Calculus Deconstructed a Second Course in First-Year Calculus
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Calculus and Differential Equations II
Calculus and Differential Equations II MATH 250 B Sequences and series Sequences and series Calculus and Differential Equations II Sequences A sequence is an infinite list of numbers, s1; s2;:::; sn;::: , indexed by integers. 1n Example 1: Find the first five terms of s = (−1)n , n 3 n ≥ 1. Example 2: Find a formula for sn, n ≥ 1, given that its first five terms are 0; 2; 6; 14; 30. Some sequences are defined recursively. For instance, sn = 2 sn−1 + 3, n > 1, with s1 = 1. If lim sn = L, where L is a number, we say that the sequence n!1 (sn) converges to L. If such a limit does not exist or if L = ±∞, one says that the sequence diverges. Sequences and series Calculus and Differential Equations II Sequences (continued) 2n Example 3: Does the sequence converge? 5n 1 Yes 2 No n 5 Example 4: Does the sequence + converge? 2 n 1 Yes 2 No sin(2n) Example 5: Does the sequence converge? n Remarks: 1 A convergent sequence is bounded, i.e. one can find two numbers M and N such that M < sn < N, for all n's. 2 If a sequence is bounded and monotone, then it converges. Sequences and series Calculus and Differential Equations II Series A series is a pair of sequences, (Sn) and (un) such that n X Sn = uk : k=1 A geometric series is of the form 2 3 n−1 k−1 Sn = a + ax + ax + ax + ··· + ax ; uk = ax 1 − xn One can show that if x 6= 1, S = a . -
1 Mean Value Theorem 1 1.1 Applications of the Mean Value Theorem
Seunghee Ye Ma 8: Week 5 Oct 28 Week 5 Summary In Section 1, we go over the Mean Value Theorem and its applications. In Section 2, we will recap what we have covered so far this term. Topics Page 1 Mean Value Theorem 1 1.1 Applications of the Mean Value Theorem . .1 2 Midterm Review 5 2.1 Proof Techniques . .5 2.2 Sequences . .6 2.3 Series . .7 2.4 Continuity and Differentiability of Functions . .9 1 Mean Value Theorem The Mean Value Theorem is the following result: Theorem 1.1 (Mean Value Theorem). Let f be a continuous function on [a; b], which is differentiable on (a; b). Then, there exists some value c 2 (a; b) such that f(b) − f(a) f 0(c) = b − a Intuitively, the Mean Value Theorem is quite trivial. Say we want to drive to San Francisco, which is 380 miles from Caltech according to Google Map. If we start driving at 8am and arrive at 12pm, we know that we were driving over the speed limit at least once during the drive. This is exactly what the Mean Value Theorem tells us. Since the distance travelled is a continuous function of time, we know that there is a point in time when our speed was ≥ 380=4 >>> speed limit. As we can see from this example, the Mean Value Theorem is usually not a tough theorem to understand. The tricky thing is realizing when you should try to use it. Roughly speaking, we use the Mean Value Theorem when we want to turn the information about a function into information about its derivative, or vice-versa. -
3.3 Convergence Tests for Infinite Series
3.3 Convergence Tests for Infinite Series 3.3.1 The integral test We may plot the sequence an in the Cartesian plane, with independent variable n and dependent variable a: n X The sum an can then be represented geometrically as the area of a collection of rectangles with n=1 height an and width 1. This geometric viewpoint suggests that we compare this sum to an integral. If an can be represented as a continuous function of n, for real numbers n, not just integers, and if the m X sequence an is decreasing, then an looks a bit like area under the curve a = a(n). n=1 In particular, m m+2 X Z m+1 X an > an dn > an n=1 n=1 n=2 For example, let us examine the first 10 terms of the harmonic series 10 X 1 1 1 1 1 1 1 1 1 1 = 1 + + + + + + + + + : n 2 3 4 5 6 7 8 9 10 1 1 1 If we draw the curve y = x (or a = n ) we see that 10 11 10 X 1 Z 11 dx X 1 X 1 1 > > = − 1 + : n x n n 11 1 1 2 1 (See Figure 1, copied from Wikipedia) Z 11 dx Now = ln(11) − ln(1) = ln(11) so 1 x 10 X 1 1 1 1 1 1 1 1 1 1 = 1 + + + + + + + + + > ln(11) n 2 3 4 5 6 7 8 9 10 1 and 1 1 1 1 1 1 1 1 1 1 1 + + + + + + + + + < ln(11) + (1 − ): 2 3 4 5 6 7 8 9 10 11 Z dx So we may bound our series, above and below, with some version of the integral : x If we allow the sum to turn into an infinite series, we turn the integral into an improper integral. -
