Math Differential Equations Material Covering Lab 2
|
|
- Barrie Edwards
- 5 years ago
- Views:
Transcription
1 Math Differential Equations Material Covering Lab 2 Separable Equations A separable equation is a first-orer ODE that can be written in the form y'= g x $h y This is the general metho to solve such ifferential equation: y x = g x $h y 1 y = g x x C c (abuse of notation) h y Remark: it is enough to a the constant only on one sie of the equation. Example 1: Solve the following DE. y'c 2$x$y 2 = 0 Solution: to be one in class. Let us ouble check our answer by using Maple: oe y'c 2$x$y 2 = 0 x y x C 2 x y x 2 = 0 solve oe y x = 1 x 2 C Example 2 (Exercise 2 on Lab 2): Solve the following IVP y' = y2 K 1 x 2 y 2 = 2 K 1 Solution: to be one in class. Let us ouble check our answer by using Maple: oe y' = y2 K 1 x 2 K 1 ics y 2 = 2 solve oe, ics x y x = y x 2 K 1 x 2 K 1 y 2 = 2 y x = x Look at Example 3 Section 2.2 of your textbook. (1.1) (1.2) (1.3) (1.4) (1.5) Linear Equations We want to solve 1st-rer linear ODE, which can be written as
2 a 1 x $y'c a 0 x $y = g x Diviing both sies by a 1 x we can rewrite the DE as y' C P x $y = Q x Remark. We are looking for a solution y = f x on an interval I where P x an Q x are both (efine an) continuous. Important. When possible solve by separation of variables since it is, usually, faster. General Solution The key iea (from Johann Bernoulli) is to multiply both sies by the same function µ x : µ x $y' x C µ x $P x $y = µ x $Q x (1) Consier the function µ x $y x an take its erivative x µ x $y x = µ x $y' C µ' x $y x Notice: if we have that µ' x = µ x $P x then equation (1) becomes x µ x $y x = µ x $Q x (2) Equation (2) is very nice because we alreay know how to solve it: µ x $y x = µ x $Q x x C C Therefore, we have reuce to solve the following ODE µ x = µ$p x We solve the above equation by separation of variables: µ µ = P x x from which ln µ x = P x x C c 1
3 an µ x = c 2 $e P x x c where c 2 = e 1 Remark. It is enough to fin just one function µ x, so we can choose c 2 = 1. Therefore we have µ x = e P x x. The ifferential equation becomes x e P x x $y x = e P x x $Q x At this point we solve the problem via simple integration. See equation (8) on page 47 of your textbook for the final formula for y x. However, the only formuala we nee to memoriza is the integrating factor µ x = e P x x. Example. Solve the DE y'= 5 K 8 y K4$x$y x C 2 2 an the IVP with initial conition y K1 = 2. Solution: to be one in class. Let us ouble check our solution on Maple: oe y'= 5 K 8 y K4$x$y x C 2 2 ics y K1 = 2 solve oe solve oe, ics x y x = 5 K 8 y x K 4 x y x x C 2 2 y x = y x = y K1 = x C 2 3 C x C 2 4 x C 2 3 C 1 3 x C 2 4 (2.1) (2.2) (2.3) (2.4)
4 Exact Equations Recall. So far, we can solve : 1st-orer separable DE (not necessary linear) 1st-orer linear DE Now we focus on 1st-orer DE that can be written in the form M x, y x C N x, y y = 0 Recall from Calc III : if z = f x, y then z = vf vf x C vx vy y when f x, y = c, a constant, we have vf vf x C y = 0. vx vy 2 Example: consier the function f x, y x C 2$ sin x C y C 2$ sin y 2 2 x, y / x C 2 sin x C y C 2 sin y 2 A one-parameter family of solutions for the DE x f x, y x C f x, y y = 0 y 2 x C 2 sin x 1 C 2 cos x x C 2 y C 2 sin y 1 C 2 cos y y = 0 is given by f x, y = c. Let us use Maple to plot a special solution: f x, y = 36$p. with plots animate, animate3, animatecurve, arrow, changecoors, complexplot, complexplot3, conformal, conformal3, contourplot, contourplot3, coorplot, coorplot3, ensityplot, isplay, ualaxisplot, fielplot, fielplot3, graplot, graplot3, implicitplot, implicitplot3, inequal, interactive, interactiveparams, intersectplot, listcontplot, listcontplot3, listensityplot, listplot, listplot3, loglogplot, logplot, matrixplot, multiple, oeplot, pareto, plotcompare, pointplot, pointplot3, polarplot, polygonplot, polygonplot3, polyhera_supporte, polyheraplot, rootlocus, semilogplot, setcolors, setoptions, setoptions3, spacecurve, sparsematrixplot, surfata, textplot, textplot3, tubeplot implicitplot f x, y = 36$p, x =K15..15, y =K15..15, grirefine = 2 ; (3.1) (3.2) (3.3)
