Basic Ideas and Applications of Smooth Infinitesimal Analysis John L
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Tutorial 8: Solutions
Tutorial 8: Solutions Applications of the Derivative 1. We are asked to find the absolute maximum and minimum values of f on the given interval, and state where those values occur: (a) f(x) = 2x3 + 3x2 12x on [1; 4]. − The absolute extrema occur either at a critical point (stationary point or point of non-differentiability) or at the endpoints. The stationary points are given by f 0(x) = 0, which in this case gives 6x2 + 6x 12 = 0 = x = 1; x = 2: − ) − Checking the values at the critical points (only x = 1 is in the interval [1; 4]) and endpoints: f(1) = 7; f(4) = 128: − Therefore the absolute maximum occurs at x = 4 and is given by f(4) = 124 and the absolute minimum occurs at x = 1 and is given by f(1) = 7. − (b) f(x) = (x2 + x)2=3 on [ 2; 3] − As before, we find the critical points. The derivative is given by 2 2x + 1 f 0(x) = 3 (x2 + x)1=2 and hence we have a stationary point when 2x + 1 = 0 and points of non- differentiability whenever x2 + x = 0. Solving these, we get the three critical points, x = 1=2; and x = 0; 1: − − Checking the value of the function at these critical points and the endpoints: f( 2) = 22=3; f( 1) = 0; f( 1=2) = 2−4=3; f(0) = 0; f(3) = (12)2=3: − − − Hence the absolute minimum occurs at either x = 1 or x = 0 since in both − these cases the minimum value is 0, while the absolute maximum occurs at x = 3 and is f(3) = (12)2=3. -
Lecture Notes – 1
Optimization Methods: Optimization using Calculus-Stationary Points 1 Module - 2 Lecture Notes – 1 Stationary points: Functions of Single and Two Variables Introduction In this session, stationary points of a function are defined. The necessary and sufficient conditions for the relative maximum of a function of single or two variables are also discussed. The global optimum is also defined in comparison to the relative or local optimum. Stationary points For a continuous and differentiable function f(x) a stationary point x* is a point at which the slope of the function vanishes, i.e. f ’(x) = 0 at x = x*, where x* belongs to its domain of definition. minimum maximum inflection point Fig. 1 A stationary point may be a minimum, maximum or an inflection point (Fig. 1). Relative and Global Optimum A function is said to have a relative or local minimum at x = x* if f ()xfxh**≤+ ( )for all sufficiently small positive and negative values of h, i.e. in the near vicinity of the point x*. Similarly a point x* is called a relative or local maximum if f ()xfxh**≥+ ( )for all values of h sufficiently close to zero. A function is said to have a global or absolute minimum at x = x* if f ()xfx* ≤ ()for all x in the domain over which f(x) is defined. Similarly, a function is D Nagesh Kumar, IISc, Bangalore M2L1 Optimization Methods: Optimization using Calculus-Stationary Points 2 said to have a global or absolute maximum at x = x* if f ()xfx* ≥ ()for all x in the domain over which f(x) is defined. -
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 -
Calculus Formulas and Theorems
Formulas and Theorems for Reference I. Tbigonometric Formulas l. sin2d+c,cis2d:1 sec2d l*cot20:<:sc:20 +.I sin(-d) : -sitt0 t,rs(-//) = t r1sl/ : -tallH 7. sin(A* B) :sitrAcosB*silBcosA 8. : siri A cos B - siu B <:os,;l 9. cos(A+ B) - cos,4cos B - siuA siriB 10. cos(A- B) : cosA cosB + silrA sirrB 11. 2 sirrd t:osd 12. <'os20- coS2(i - siu20 : 2<'os2o - I - 1 - 2sin20 I 13. tan d : <.rft0 (:ost/ I 14. <:ol0 : sirrd tattH 1 15. (:OS I/ 1 16. cscd - ri" 6i /F tl r(. cos[I ^ -el : sitt d \l 18. -01 : COSA 215 216 Formulas and Theorems II. Differentiation Formulas !(r") - trr:"-1 Q,:I' ]tra-fg'+gf' gJ'-,f g' - * (i) ,l' ,I - (tt(.r))9'(.,') ,i;.[tyt.rt) l'' d, \ (sttt rrJ .* ('oqI' .7, tJ, \ . ./ stll lr dr. l('os J { 1a,,,t,:r) - .,' o.t "11'2 1(<,ot.r') - (,.(,2.r' Q:T rl , (sc'c:.r'J: sPl'.r tall 11 ,7, d, - (<:s<t.r,; - (ls(].]'(rot;.r fr("'),t -.'' ,1 - fr(u") o,'ltrc ,l ,, 1 ' tlll ri - (l.t' .f d,^ --: I -iAl'CSllLl'l t!.r' J1 - rz 1(Arcsi' r) : oT Il12 Formulas and Theorems 2I7 III. Integration Formulas 1. ,f "or:artC 2. [\0,-trrlrl *(' .t "r 3. [,' ,t.,: r^x| (' ,I 4. In' a,,: lL , ,' .l 111Q 5. In., a.r: .rhr.r' .r r (' ,l f 6. sirr.r d.r' - ( os.r'-t C ./ 7. /.,,.r' dr : sitr.i'| (' .t 8. tl:r:hr sec,rl+ C or ln Jccrsrl+ C ,f'r^rr f 9. -
