Math 221 First Semester Calculus
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Mathematics Calculation Policy ÷
Mathematics Calculation Policy ÷ Commissioned by The PiXL Club Ltd. June 2016 This resource is strictly for the use of member schools for as long as they remain members of The PiXL Club. It may not be copied, sold nor transferred to a third party or used by the school after membership ceases. Until such time it may be freely used within the member school. All opinions and contributions are those of the authors. The contents of this resource are not connected with nor endorsed by any other company, organisation or institution. © Copyright The PiXL Club Ltd, 2015 About PiXL’s Calculation Policy • The following calculation policy has been devised to meet requirements of the National Curriculum 2014 for the teaching and learning of mathematics, and is also designed to give pupils a consistent and smooth progression of learning in calculations across the school. • Age stage expectations: The calculation policy is organised according to age stage expectations as set out in the National Curriculum 2014 and the method(s) shown for each year group should be modelled to the vast majority of pupils. However, it is vital that pupils are taught according to the pathway that they are currently working at and are showing to have ‘mastered’ a pathway before moving on to the next one. Of course, pupils who are showing to be secure in a skill can be challenged to the next pathway as necessary. • Choosing a calculation method: Before pupils opt for a written method they should first consider these steps: Should I use a formal Can I do it in my Could I use -
Lesson 2: the Multiplication of Polynomials
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 2 M1 ALGEBRA II Lesson 2: The Multiplication of Polynomials Student Outcomes . Students develop the distributive property for application to polynomial multiplication. Students connect multiplication of polynomials with multiplication of multi-digit integers. Lesson Notes This lesson begins to address standards A-SSE.A.2 and A-APR.C.4 directly and provides opportunities for students to practice MP.7 and MP.8. The work is scaffolded to allow students to discern patterns in repeated calculations, leading to some general polynomial identities that are explored further in the remaining lessons of this module. As in the last lesson, if students struggle with this lesson, they may need to review concepts covered in previous grades, such as: The connection between area properties and the distributive property: Grade 7, Module 6, Lesson 21. Introduction to the table method of multiplying polynomials: Algebra I, Module 1, Lesson 9. Multiplying polynomials (in the context of quadratics): Algebra I, Module 4, Lessons 1 and 2. Since division is the inverse operation of multiplication, it is important to make sure that your students understand how to multiply polynomials before moving on to division of polynomials in Lesson 3 of this module. In Lesson 3, division is explored using the reverse tabular method, so it is important for students to work through the table diagrams in this lesson to prepare them for the upcoming work. There continues to be a sharp distinction in this curriculum between justification and proof, such as justifying the identity (푎 + 푏)2 = 푎2 + 2푎푏 + 푏 using area properties and proving the identity using the distributive property. -
33 .1 Implicit Differentiation 33.1.1Implicit Function
Module 11 : Partial derivatives, Chain rules, Implicit differentiation, Gradient, Directional derivatives Lecture 33 : Implicit differentiation [Section 33.1] Objectives In this section you will learn the following : The concept of implicit differentiation of functions. 33 .1 Implicit differentiation \ As we had observed in section , many a times a function of an independent variable is not given explicitly, but implicitly by a relation. In section we had also mentioned about the implicit function theorem. We state it precisely now, without proof. 33.1.1Implicit Function Theorem (IFT): Let and be such that the following holds: (i) Both the partial derivatives and exist and are continuous in . (ii) and . Then there exists some and a function such that is differentiable, its derivative is continuous with and 33.1.2Remark: We have a corresponding version of the IFT for solving in terms of . Here, the hypothesis would be . 33.1.3Example: Let we want to know, when does the implicit expression defines explicitly as a function of . We note that and are both continuous. Since for the points and the implicit function theorem is not applicable. For , and , the equation defines the explicit function and for , the equation defines the explicit function Figure 1. y is a function of x. A result similar to that of theorem holds for function of three variables, as stated next. Theorem : 33.1.4 Let and be such that (i) exist and are continuous at . (ii) and . Then the equation determines a unique function in the neighborhood of such that for , and , Practice Exercises : Show that the following functions satisfy conditions of the implicit function theorem in the neighborhood of (1) the indicated point. -
