. Although their arguments were fallacious, a special Providence saw to it that their conclusions were more or less true. Hegel fastened upon the obscuri- ties in the foundations of mathematics, turned them into dialectical contradictions, and resolved them by nonsensical syntheses. .The resulting puzzles [of mathematics] were all cleared up during the nine- teenth century, not by heroic philosophical doctrines such as that of Kant or that of Hegel, but by patient attention to detail (1956, 368–69). Recently, however, interest in Hegel’s discussion of calculus has been awakened by an unlikely source: Gilles Deleuze. In particular, work by Simon Duffy and Henry Somers-Hall has demonstrated how close Deleuze and Hegel are in their treatment of the calculus as compared with most other philosophers of mathematics. -
2. the Tangent Line
2. The Tangent Line The tangent line to a circle at a point P on its circumference is the line perpendicular to the radius of the circle at P. In Figure 1, The line T is the tangent line which is Tangent Line perpendicular to the radius of the circle at the point P. T Figure 1: A Tangent Line to a Circle While the tangent line to a circle has the property that it is perpendicular to the radius at the point of tangency, it is not this property which generalizes to other curves. We shall make an observation about the tangent line to the circle which is carried over to other curves, and may be used as its defining property. Let us look at a specific example. Let the equation of the circle in Figure 1 be x22 + y = 25. We may easily determine the equation of the tangent line to this circle at the point P(3,4). First, we observe that the radius is a segment of the line passing through the origin (0, 0) and P(3,4), and its equation is (why?). Since the tangent line is perpendicular to this line, its slope is -3/4 and passes through P(3, 4), using the point-slope formula, its equation is found to be Let us compute y-values on both the tangent line and the circle for x-values near the point P(3, 4). Note that near P, we can solve for the y-value on the upper half of the circle which is found to be When x = 3.01, we find the y-value on the tangent line is y = -3/4(3.01) + 25/4 = 3.9925, while the corresponding value on the circle is (Note that the tangent line lie above the circle, so its y-value was expected to be a larger.) In Table 1, we indicate other corresponding values as we vary x near P. -
Limits and Derivatives 2
57425_02_ch02_p089-099.qk 11/21/08 10:34 AM Page 89 FPO thomasmayerarchive.com Limits and Derivatives 2 In A Preview of Calculus (page 3) we saw how the idea of a limit underlies the various branches of calculus. Thus it is appropriate to begin our study of calculus by investigating limits and their properties. The special type of limit that is used to find tangents and velocities gives rise to the central idea in differential calcu- lus, the derivative. We see how derivatives can be interpreted as rates of change in various situations and learn how the derivative of a function gives information about the original function. 89 57425_02_ch02_p089-099.qk 11/21/08 10:35 AM Page 90 90 CHAPTER 2 LIMITS AND DERIVATIVES 2.1 The Tangent and Velocity Problems In this section we see how limits arise when we attempt to find the tangent to a curve or the velocity of an object. The Tangent Problem The word tangent is derived from the Latin word tangens, which means “touching.” Thus t a tangent to a curve is a line that touches the curve. In other words, a tangent line should have the same direction as the curve at the point of contact. How can this idea be made precise? For a circle we could simply follow Euclid and say that a tangent is a line that intersects the circle once and only once, as in Figure 1(a). For more complicated curves this defini- tion is inadequate. Figure l(b) shows two lines and tl passing through a point P on a curve (a) C. -
SHEET 14: LINEAR ALGEBRA 14.1 Vector Spaces
SHEET 14: LINEAR ALGEBRA Throughout this sheet, let F be a field. In examples, you need only consider the field F = R. 14.1 Vector spaces Definition 14.1. A vector space over F is a set V with two operations, V × V ! V :(x; y) 7! x + y (vector addition) and F × V ! V :(λ, x) 7! λx (scalar multiplication); that satisfy the following axioms. 1. Addition is commutative: x + y = y + x for all x; y 2 V . 2. Addition is associative: x + (y + z) = (x + y) + z for all x; y; z 2 V . 3. There is an additive identity 0 2 V satisfying x + 0 = x for all x 2 V . 4. For each x 2 V , there is an additive inverse −x 2 V satisfying x + (−x) = 0. 5. Scalar multiplication by 1 fixes vectors: 1x = x for all x 2 V . 6. Scalar multiplication is compatible with F :(λµ)x = λ(µx) for all λ, µ 2 F and x 2 V . 7. Scalar multiplication distributes over vector addition and over scalar addition: λ(x + y) = λx + λy and (λ + µ)x = λx + µx for all λ, µ 2 F and x; y 2 V . In this context, elements of F are called scalars and elements of V are called vectors. Definition 14.2. Let n be a nonnegative integer. The coordinate space F n = F × · · · × F is the set of all n-tuples of elements of F , conventionally regarded as column vectors. Addition and scalar multiplication are defined componentwise; that is, 2 3 2 3 2 3 2 3 x1 y1 x1 + y1 λx1 6x 7 6y 7 6x + y 7 6λx 7 6 27 6 27 6 2 2 7 6 27 if x = 6 . -
1101 Calculus I Lecture 2.1: the Tangent and Velocity Problems