The Theory of Stationary Point Processes By
THE THEORY OF STATIONARY POINT PROCESSES BY FREDERICK J. BEUTLER and OSCAR A. Z. LENEMAN (1) The University of Michigan, Ann Arbor, Michigan, U.S.A. (3) Table of contents Abstract .................................... 159 1.0. Introduction and Summary ......................... 160 2.0. Stationarity properties for point processes ................... 161 2.1. Forward recurrence times and points in an interval ............. 163 2.2. Backward recurrence times and stationarity ................ 167 2.3. Equivalent stationarity conditions .................... 170 3.0. Distribution functions, moments, and sample averages of the s.p.p ......... 172 3.1. Convexity and absolute continuity .................... 172 3.2. Existence and global properties of moments ................ 174 3.3. First and second moments ........................ 176 3.4. Interval statistics and the computation of moments ............ 178 3.5. Distribution of the t n .......................... 182 3.6. An ergodic theorem ........................... 184 4.0. Classes and examples of stationary point processes ............... 185 4.1. Poisson processes ............................ 186 4.2. Periodic processes ........................... 187 4.3. Compound processes .......................... 189 4.4. The generalized skip process ....................... 189 4.5. Jitte processes ............................ 192 4.6. ~deprendent identically distributed intervals ................ 193 Acknowledgments ............................... 196 References ................................... 196 Abstract An axiomatic formulation is presented for point processes which may be interpreted as ordered sequences of points randomly located on the real line. Such concepts as forward recurrence times and number of points in intervals are defined and related in set-theoretic Q) Presently at Massachusetts Institute of Technology, Lexington, Mass., U.S.A. (2) This work was supported by the National Aeronautics and Space Administration under research grant NsG-2-59. 11 - 662901. Aeta mathematica. 116. Imprlm$ le 19 septembre 1966. -
4.1 AP Calc Antiderivatives and Indefinite Integration.Notebook November 28, 2016
4.1 AP Calc Antiderivatives and Indefinite Integration.notebook November 28, 2016 4.1 Antiderivatives and Indefinite Integration Learning Targets 1. Write the general solution of a differential equation 2. Use indefinite integral notation for antiderivatives 3. Use basic integration rules to find antiderivatives 4. Find a particular solution of a differential equation 5. Plot a slope field 6. Working backwards in physics Intro/Warmup 1 4.1 AP Calc Antiderivatives and Indefinite Integration.notebook November 28, 2016 Note: I am changing up the procedure of the class! When you walk in the class, you will put a tally mark on those problems that you would like me to go over, and you will turn in your homework with your name, the section and the page of the assignment. Order of the day 2 4.1 AP Calc Antiderivatives and Indefinite Integration.notebook November 28, 2016 Vocabulary First, find a function F whose derivative is . because any other function F work? So represents the family of all antiderivatives of . The constant C is called the constant of integration. The family of functions represented by F is the general antiderivative of f, and is the general solution to the differential equation The operation of finding all solutions to the differential equation is called antidifferentiation (or indefinite integration) and is denoted by an integral sign: is read as the antiderivative of f with respect to x. Vocabulary 3 4.1 AP Calc Antiderivatives and Indefinite Integration.notebook November 28, 2016 Nov 289:38 AM 4 4.1 AP Calc Antiderivatives and Indefinite Integration.notebook November 28, 2016 ∫ Use basic integration rules to find antiderivatives The Power Rule: where 1. -
The First Derivative and Stationary Points