What Is on Today 1 Alternating Series
MA 124 (Calculus II) Lecture 17: March 28, 2019 Section A3 Professor Jennifer Balakrishnan, [email protected] What is on today 1 Alternating series1 1 Alternating series Briggs-Cochran-Gillett x8:6 pp. 649 - 656 We begin by reviewing the Alternating Series Test: P k+1 Theorem 1 (Alternating Series Test). The alternating series (−1) ak converges if 1. the terms of the series are nonincreasing in magnitude (0 < ak+1 ≤ ak, for k greater than some index N) and 2. limk!1 ak = 0. For series of positive terms, limk!1 ak = 0 does NOT imply convergence. For alter- nating series with nonincreasing terms, limk!1 ak = 0 DOES imply convergence. Example 2 (x8.6 Ex 16, 20, 24). Determine whether the following series converge. P1 (−1)k 1. k=0 k2+10 P1 1 k 2. k=0 − 5 P1 (−1)k 3. k=2 k ln2 k 1 MA 124 (Calculus II) Lecture 17: March 28, 2019 Section A3 Recall that if a series converges to a value S, then the remainder is Rn = S − Sn, where Sn is the sum of the first n terms of the series. An upper bound on the magnitude of the remainder (the absolute error) in an alternating series arises form the following observation: when the terms are nonincreasing in magnitude, the value of the series is always trapped between successive terms of the sequence of partial sums. Thus we have jRnj = jS − Snj ≤ jSn+1 − Snj = an+1: This justifies the following theorem: P1 k+1 Theorem 3 (Remainder in Alternating Series). -
Series: Convergence and Divergence Comparison Tests
Series: Convergence and Divergence Here is a compilation of what we have done so far (up to the end of October) in terms of convergence and divergence. • Series that we know about: P∞ n Geometric Series: A geometric series is a series of the form n=0 ar . The series converges if |r| < 1 and 1 a1 diverges otherwise . If |r| < 1, the sum of the entire series is 1−r where a is the first term of the series and r is the common ratio. P∞ 1 2 p-Series Test: The series n=1 np converges if p1 and diverges otherwise . P∞ • Nth Term Test for Divergence: If limn→∞ an 6= 0, then the series n=1 an diverges. Note: If limn→∞ an = 0 we know nothing. It is possible that the series converges but it is possible that the series diverges. Comparison Tests: P∞ • Direct Comparison Test: If a series n=1 an has all positive terms, and all of its terms are eventually bigger than those in a series that is known to be divergent, then it is also divergent. The reverse is also true–if all the terms are eventually smaller than those of some convergent series, then the series is convergent. P P P That is, if an, bn and cn are all series with positive terms and an ≤ bn ≤ cn for all n sufficiently large, then P P if cn converges, then bn does as well P P if an diverges, then bn does as well. (This is a good test to use with rational functions. -
The Analytic-Synthetic Distinction and the Classical Model of Science: Kant, Bolzano and Frege
Synthese (2010) 174:237–261 DOI 10.1007/s11229-008-9420-9 The analytic-synthetic distinction and the classical model of science: Kant, Bolzano and Frege Willem R. de Jong Received: 10 April 2007 / Revised: 24 July 2007 / Accepted: 1 April 2008 / Published online: 8 November 2008 © The Author(s) 2008. This article is published with open access at Springerlink.com Abstract This paper concentrates on some aspects of the history of the analytic- synthetic distinction from Kant to Bolzano and Frege. This history evinces con- siderable continuity but also some important discontinuities. The analytic-synthetic distinction has to be seen in the first place in relation to a science, i.e. an ordered system of cognition. Looking especially to the place and role of logic it will be argued that Kant, Bolzano and Frege each developed the analytic-synthetic distinction within the same conception of scientific rationality, that is, within the Classical Model of Science: scientific knowledge as cognitio ex principiis. But as we will see, the way the distinction between analytic and synthetic judgments or propositions functions within this model turns out to differ considerably between them. Keywords Analytic-synthetic · Science · Logic · Kant · Bolzano · Frege 1 Introduction As is well known, the critical Kant is the first to apply the analytic-synthetic distinction to such things as judgments, sentences or propositions. For Kant this distinction is not only important in his repudiation of traditional, so-called dogmatic, metaphysics, but it is also crucial in his inquiry into (the possibility of) metaphysics as a rational science. Namely, this distinction should be “indispensable with regard to the critique of human understanding, and therefore deserves to be classical in it” (Kant 1783, p. -