5 10 y 5 K10 K x K5 K10 Question: Given the DE M x, y x C N x, y y = 0, how can we check whether there exists f x, y such that 1 M x, y = vf vx an 2 N x, y = vf? vy Definition: we say that the DE: M x, y x C N x, y y = 0 is exact if there exists a function f x, y that satisfies properties (1) an (2). Theorem (Theorem 2, page 57 of your textbook): Suppose that the first partial erivatives of M x, y an N x, y are continuous in a rectangle R. Then M x, y x C N x, y y = 0,
6 is exact if an only if vm vy x, y = vn vx x, y hols for every x, y in R. Example/Explanation: Consier the DE: 2$x$y 2 K3 x C 2 x 2 y C 4 y = 0. Determine whether the DE is exact. If it is exact, solve it. Solution: to be one in class. See also examples 2, 3, an 4 on your textbook. Remark: to solve an IVP plug-in x an y into the final solution f x, y = c in orer to etermine c. Then write the final equation (even in implicit form). Special Integrating Factor Question: What if the DE: M x, y x C N x, y y = 0 is not exact? Answer: We look for a suitable intagraing factor. More precisely: we want to fin a function µ x, y such that the DE: µ x, y $M x, y x C µ x, y $N x, y y = 0 is exact. First of all, assume that the continuity hypothesis works. Then we want: µ M y = µ N x Let us expan both sies: µ y M C µ M y = µ x N C µ N x now we manipulate our equation to get µ x N Kµ y M = µ M y K N x (3) Simplifying assumptions:
7 Case 1) µ is only a function in terms of x. This means that µ y = 0 an from (3): µ x = M y K N x N µ We can etermine µ x if M y K N x N µ x = e M y K N x N x only epens of x: Case 1) µ is only a function in terms of y. This means that µ x = 0 an from (3): µ y = N x KM y M µ We can etermine µ y if N x KM y M only epens of x: µ y = e N x KM y M y Remark: the solutions of the new DE are almost equivalent: we are aing µ x = 0 or µ y = 0 obtaine, in general, only for finite values of xs an ys. Example/Explanation: Consier the DE: 2 y 2 C 3 x x C 2 x y y = 0. a) Check that the DE is not exact. b) Solve the DE via integrating factor. Solution: to be one in class. Substitutions an Transformations Homogeneous Equations We say that the DE y'= f x, y
8 is homogeneous if f x, y can be written as a function of y x. In this case we apply the substitution y = v$x. At the en recover y(x). Keep in min that: y' = v C x$ v x. After substituting, we can solve by separation of variables. Example: Solve the DE y 2 C y$x x C x 2 y = 0. Solution: to be one in class. Let us check our answer by using Maple: oe y 2 C y$x C x 2 $y' = 0 y x 2 C y x x C x 2 x y x = 0 solve oe y x = 2 x K1 C 2 x 2 (5.1.1) (5.1.2) Equations of the form y'=g(ax+by) When the DE can be written as y'= G a x C b y we can apply the substitution z = a x C b y. Assume b s 0 (otherwise we can immeiately apply separation of variables). We have y'= 1 b z' K a b. After substituting, we can solve by separation of variables. At the en recover y(x). Example: Solve the DE y'= x C y K1. Solution: to be one in class. Let us check our solution on Maple oe y'= solve oe x C y K1 x y x = x C y x K 1 (5.2.1)
9 x K 2 x C y x K = 0 Remark: this argument also works with y'= G a x C b y C c. (5.2.2) Bernoulli equations A Bernoulli equation is an equation that can be written in the form y' C P x y = Q x y n with n s 1. Sunstitution: v = y 1 Kn. We get y x = 1 1 K n yn v x. After substituting solve as a 1st-orer linear DE. At the en recover y(x). Example: Solve the DE y' - y = e x $y 2. Solution: to be one in class. Let us check our solution on Maple: oe y' Ky = exp x $y 2 solve oe x y x K y x = ex y x 2 y x = 2 Ke x C 2 e Kx (5.3.1) (5.3.2) Equations with linear coefficients A DE that can be written in the form a 1 x C b 1 y C c 1 x C a 2 x C b 2 y C c 2 y = 0 it is calle DE with linear coefficients. When a 1 b 2 = a 2 b 1 then the DE above can be reuce to the form y'= G a x C b y. Otherwise, we have two cases (I) c 1 = c 2 = 0 then the DE becomes homogeneous.