Mathematics Learning Centre The first derivative and stationary points Jackie Nicholas c 2004 University of Sydney Mathematics Learning Centre, University of Sydney 1 The first derivative and stationary points dy The derivative of a function y = f(x) tell us a lot about the shape of a curve. In this dx section we will discuss the concepts of stationary points and increasing and decreasing functions. However, we will limit our discussion to functions y = f(x) which are well behaved. Certain functions cause technical difficulties so we will concentrate on those that don’t! The first derivative dy The derivative, dx ,isthe slope of the tangent to the curve y = f(x)atthe point x.Ifwe know about the derivative we can deduce a lot about the curve itself. Increasing functions dy If > 0 for all values of x in an interval I, then we know that the slope of the tangent dx to the curve is positive for all values of x in I and so the function y = f(x)isincreasing on the interval I. 3 For example, let y = x + x, then y dy 1 =3x2 +1> 0 for all values of x. dx That is, the slope of the tangent to the x curve is positive for all values of x. So, –1 0 1 y = x3 + x is an increasing function for all values of x. –1 The graph of y = x3 + x. We know that the function y = x2 is increasing for x>0. We can work this out from the derivative. 2 If y = x then y dy 2 =2x>0 for all x>0. -
The Legacy of Leonhard Euler: a Tricentennial Tribute (419 Pages)
P698.TP.indd 1 9/8/09 5:23:37 PM This page intentionally left blank Lokenath Debnath The University of Texas-Pan American, USA Imperial College Press ICP P698.TP.indd 2 9/8/09 5:23:39 PM Published by Imperial College Press 57 Shelton Street Covent Garden London WC2H 9HE Distributed by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. THE LEGACY OF LEONHARD EULER A Tricentennial Tribute Copyright © 2010 by Imperial College Press All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN-13 978-1-84816-525-0 ISBN-10 1-84816-525-0 Printed in Singapore. LaiFun - The Legacy of Leonhard.pmd 1 9/4/2009, 3:04 PM September 4, 2009 14:33 World Scientific Book - 9in x 6in LegacyLeonhard Leonhard Euler (1707–1783) ii September 4, 2009 14:33 World Scientific Book - 9in x 6in LegacyLeonhard To my wife Sadhana, grandson Kirin,and granddaughter Princess Maya, with love and affection. -
EE2 Maths: Stationary Points
EE2 Maths: Stationary Points df Univariate case: When we maximize f(x) (solving for the x0 such that dx = 0) the d df gradient is zero at these points. What about the rate of change of gradient: dx ( dx ) at the minimum x0? For a minimum the gradient increases as x0 ! x0 + ∆x (∆x > 0). It follows d2f d2f that dx2 > 0. The opposite is true for a maximum: dx2 < 0, the gradient decreases upon d2f positive steps away from x0. For a point of inflection dx2 = 0. r @f @f Multivariate case: Stationary points occur when f = 0. In 2-d this is ( @x ; @y ) = 0, @f @f namely, a generalization of the univariate case. Recall that df = @x dx + @y dy can be written as df = ds · rf where ds = (dx; dy). If rf = 0 at (x0; y0) then any infinitesimal step ds away from (x0; y0) will still leave f unchanged, i.e. df = 0. There are three types of stationary points of f(x; y): Maxima, Minima and Saddle Points. We'll draw some of their properties on the board. We will now attempt to find ways of identifying the character of each of the stationary points of f(x; y). Consider a Taylor expansion about a stationary point (x0; y0). We know that rf = 0 at (x0; y0) so writing (∆x; ∆y) = (x − x0; y − y0) we find: − ∆f = f(x; y)[ f(x0; y0) ] 1 @2f @2f @2f ' 0 + (∆x)2 + 2 ∆x∆y + (∆y)2 : (1) 2! @x2 @x@y @y2 Maxima: At a maximum all small steps away from (x0; y0) lead to ∆f < 0. -
Gottfried Wilhelm Leibnitz (Or Leibniz) Was Born at Leipzig on June 21 (O.S.), 1646, and Died in Hanover on November 14, 1716. H