Bernard Bolzano. Theory of Science
428 • Philosophia Mathematica Menzel, Christopher [1991] ‘The true modal logic’, Journal of Philosophical Logic 20, 331–374. ———[1993]: ‘Singular propositions and modal logic’, Philosophical Topics 21, 113–148. ———[2008]: ‘Actualism’, in Edward N. Zalta, ed., Stanford Encyclopedia of Philosophy (Winter 2008 Edition). Stanford University. http://plato.stanford.edu/archives/win2008/entries/actualism, last accessed June 2015. Nelson, Michael [2009]: ‘The contingency of existence’, in L.M. Jorgensen and S. Newlands, eds, Metaphysics and the Good: Themes from the Philosophy of Robert Adams, Chapter 3, pp. 95–155. Oxford University Press. Downloaded from https://academic.oup.com/philmat/article/23/3/428/1449457 by guest on 30 September 2021 Parsons, Charles [1983]: Mathematics in Philosophy: Selected Essays. New York: Cornell University Press. Plantinga, Alvin [1979]: ‘Actualism and possible worlds’, in Michael Loux, ed., The Possible and the Actual, pp. 253–273. Ithaca: Cornell University Press. ———[1983]: ‘On existentialism’, Philosophical Studies 44, 1–20. Prior, Arthur N. [1956]: ‘Modality and quantification in S5’, The Journal of Symbolic Logic 21, 60–62. ———[1957]: Time and Modality. Oxford: Clarendon Press. ———[1968]: Papers on Time and Tense. Oxford University Press. Quine, W.V. [1951]: ‘Two dogmas of empiricism’, Philosophical Review 60, 20–43. ———[1969]: Ontological Relativity and Other Essays. New York: Columbia University Press. ———[1986]: Philosophy of Logic. 2nd ed. Cambridge, Mass.: Harvard University Press. Shapiro, Stewart [1991]: Foundations without Foundationalism: A Case for Second-order Logic.Oxford University Press. Stalnaker, Robert [2012]: Mere Possibilities: Metaphysical Foundations of Modal Semantics. Princeton, N.J.: Princeton University Press. Turner, Jason [2005]: ‘Strong and weak possibility’, Philosophical Studies 125, 191–217. -
When Algebraic Entropy Vanishes Jim Propp (U. Wisconsin) Tufts
When algebraic entropy vanishes Jim Propp (U. Wisconsin) Tufts University October 31, 2003 1 I. Overview P robabilistic DYNAMICS ↑ COMBINATORICS ↑ Algebraic DYNAMICS 2 II. Rational maps Projective space: CPn = (Cn+1 \ (0, 0,..., 0)) / ∼, where u ∼ v iff v = cu for some c =6 0. We write the equivalence class of (x1, x2, . , xn+1) n in CP as (x1 : x2 : ... : xn+1). The standard imbedding of affine n-space into projective n-space is (x1, x2, . , xn) 7→ (x1 : x2 : ... : xn : 1). The “inverse map” is (x : ... : x : x ) 7→ ( x1 ,..., xn ). 1 n n+1 xn+1 xn+1 3 Geometrical version: CPn is the set of lines through the origin in (n + 1)-space. n The point (a1 : a2 : ... : an+1) in CP cor- responds to the line a1x1 = a2x2 = ··· = n+1 an+1xn+1 in C . The intersection of this line with the hyperplane xn+1 = 1 is the point a a a ( 1 , 2 ,..., n , 1) an+1 an+1 an+1 (as long as an+1 =6 0). We identity affine n-space with the hyperplane xn+1 = 1. Affine n-space is a Zariski-dense subset of pro- jective n-space. 4 A rational map is a function from (a Zariski-dense subset of) CPn to CPm given by m rational functions of the affine coordinate variables, or, the associated function from a Zariski-dense sub- set of Cn to CPm (e.g., the “function” x 7→ 1/x on affine 1-space, associated with the function (x : y) 7→ (y : x) on projective 1-space). -
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 -
From the Editor