10 (II) apply the substitution x = u C h an y = v C k an etermine h an k in orer to reuce to case 1. Example: Solve the DE 2 x Ky x C 4 x C y K3 x = 0. Solution: to be one in class. Let us ouble check on Maple oe y'=k solve oe y x = 1 C 1 24 K 1 2 x Ky 4 x C y K 3 x y x = K 2 x K y x 4 x C y x K 3 1 K72 2 x K 1 C x K 1 2 C 8 C x 2 x K 1 K4 C 27 2 x K / 3 K x (5.4.1) (5.4.2) K 1 K 1 K72 2 x K 1 C x K 1 2 C 8 C x K 1 2 x K 1 K4 C 27 2 x K / 3 K C 3 2 x K 1 K 1 4 $I K72 2 x K 1 C x K 1 2 C 8 C x K 1 2 x K 1 K4 C 27 2 x K / 3 C x K 1 K 1 K72 2 x K 1 C x K 1 2 C 8 C x K 1 2 x K 1 K4 C 27 2 x K / 3
Vectors in Space. Standard Graphing
Vectors in Space O restart:with(plots);with(vectorcalculus): animate, animated, animatecurve, arrow, changecoords, complexplot, complexplotd, conformal, conformald, contourplot, contourplotd, coordplot,
More informationTaylor Polynomials restart;with(plots); with(student); with(numericalanalysis); with(student[numericalanalysis]);
Talor Polnomials Talor's Theorem is used etensivel in Numerical Analsis and is the basis for the development of several important techniques. We begin b restarting and loading the plots package for some
More informationLINEAR DIFFERENTIAL EQUATIONS OF ORDER 1. where a(x) and b(x) are functions. Observe that this class of equations includes equations of the form
LINEAR DIFFERENTIAL EQUATIONS OF ORDER 1 We consier ifferential equations of the form y + a()y = b(), (1) y( 0 ) = y 0, where a() an b() are functions. Observe that this class of equations inclues equations
More information23 Implicit differentiation
23 Implicit ifferentiation 23.1 Statement The equation y = x 2 + 3x + 1 expresses a relationship between the quantities x an y. If a value of x is given, then a corresponing value of y is etermine. For
More informationLinear First-Order Equations
5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)
More informationImplicit Differentiation
Implicit Differentiation Thus far, the functions we have been concerne with have been efine explicitly. A function is efine explicitly if the output is given irectly in terms of the input. For instance,
More informationApplications of the Wronskian to ordinary linear differential equations
Physics 116C Fall 2011 Applications of the Wronskian to orinary linear ifferential equations Consier a of n continuous functions y i (x) [i = 1,2,3,...,n], each of which is ifferentiable at least n times.
More information2 ODEs Integrating Factors and Homogeneous Equations
2 ODEs Integrating Factors an Homogeneous Equations We begin with a slightly ifferent type of equation: 2.1 Exact Equations These are ODEs whose general solution can be obtaine by simply integrating both
More informationStrauss PDEs 2e: Section Exercise 6 Page 1 of 5
Strauss PDEs 2e: Section 4.3 - Exercise 6 Page 1 of 5 Exercise 6 If a 0 = a l = a in the Robin problem, show that: (a) There are no negative eigenvalues if a 0, there is one if 2/l < a < 0, an there are
More informationUnderstanding Molecular Orbitals; Sigma Orbitals
Universit of Connecticut DigitalCommons@UConn Chemistr Education Materials Department of Chemistr April 7 Understanding Molecular Orbitals; Sigma Orbitals Carl W. David Universit of Connecticut, Carl.David@uconn.edu
More informationIntroduction to Differential Equations Math 286 X1 Fall 2009 Homework 2 Solutions
Introuction to Differential Equations Math 286 X1 Fall 2009 Homewk 2 Solutions 1. Solve each of the following ifferential equations: (a) y + 3xy = 0 (b) y + 3y = 3x (c) y t = cos(t)y () x 2 y x y = 3 Solution:
More informationMath 1B, lecture 8: Integration by parts
Math B, lecture 8: Integration by parts Nathan Pflueger 23 September 2 Introuction Integration by parts, similarly to integration by substitution, reverses a well-known technique of ifferentiation an explores
More informationMath 251 Notes. Part I.