Calculus Lecture 2.1: The Tangent and Velocity Problems Page 1 1101 Calculus I Lecture 2.1: The Tangent and Velocity Problems The Tangent Problem A good way to think of what the tangent line to a curve is that it is a straight line which approximates the curve well in the region where it touches the curve. A more precise definition will be developed later. Recall, straight lines have equations y = mx + b (slope m, y-intercept b), or, more useful in this case, y − y0 = m(x − x0) (slope m, and passes through the point (x0, y0)). Your text has a fairly simple example. I will do something more complex instead. Example Find the tangent line to the parabola y = −3x2 + 12x − 8 at the point P (3, 1). Our solution involves finding the equation of a straight line, which is y − y0 = m(x − x0). We already know the tangent line should touch the curve, so it will pass through the point P (3, 1). This means x0 = 3 and y0 = 1. We now need to determine the slope of the tangent line, m. But we need two points to determine the slope of a line, and we only know one. We only know that the tangent line passes through the point P (3, 1). We proceed by approximations. We choose a point on the parabola that is nearby (3,1) and use it to approximate the slope of the tangent line. Let’s draw a sketch. Choose a point close to P (3, 1), say Q(4, −8). -
Two Fundamental Theorems About the Definite Integral
Two Fundamental Theorems about the Definite Integral These lecture notes develop the theorem Stewart calls The Fundamental Theorem of Calculus in section 5.3. The approach I use is slightly different than that used by Stewart, but is based on the same fundamental ideas. 1 The definite integral Recall that the expression b f(x) dx ∫a is called the definite integral of f(x) over the interval [a,b] and stands for the area underneath the curve y = f(x) over the interval [a,b] (with the understanding that areas above the x-axis are considered positive and the areas beneath the axis are considered negative). In today's lecture I am going to prove an important connection between the definite integral and the derivative and use that connection to compute the definite integral. The result that I am eventually going to prove sits at the end of a chain of earlier definitions and intermediate results. 2 Some important facts about continuous functions The first intermediate result we are going to have to prove along the way depends on some definitions and theorems concerning continuous functions. Here are those definitions and theorems. The definition of continuity A function f(x) is continuous at a point x = a if the following hold 1. f(a) exists 2. lim f(x) exists xœa 3. lim f(x) = f(a) xœa 1 A function f(x) is continuous in an interval [a,b] if it is continuous at every point in that interval. The extreme value theorem Let f(x) be a continuous function in an interval [a,b]. -
The Axiom of Choice and Its Implications
THE AXIOM OF CHOICE AND ITS IMPLICATIONS KEVIN BARNUM Abstract. In this paper we will look at the Axiom of Choice and some of the various implications it has. These implications include a number of equivalent statements, and also some less accepted ideas. The proofs discussed will give us an idea of why the Axiom of Choice is so powerful, but also so controversial. Contents 1. Introduction 1 2. The Axiom of Choice and Its Equivalents 1 2.1. The Axiom of Choice and its Well-known Equivalents 1 2.2. Some Other Less Well-known Equivalents of the Axiom of Choice 3 3. Applications of the Axiom of Choice 5 3.1. Equivalence Between The Axiom of Choice and the Claim that Every Vector Space has a Basis 5 3.2. Some More Applications of the Axiom of Choice 6 4. Controversial Results 10 Acknowledgments 11 References 11 1. Introduction The Axiom of Choice states that for any family of nonempty disjoint sets, there exists a set that consists of exactly one element from each element of the family. It seems strange at first that such an innocuous sounding idea can be so powerful and controversial, but it certainly is both. To understand why, we will start by looking at some statements that are equivalent to the axiom of choice. Many of these equivalences are very useful, and we devote much time to one, namely, that every vector space has a basis. We go on from there to see a few more applications of the Axiom of Choice and its equivalents, and finish by looking at some of the reasons why the Axiom of Choice is so controversial. -
17 Axiom of Choice
Math 361 Axiom of Choice 17 Axiom of Choice De¯nition 17.1. Let be a nonempty set of nonempty sets. Then a choice function for is a function f sucFh that f(S) S for all S . F 2 2 F Example 17.2. Let = (N)r . Then we can de¯ne a choice function f by F P f;g f(S) = the least element of S: Example 17.3. Let = (Z)r . Then we can de¯ne a choice function f by F P f;g f(S) = ²n where n = min z z S and, if n = 0, ² = min z= z z = n; z S . fj j j 2 g 6 f j j j j j 2 g Example 17.4. Let = (Q)r . Then we can de¯ne a choice function f as follows. F P f;g Let g : Q N be an injection. Then ! f(S) = q where g(q) = min g(r) r S . f j 2 g Example 17.5. Let = (R)r . Then it is impossible to explicitly de¯ne a choice function for . F P f;g F Axiom 17.6 (Axiom of Choice (AC)). For every set of nonempty sets, there exists a function f such that f(S) S for all S . F 2 2 F We say that f is a choice function for . F Theorem 17.7 (AC). If A; B are non-empty sets, then the following are equivalent: (a) A B ¹ (b) There exists a surjection g : B A. ! Proof. (a) (b) Suppose that A 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