Department of Mathematics University of Wisconsin From the Editor. This year’s biggest news is the awarding of the National Medal of Science to Carl de Boor, professor emeritus of mathematics and computer science. Accompanied by his family, Professor de Boor received the medal at a White House ceremony on March 14, 2005. Carl was one of 14 new National Medal of Science Lau- reates, the only one in the category of mathematics. The award, administered by the National Science Foundation originated with the 1959 Congress. It honors individuals for pioneering research that has led to a better under- standing of the world, as well as to innovations and tech- nologies that give the USA a global economic edge. Carl is an authority on the theory and application of splines, which play a central role in, among others, computer- aided design and manufacturing, applications of com- puter graphics, and signal and image processing. The new dean of the College of Letters and Science, Gary Sandefur, said “Carl de Boor’s selection for the na- tion’s highest scientific award reflects the significance of his work and the tradition of excellence among our mathematics and computer science faculty.” We in the Department of Mathematics are extremely proud of Carl de Boor. Carl retired from the University in 2003 as Steen- bock Professor of Mathematical Sciences and now lives in Washington State, although he keeps a small condo- minium in Madison. Last year’s newsletter contains in- Carl de Boor formation about Carl’s distinguished career and a 65th birthday conference held in his honor in Germany. -
Bolzano and the Traditions of Analysis
Bolzano and the Traditions of Analysis Paul Rusnock (Appeared in Grazer Phil. Studien 53 (1997) 61-86.) §1 Russell’s discussion of analytic philosophy in his popular History begins on a sur- prising note: the first analytic philosopher he mentions is . Weierstrass. His fur- ther remarks—in which he discusses Cantor and Frege, singling out their work in the foundations of mathematics—indicate that he thought that the origin of mod- ern philosophical analysis lay in the elaboration of modern mathematical analysis in the nineteenth century [13, 829-30]. Given the markedly different meanings attached to the word “analysis” in these two contexts, this juxtaposition might be dismissed as merely an odd coincidence. As it turns out, however, modern philo- sophical and mathematical analysis are rather closely linked. They have, for one thing, a common root, albeit one long since buried and forgotten. More important still, and apparently unknown to Russell, is the circumstance that one individual was instrumental in the creation of both: Bolzano. Russell’s account could easily leave one with the impression that analytic phi- losophy had no deep roots in philosophical tradition; that, instead, it emerged when methods and principles used more or less tacitly in mathematics were, af- ter long use, finally articulated and brought to the attention of the philosophical public. A most misleading impression this would be. For right at the begin- ning of the reconstruction of the calculus which Russell attributed to Weierstrass we find Bolzano setting out with great clarity the methodology guiding these de- velopments in mathematics—a methodology which, far from being rootless, was developed in close conjunction with Bolzano’s usual critical survey of the relevant philosophical literature. -
Reason, Causation and Compatibility with the Phenomena
Reason, causation and compatibility with the phenomena Basil Evangelidis Series in Philosophy Copyright © 2020 Vernon Press, an imprint of Vernon Art and Science Inc, on behalf of the author. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Vernon Art and Science Inc. www.vernonpress.com In the Americas: In the rest of the world: Vernon Press Vernon Press 1000 N West Street, C/Sancti Espiritu 17, Suite 1200, Wilmington, Malaga, 29006 Delaware 19801 Spain United States Series in Philosophy Library of Congress Control Number: 2019942259 ISBN: 978-1-62273-755-0 Cover design by Vernon Press. Cover image by Garik Barseghyan from Pixabay. Product and company names mentioned in this work are the trademarks of their respective owners. While every care has been taken in preparing this work, neither the authors nor Vernon Art and Science Inc. may be held responsible for any loss or damage caused or alleged to be caused directly or indirectly by the information contained in it. Every effort has been made to trace all copyright holders, but if any have been inadvertently overlooked the publisher will be pleased to include any necessary credits in any subsequent reprint or edition. Table of contents Abbreviations vii Preface ix Introduction xi Chapter 1 Causation, determinism and the universe 1 1. Natural principles and the rise of free-will 1 1.1. “The most exact of the sciences” 2 1.2.