Math 251 Notes. Part I. F. Patricia Meina May 6, 2013 Growth Moel.Consumer price inex. [Problem 20, page 172] The U.S. consumer price inex (CPI) measures the cost of living base on a value of 100 in the
More informationLectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs
Lectures - Week 10 Introuction to Orinary Differential Equations (ODES) First Orer Linear ODEs When stuying ODEs we are consiering functions of one inepenent variable, e.g., f(x), where x is the inepenent
More informationFinal Exam Study Guide and Practice Problems Solutions
Final Exam Stuy Guie an Practice Problems Solutions Note: These problems are just some of the types of problems that might appear on the exam. However, to fully prepare for the exam, in aition to making
More informationMath 2163, Practice Exam II, Solution
Math 63, Practice Exam II, Solution. (a) f =< f s, f t >=< s e t, s e t >, an v v = , so D v f(, ) =< ()e, e > =< 4, 4 > = 4. (b) f =< xy 3, 3x y 4y 3 > an v =< cos π, sin π >=, so
More informationSection The Chain Rule and Implicit Differentiation with Application on Derivative of Logarithm Functions
Section 3.4-3.6 The Chain Rule an Implicit Differentiation with Application on Derivative of Logarithm Functions Ruipeng Shen September 3r, 5th Ruipeng Shen MATH 1ZA3 September 3r, 5th 1 / 3 The Chain
More informationMath 180, Exam 2, Fall 2012 Problem 1 Solution. (a) The derivative is computed using the Chain Rule twice. 1 2 x x
. Fin erivatives of the following functions: (a) f() = tan ( 2 + ) ( ) 2 (b) f() = ln 2 + (c) f() = sin() Solution: Math 80, Eam 2, Fall 202 Problem Solution (a) The erivative is compute using the Chain
More informationDesigning Information Devices and Systems II Spring 2018 J. Roychowdhury and M. Maharbiz Discussion 2A
EECS 6B Designing Information Devices an Systems II Spring 208 J. Roychowhury an M. Maharbiz Discussion 2A Secon-Orer Differential Equations Secon-orer ifferential equations are ifferential equations of
More informationThe Exact Form and General Integrating Factors
7 The Exact Form an General Integrating Factors In the previous chapters, we ve seen how separable an linear ifferential equations can be solve using methos for converting them to forms that can be easily
More information1 Lecture 20: Implicit differentiation
Lecture 20: Implicit ifferentiation. Outline The technique of implicit ifferentiation Tangent lines to a circle Derivatives of inverse functions by implicit ifferentiation Examples.2 Implicit ifferentiation
More informationExploring Calculus Using a Maple Approach
Exploring Calculus Using a Maple Approach Last Update: October, 00 Zhao Chen Janet Liou-Mark Arnavaz Taraporevala Table of Contents Preface... Chapter : Introduction to MAPLE... Objectives... Lab Activities...9
More informationThe Explicit Form of a Function
Section 3 5 Implicit Differentiation The Eplicit Form of a Function The normal way we see function notation has f () on one sie of an equation an an epression in terms of on the other sie. We know the
More informationImplicit Differentiation
Implicit Differentiation Implicit Differentiation Using the Chain Rule In the previous section we focuse on the erivatives of composites an saw that THEOREM 20 (Chain Rule) Suppose that u = g(x) is ifferentiable
More informationMath 342 Partial Differential Equations «Viktor Grigoryan
Math 342 Partial Differential Equations «Viktor Grigoryan 6 Wave equation: solution In this lecture we will solve the wave equation on the entire real line x R. This correspons to a string of infinite
More informationSturm-Liouville Theory
LECTURE 5 Sturm-Liouville Theory In the three preceing lectures I emonstrate the utility of Fourier series in solving PDE/BVPs. As we ll now see, Fourier series are just the tip of the iceberg of the theory
More informationImplicit Differentiation. Lecture 16.
Implicit Differentiation. Lecture 16. We are use to working only with functions that are efine explicitly. That is, ones like f(x) = 5x 3 + 7x x 2 + 1 or s(t) = e t5 3, in which the function is escribe
More informationChapter 2 The Derivative Business Calculus 155
Chapter The Derivative Business Calculus 155 Section 11: Implicit Differentiation an Relate Rates In our work up until now, the functions we neee to ifferentiate were either given explicitly, x such as
More informationd dx [xn ] = nx n 1. (1) dy dx = 4x4 1 = 4x 3. Theorem 1.3 (Derivative of a constant function). If f(x) = k and k is a constant, then f (x) = 0.