James Clerk Maxwell
James Clerk Maxwell JAMES CLERK MAXWELL Perspectives on his Life and Work Edited by raymond flood mark mccartney and andrew whitaker 3 3 Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries c Oxford University Press 2014 The moral rights of the authors have been asserted First Edition published in 2014 Impression: 1 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, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2013942195 ISBN 978–0–19–966437–5 Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY Links to third party websites are provided by Oxford in good faith and for information only. -
Inverse of Exponential Functions Are Logarithmic Functions
Math Instructional Framework Full Name Math III Unit 3 Lesson 2 Time Frame Unit Name Logarithmic Functions as Inverses of Exponential Functions Learning Task/Topics/ Task 2: How long Does It Take? Themes Task 3: The Population of Exponentia Task 4: Modeling Natural Phenomena on Earth Culminating Task: Traveling to Exponentia Standards and Elements MM3A2. Students will explore logarithmic functions as inverses of exponential functions. c. Define logarithmic functions as inverses of exponential functions. Lesson Essential Questions How can you graph the inverse of an exponential function? Activator PROBLEM 2.Task 3: The Population of Exponentia (Problem 1 could be completed prior) Work Session Inverse of Exponential Functions are Logarithmic Functions A Graph the inverse of exponential functions. B Graph logarithmic functions. See Notes Below. VOCABULARY Asymptote: A line or curve that describes the end behavior of the graph. A graph never crosses a vertical asymptote but it may cross a horizontal or oblique asymptote. Common logarithm: A logarithm with a base of 10. A common logarithm is the power, a, such that 10a = b. The common logarithm of x is written log x. For example, log 100 = 2 because 102 = 100. Exponential functions: A function of the form y = a·bx where a > 0 and either 0 < b < 1 or b > 1. Logarithmic functions: A function of the form y = logbx, with b 1 and b and x both positive. A logarithmic x function is the inverse of an exponential function. The inverse of y = b is y = logbx. Logarithm: The logarithm base b of a number x, logbx, is the power to which b must be raised to equal x. -
Number Theory
“mcs-ftl” — 2010/9/8 — 0:40 — page 81 — #87 4 Number Theory Number theory is the study of the integers. Why anyone would want to study the integers is not immediately obvious. First of all, what’s to know? There’s 0, there’s 1, 2, 3, and so on, and, oh yeah, -1, -2, . Which one don’t you understand? Sec- ond, what practical value is there in it? The mathematician G. H. Hardy expressed pleasure in its impracticality when he wrote: [Number theorists] may be justified in rejoicing that there is one sci- ence, at any rate, and that their own, whose very remoteness from or- dinary human activities should keep it gentle and clean. Hardy was specially concerned that number theory not be used in warfare; he was a pacifist. You may applaud his sentiments, but he got it wrong: Number Theory underlies modern cryptography, which is what makes secure online communication possible. Secure communication is of course crucial in war—which may leave poor Hardy spinning in his grave. It’s also central to online commerce. Every time you buy a book from Amazon, check your grades on WebSIS, or use a PayPal account, you are relying on number theoretic algorithms. Number theory also provides an excellent environment for us to practice and apply the proof techniques that we developed in Chapters 2 and 3. Since we’ll be focusing on properties of the integers, we’ll adopt the default convention in this chapter that variables range over the set of integers, Z. 4.1 Divisibility The nature of number theory emerges as soon as we consider the divides relation a divides b iff ak b for some k: D The notation, a b, is an abbreviation for “a divides b.” If a b, then we also j j say that b is a multiple of a. -
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 -
IVC Factsheet Functions Comp Inverse