Gottfried Wilhelm Leibnitz (or Leibniz) was born at Leipzig on June 21 (O.S.), 1646, and died in Hanover on November 14, 1716. His father died before he was six, and the teaching at the school to which he was then sent was inefficient, but his industry triumphed over all difficulties; by the time he was twelve he had taught himself to read Latin easily, and had begun Greek; and before he was twenty he had mastered the ordinary text-books on mathematics, philosophy, theology and law. Refused the degree of doctor of laws at Leipzig by those who were jealous of his youth and learning, he moved to Nuremberg. An essay which there wrote on the study of law was dedicated to the Elector of Mainz, and led to his appointment by the elector on a commission for the revision of some statutes, from which he was subsequently promoted to the diplomatic service. In the latter capacity he supported (unsuccessfully) the claims of the German candidate for the crown of Poland. The violent seizure of various small places in Alsace in 1670 excited universal alarm in Germany as to the designs of Louis XIV.; and Leibnitz drew up a scheme by which it was proposed to offer German co-operation, if France liked to take Egypt, and use the possessions of that country as a basis for attack against Holland in Asia, provided France would agree to leave Germany undisturbed. This bears a curious resemblance to the similar plan by which Napoleon I. proposed to attack England. In 1672 Leibnitz went to Paris on the invitation of the French government to explain the details of the scheme, but nothing came of it. -
Applications of Differentiation Applications of Differentiation– a Guide for Teachers (Years 11–12)
1 Supporting Australian Mathematics Project 2 3 4 5 6 12 11 7 10 8 9 A guide for teachers – Years 11 and 12 Calculus: Module 12 Applications of differentiation Applications of differentiation – A guide for teachers (Years 11–12) Principal author: Dr Michael Evans, AMSI Peter Brown, University of NSW Associate Professor David Hunt, University of NSW Dr Daniel Mathews, Monash University Editor: Dr Jane Pitkethly, La Trobe University Illustrations and web design: Catherine Tan, Michael Shaw Full bibliographic details are available from Education Services Australia. Published by Education Services Australia PO Box 177 Carlton South Vic 3053 Australia Tel: (03) 9207 9600 Fax: (03) 9910 9800 Email: [email protected] Website: www.esa.edu.au © 2013 Education Services Australia Ltd, except where indicated otherwise. You may copy, distribute and adapt this material free of charge for non-commercial educational purposes, provided you retain all copyright notices and acknowledgements. This publication is funded by the Australian Government Department of Education, Employment and Workplace Relations. Supporting Australian Mathematics Project Australian Mathematical Sciences Institute Building 161 The University of Melbourne VIC 3010 Email: [email protected] Website: www.amsi.org.au Assumed knowledge ..................................... 4 Motivation ........................................... 4 Content ............................................. 5 Graph sketching ....................................... 5 More graph sketching .................................. -
Lecture 3: 20 September 2018 3.1 Taylor Series Approximation
COS 597G: Toward Theoretical Understanding of Deep Learning Fall 2018 Lecture 3: 20 September 2018 Lecturer: Sanjeev Arora Scribe: Abhishek Bhrushundi, Pritish Sahu, Wenjia Zhang Recall the basic form of an optimization problem: min (or max) f(x) subject to x 2 B; where B ⊆ Rd and f : Rd ! R is the objective function1. Gradient descent (ascent) and its variants are a popular choice for finding approximate solutions to these sort of problems. In fact, it turns out that these methods also work well empirically even when the objective function f is non-convex, most notably when training neural networks with various loss functions. In this lecture we will explore some of the basics ideas behind gradient descent, try to understand its limitations, and also discuss some of its popular variants that try to get around these limitations. We begin with the Taylor series approximation of functions which serves as a starting point for these methods. 3.1 Taylor series approximation We begin by recalling the Taylor series for univariate real-valued functions from Calculus 101: if f : R ! R is infinitely differentiable at x 2 R then the Taylor series for f at x is the following power series (∆x)2 (∆x)k f(x) + f 0(x)∆x + f 00(x) + ::: + f (k)(x) + ::: 2! k! Truncating this power series at some power (∆x)k results in a polynomial that approximates f around the point x. In particular, for small ∆x, (∆x)2 (∆x)k f(x + ∆x) ≈ f(x) + f 0(x)∆x + f 00(x) + ::: + f (k)(x) 2! k! Here the error of the approximation goes to zero at least as fast as (∆x)k as ∆x ! 0.