Calculus refresher Disclaimer: I claim no original content on this ocument, which is mostly a summary-rewrite of what any stanar college calculus book offers. (Here I ve use Calculus by Dennis Zill.) I
More informationMath Implicit Differentiation. We have discovered (and proved) formulas for finding derivatives of functions like
Math 400 3.5 Implicit Differentiation Name We have iscovere (an prove) formulas for fining erivatives of functions like f x x 3x 4x. 3 This amounts to fining y for 3 y x 3x 4x. Notice that in this case,
More informationReview of Differentiation and Integration for Ordinary Differential Equations
Schreyer Fall 208 Review of Differentiation an Integration for Orinary Differential Equations In this course you will be expecte to be able to ifferentiate an integrate quickly an accurately. Many stuents
More informationMath 300 Winter 2011 Advanced Boundary Value Problems I. Bessel s Equation and Bessel Functions
Math 3 Winter 2 Avance Bounary Value Problems I Bessel s Equation an Bessel Functions Department of Mathematical an Statistical Sciences University of Alberta Bessel s Equation an Bessel Functions We use
More informationSolutions to Math 41 Second Exam November 4, 2010
Solutions to Math 41 Secon Exam November 4, 2010 1. (13 points) Differentiate, using the metho of your choice. (a) p(t) = ln(sec t + tan t) + log 2 (2 + t) (4 points) Using the rule for the erivative of
More informationDiagonalization of Matrices Dr. E. Jacobs
Diagonalization of Matrices Dr. E. Jacobs One of the very interesting lessons in this course is how certain algebraic techniques can be use to solve ifferential equations. The purpose of these notes is
More informationMathematical Review Problems
Fall 6 Louis Scuiero Mathematical Review Problems I. Polynomial Equations an Graphs (Barrante--Chap. ). First egree equation an graph y f() x mx b where m is the slope of the line an b is the line's intercept
More informationThe Explicit Form of a Function
Section 3 5 Implicit Differentiation The Eplicit Form of a Function Function Notation requires that we state a function with f () on one sie of an equation an an epression in terms of on the other sie
More informationMath 1271 Solutions for Fall 2005 Final Exam
Math 7 Solutions for Fall 5 Final Eam ) Since the equation + y = e y cannot be rearrange algebraically in orer to write y as an eplicit function of, we must instea ifferentiate this relation implicitly
More informationDifferentiation ( , 9.5)
Chapter 2 Differentiation (8.1 8.3, 9.5) 2.1 Rate of Change (8.2.1 5) Recall that the equation of a straight line can be written as y = mx + c, where m is the slope or graient of the line, an c is the
More informationAssignment 1. g i (x 1,..., x n ) dx i = 0. i=1
Assignment 1 Golstein 1.4 The equations of motion for the rolling isk are special cases of general linear ifferential equations of constraint of the form g i (x 1,..., x n x i = 0. i=1 A constraint conition
More informationSection 7.1: Integration by Parts
Section 7.1: Integration by Parts 1. Introuction to Integration Techniques Unlike ifferentiation where there are a large number of rules which allow you (in principle) to ifferentiate any function, the
More informationFebruary 21 Math 1190 sec. 63 Spring 2017
February 21 Math 1190 sec. 63 Spring 2017 Chapter 2: Derivatives Let s recall the efinitions an erivative rules we have so far: Let s assume that y = f (x) is a function with c in it s omain. The erivative
More information016A Homework 10 Solution
016A Homework 10 Solution Jae-young Park November 2, 2008 4.1 #14 Write each expression in the form of 2 kx or 3 kx, for a suitable constant k; (3 x 3 x/5 ) 5, (16 1/4 16 3/4 ) 3x Solution (3 x 3 x/5 )
More informationOutline. MS121: IT Mathematics. Differentiation Rules for Differentiation: Part 1. Outline. Dublin City University 4 The Quotient Rule
MS2: IT Mathematics Differentiation Rules for Differentiation: Part John Carroll School of Mathematical Sciences Dublin City University Pattern Observe You may have notice the following pattern when we
More informationImplicit Differentiation and Inverse Trigonometric Functions