Imperial Valley College Math Lab Functions: Composition and Inverse Functions FUNCTION COMPOSITION In order to perform a composition of functions, it is essential to be familiar with function notation. If you see something of the form “푓(푥) = [expression in terms of x]”, this means that whatever you see in the parentheses following f should be substituted for x in the expression. This can include numbers, variables, other expressions, and even other functions. EXAMPLE: 푓(푥) = 4푥2 − 13푥 푓(2) = 4 ∙ 22 − 13(2) 푓(−9) = 4(−9)2 − 13(−9) 푓(푎) = 4푎2 − 13푎 푓(푐3) = 4(푐3)2 − 13푐3 푓(ℎ + 5) = 4(ℎ + 5)2 − 13(ℎ + 5) Etc. A composition of functions occurs when one function is “plugged into” another function. The notation (푓 ○푔)(푥) is pronounced “푓 of 푔 of 푥”, and it literally means 푓(푔(푥)). In other words, you “plug” the 푔(푥) function into the 푓(푥) function. Similarly, (푔 ○푓)(푥) is pronounced “푔 of 푓 of 푥”, and it literally means 푔(푓(푥)). In other words, you “plug” the 푓(푥) function into the 푔(푥) function. WARNING: Be careful not to confuse (푓 ○푔)(푥) with (푓 ∙ 푔)(푥), which means 푓(푥) ∙ 푔(푥) . EXAMPLES: 푓(푥) = 4푥2 − 13푥 푔(푥) = 2푥 + 1 a. (푓 ○푔)(푥) = 푓(푔(푥)) = 4[푔(푥)]2 − 13 ∙ 푔(푥) = 4(2푥 + 1)2 − 13(2푥 + 1) = [푠푚푝푙푓푦] … = 16푥2 − 10푥 − 9 b. (푔 ○푓)(푥) = 푔(푓(푥)) = 2 ∙ 푓(푥) + 1 = 2(4푥2 − 13푥) + 1 = 8푥2 − 26푥 + 1 A function can even be “composed” with itself: c. -
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. -
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. -
An Arbitrary Precision Computation Package David H
ARPREC: An Arbitrary Precision Computation Package David H. Bailey, Yozo Hida, Xiaoye S. Li and Brandon Thompson1 Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Draft Date: 2002-10-14 Abstract This paper describes a new software package for performing arithmetic with an arbitrarily high level of numeric precision. It is based on the earlier MPFUN package [2], enhanced with special IEEE floating-point numerical techniques and several new functions. This package is written in C++ code for high performance and broad portability and includes both C++ and Fortran-90 translation modules, so that conventional C++ and Fortran-90 programs can utilize the package with only very minor changes. This paper includes a survey of some of the interesting applications of this package and its predecessors. 1. Introduction For a small but growing sector of the scientific computing world, the 64-bit and 80-bit IEEE floating-point arithmetic formats currently provided in most computer systems are not sufficient. As a single example, the field of climate modeling has been plagued with non-reproducibility, in that climate simulation programs give different results on different computer systems (or even on different numbers of processors), making it very difficult to diagnose bugs. Researchers in the climate modeling community have assumed that this non-reproducibility was an inevitable conse- quence of the chaotic nature of climate simulations. Recently, however, it has been demonstrated that for at least one of the widely used climate modeling programs, much of this variability is due to numerical inaccuracy in a certain inner loop. When some double-double arithmetic rou- tines, developed by the present authors, were incorporated into this program, almost all of this variability disappeared [12]. -
Which Moments of a Logarithmic Derivative Imply Quasiinvariance?
Doc Math J DMV Which Moments of a Logarithmic Derivative Imply Quasi invariance Michael Scheutzow Heinrich v Weizsacker Received June Communicated by Friedrich Gotze Abstract In many sp ecial contexts quasiinvariance of a measure under a oneparameter group of transformations has b een established A remarkable classical general result of AV Skorokhod states that a measure on a Hilb ert space is quasiinvariant in a given direction if it has a logarithmic aj j derivative in this direction such that e is integrable for some a In this note we use the techniques of to extend this result to general oneparameter families of measures and moreover we give a complete char acterization of all functions for which the integrability of j j implies quasiinvariance of If is convex then a necessary and sucient condition is that log xx is not integrable at Mathematics Sub ject Classication A C G Overview The pap er is divided into two parts The rst part do es not mention quasiinvariance at all It treats only onedimensional functions and implicitly onedimensional measures The reason is as follows A measure on R has a logarithmic derivative if and only if has an absolutely continuous Leb esgue density f and is given by 0 f x ae Then the integrability of jj is equivalent to the Leb esgue x f 0 f jf The quasiinvariance of is equivalent to the statement integrability of j f that f x Leb esgueae Therefore in the case of onedimensional measures a function allows a quasiinvariance criterion as indicated in the abstract