Implicit Differentiation an Inverse Trigonometric Functions MATH 161 Calculus I J. Robert Buchanan Department of Mathematics Summer 2018 Explicit vs. Implicit Functions 0.5 1 y 0.0 y 2 0.5 3 4 1.0 0.5
More informationTable of Common Derivatives By David Abraham
Prouct an Quotient Rules: Table of Common Derivatives By Davi Abraham [ f ( g( ] = [ f ( ] g( + f ( [ g( ] f ( = g( [ f ( ] g( g( f ( [ g( ] Trigonometric Functions: sin( = cos( cos( = sin( tan( = sec
More informationMath Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors
Math 18.02 Notes on ifferentials, the Chain Rule, graients, irectional erivative, an normal vectors Tangent plane an linear approximation We efine the partial erivatives of f( xy, ) as follows: f f( x+
More information18 EVEN MORE CALCULUS
8 EVEN MORE CALCULUS Chapter 8 Even More Calculus Objectives After stuing this chapter you shoul be able to ifferentiate an integrate basic trigonometric functions; unerstan how to calculate rates of change;
More informationcosh x sinh x So writing t = tan(x/2) we have 6.4 Integration using tan(x/2) 2t 1 + t 2 cos x = 1 t2 sin x =
6.4 Integration using tan/ We will revisit the ouble angle ientities: sin = sin/ cos/ = tan/ sec / = tan/ + tan / cos = cos / sin / tan = = tan / sec / tan/ tan /. = tan / + tan / So writing t = tan/ we
More informationDifferentiation Rules Derivatives of Polynomials and Exponential Functions
Derivatives of Polynomials an Exponential Functions Differentiation Rules Derivatives of Polynomials an Exponential Functions Let s start with the simplest of all functions, the constant function f(x)
More informationFirst Order Linear Differential Equations
LECTURE 6 First Orer Linear Differential Equations A linear first orer orinary ifferential equation is a ifferential equation of the form ( a(xy + b(xy = c(x. Here y represents the unknown function, y
More informationLecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations
Lecture XII Abstract We introuce the Laplace equation in spherical coorinates an apply the metho of separation of variables to solve it. This will generate three linear orinary secon orer ifferential equations:
More informationOrdinary Differential Equations
Orinary Differential Equations Example: Harmonic Oscillator For a perfect Hooke s-law spring,force as a function of isplacement is F = kx Combine with Newton s Secon Law: F = ma with v = a = v = 2 x 2
More informationQF101: Quantitative Finance September 5, Week 3: Derivatives. Facilitator: Christopher Ting AY 2017/2018. f ( x + ) f(x) f(x) = lim
QF101: Quantitative Finance September 5, 2017 Week 3: Derivatives Facilitator: Christopher Ting AY 2017/2018 I recoil with ismay an horror at this lamentable plague of functions which o not have erivatives.
More informationA. Incorrect! The letter t does not appear in the expression of the given integral
AP Physics C - Problem Drill 1: The Funamental Theorem of Calculus Question No. 1 of 1 Instruction: (1) Rea the problem statement an answer choices carefully () Work the problems on paper as neee (3) Question
More informationDiophantine Approximations: Examining the Farey Process and its Method on Producing Best Approximations
Diophantine Approximations: Examining the Farey Process an its Metho on Proucing Best Approximations Kelly Bowen Introuction When a person hears the phrase irrational number, one oes not think of anything
More informationUnit #6 - Families of Functions, Taylor Polynomials, l Hopital s Rule
Unit # - Families of Functions, Taylor Polynomials, l Hopital s Rule Some problems an solutions selecte or aapte from Hughes-Hallett Calculus. Critical Points. Consier the function f) = 54 +. b) a) Fin
More information3.7 Implicit Differentiation -- A Brief Introduction -- Student Notes
Fin these erivatives of these functions: y.7 Implicit Differentiation -- A Brief Introuction -- Stuent Notes tan y sin tan = sin y e = e = Write the inverses of these functions: y tan y sin How woul we
More informationx f(x) x f(x) approaching 1 approaching 0.5 approaching 1 approaching 0.
Engineering Mathematics 2 26 February 2014 Limits of functions Consier the function 1 f() = 1. The omain of this function is R + \ {1}. The function is not efine at 1. What happens when is close to 1?
More informationFinal Exam: Sat 12 Dec 2009, 09:00-12:00
MATH 1013 SECTIONS A: Professor Szeptycki APPLIED CALCULUS I, FALL 009 B: Professor Toms C: Professor Szeto NAME: STUDENT #: SECTION: No ai (e.g. calculator, written notes) is allowe. Final Exam: Sat 1
More informationIntroduction to the Vlasov-Poisson system
Introuction to the Vlasov-Poisson system Simone Calogero 1 The Vlasov equation Consier a particle with mass m > 0. Let x(t) R 3 enote the position of the particle at time t R an v(t) = ẋ(t) = x(t)/t its
More informationMath 210 Midterm #1 Review
Math 20 Miterm # Review This ocument is intene to be a rough outline of what you are expecte to have learne an retaine from this course to be prepare for the first miterm. : Functions Definition: A function
More informationThe derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x)
Y. D. Chong (2016) MH2801: Complex Methos for the Sciences 1. Derivatives The erivative of a function f(x) is another function, efine in terms of a limiting expression: f (x) f (x) lim x δx 0 f(x + δx)
More informationTable of Contents Derivatives of Logarithms
Derivatives of Logarithms- Table of Contents Derivatives of Logarithms Arithmetic Properties of Logarithms Derivatives of Logarithms Example Example 2 Example 3 Example 4 Logarithmic Differentiation Example
More informationMathcad Lecture #5 In-class Worksheet Plotting and Calculus
Mathca Lecture #5 In-class Worksheet Plotting an Calculus At the en of this lecture, you shoul be able to: graph expressions, functions, an matrices of ata format graphs with titles, legens, log scales,
More informationConvergence of Random Walks
Chapter 16 Convergence of Ranom Walks This lecture examines the convergence of ranom walks to the Wiener process. This is very important both physically an statistically, an illustrates the utility of
More informationInverse Functions. Review from Last Time: The Derivative of y = ln x. [ln. Last time we saw that
Inverse Functions Review from Last Time: The Derivative of y = ln Last time we saw that THEOREM 22.0.. The natural log function is ifferentiable an More generally, the chain rule version is ln ) =. ln
More informationLecture 10: October 30, 2017
Information an Coing Theory Autumn 2017 Lecturer: Mahur Tulsiani Lecture 10: October 30, 2017 1 I-Projections an applications In this lecture, we will talk more about fining the istribution in a set Π
More informationSummary: Differentiation
Techniques of Differentiation. Inverse Trigonometric functions The basic formulas (available in MF5 are: Summary: Differentiation ( sin ( cos The basic formula can be generalize as follows: Note: ( sin
More informationcosh x sinh x So writing t = tan(x/2) we have 6.4 Integration using tan(x/2) = 2 2t 1 + t 2 cos x = 1 t2 We will revisit the double angle identities:
6.4 Integration using tanx/) We will revisit the ouble angle ientities: sin x = sinx/) cosx/) = tanx/) sec x/) = tanx/) + tan x/) cos x = cos x/) sin x/) tan x = = tan x/) sec x/) tanx/) tan x/). = tan
More informationMath 115 Section 018 Course Note
Course Note 1 General Functions Definition 1.1. A function is a rule that takes certain numbers as inputs an assigns to each a efinite output number. The set of all input numbers is calle the omain of
More informationState-Space Model for a Multi-Machine System
State-Space Moel for a Multi-Machine System These notes parallel section.4 in the text. We are ealing with classically moele machines (IEEE Type.), constant impeance loas, an a network reuce to its internal
More informationCalculus and optimization
Calculus an optimization These notes essentially correspon to mathematical appenix 2 in the text. 1 Functions of a single variable Now that we have e ne functions we turn our attention to calculus. A function
More informationPhysics 505 Electricity and Magnetism Fall 2003 Prof. G. Raithel. Problem Set 3. 2 (x x ) 2 + (y y ) 2 + (z + z ) 2
Physics 505 Electricity an Magnetism Fall 003 Prof. G. Raithel Problem Set 3 Problem.7 5 Points a): Green s function: Using cartesian coorinates x = (x, y, z), it is G(x, x ) = 1 (x x ) + (y y ) + (z z
More informationYear 11 Matrices Semester 2. Yuk
Year 11 Matrices Semester 2 Chapter 5A input/output Yuk 1 Chapter 5B Gaussian Elimination an Systems of Linear Equations This is an extension of solving simultaneous equations. What oes a System of Linear
More informationChapter 3 Notes, Applied Calculus, Tan
Contents 3.1 Basic Rules of Differentiation.............................. 2 3.2 The Prouct an Quotient Rules............................ 6 3.3 The Chain Rule...................................... 9 3.4
More informationd dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1
Lecture 5 Some ifferentiation rules Trigonometric functions (Relevant section from Stewart, Seventh Eition: Section 3.3) You all know that sin = cos cos = sin. () But have you ever seen a erivation of
More informationYORK UNIVERSITY. Faculty of Science Department of Mathematics and Statistics. MATH A Test #2. June 25, 2014 SOLUTIONS
YORK UNIVERSITY Faculty of Science Department of Mathematics an Statistics MATH 505 6.00 A Test # June 5, 04 SOLUTIONS Family Name (print): Given Name: Stuent No: Signature: INSTRUCTIONS:. Please write
More informationCHAPTER 4. INTEGRATION 68. Previously, we chose an antiderivative which is correct for the given integrand 1/x 2. However, 6= 1 dx x x 2 if x =0.
CHAPTER 4. INTEGRATION 68 Previously, we chose an antierivative which is correct for the given integran /. However, recall 6 if 0. That is F 0 () f() oesn t hol for apple apple. We have to be sure the
More information1 dx. where is a large constant, i.e., 1, (7.6) and Px is of the order of unity. Indeed, if px is given by (7.5), the inequality (7.
Lectures Nine an Ten The WKB Approximation The WKB metho is a powerful tool to obtain solutions for many physical problems It is generally applicable to problems of wave propagation in which the frequency
More informationHyperbolic Functions. Notice: this material must not be used as a substitute for attending. the lectures
Hyperbolic Functions Notice: this material must not be use as a substitute for attening the lectures 0. Hyperbolic functions sinh an cosh The hyperbolic functions sinh (pronounce shine ) an cosh are efine
More informationRules of Differentiation
LECTURE 2 Rules of Differentiation At te en of Capter 2, we finally arrive at te following efinition of te erivative of a function f f x + f x x := x 0 oing so only after an extene iscussion as wat te
More information0.1 The Chain Rule. db dt = db
0. The Chain Rule A basic illustration of the chain rules comes in thinking about runners in a race. Suppose two brothers, Mark an Brian, hol an annual race to see who is the fastest. Last year Mark won
More informationMath Chapter 2 Essentials of Calculus by James Stewart Prepared by Jason Gaddis
Math 231 - Chapter 2 Essentials of Calculus by James Stewart Prepare by Jason Gais Chapter 2 - Derivatives 21 - Derivatives an Rates of Change Definition A tangent to a curve is a line that intersects
More informationIntroduction to variational calculus: Lecture notes 1
October 10, 2006 Introuction to variational calculus: Lecture notes 1 Ewin Langmann Mathematical Physics, KTH Physics, AlbaNova, SE-106 91 Stockholm, Sween Abstract I give an informal summary of variational
More informationCalculus of Variations
16.323 Lecture 5 Calculus of Variations Calculus of Variations Most books cover this material well, but Kirk Chapter 4 oes a particularly nice job. x(t) x* x*+ αδx (1) x*- αδx (1) αδx (1) αδx (1) t f t
More informationFURTHER BOUNDS FOR THE ESTIMATION ERROR VARIANCE OF A CONTINUOUS STREAM WITH STATIONARY VARIOGRAM
FURTHER BOUNDS FOR THE ESTIMATION ERROR VARIANCE OF A CONTINUOUS STREAM WITH STATIONARY VARIOGRAM N. S. BARNETT, S. S. DRAGOMIR, AND I. S. GOMM Abstract. In this paper we establish an upper boun for the
More informationSolutions to MATH 271 Test #3H
Solutions to MATH 71 Test #3H This is the :4 class s version of the test. See pages 4 7 for the 4:4 class s. (1) (5 points) Let a k = ( 1)k. Is a k increasing? Decreasing? Boune above? Boune k below? Convergant
More informationEuler equations for multiple integrals
Euler equations for multiple integrals January 22, 2013 Contents 1 Reminer of multivariable calculus 2 1.1 Vector ifferentiation......................... 2 1.2 Matrix ifferentiation........................
More informationPhysics 251 Results for Matrix Exponentials Spring 2017
Physics 25 Results for Matrix Exponentials Spring 27. Properties of the Matrix Exponential Let A be a real or complex n n matrix. The exponential of A is efine via its Taylor series, e A A n = I + n!,
More informationx f(x) x f(x) approaching 1 approaching 0.5 approaching 1 approaching 0.
Engineering Mathematics 2 26 February 2014 Limits of functions Consier the function 1 f() = 1. The omain of this function is R + \ {1}. The function is not efine at 1. What happens when is close to 1?
More informationMA 2232 Lecture 08 - Review of Log and Exponential Functions and Exponential Growth
MA 2232 Lecture 08 - Review of Log an Exponential Functions an Exponential Growth Friay, February 2, 2018. Objectives: Review log an exponential functions, their erivative an integration formulas. Exponential
More informationOn linear and non-linear equations.(sect. 2.4).
On linear and non-linear equations.sect. 2.4). Review: Linear differential equations. Non-linear differential equations. Properties of solutions to non-linear ODE. The Bernoulli equation. Review: Linear
More informationay (t) + by (t) + cy(t) = 0 (2)
Solving ay + by + cy = 0 Without Characteristic Equations, Complex Numbers, or Hats John Tolle Department of Mathematical Sciences Carnegie Mellon University Pittsburgh, PA 15213-3890 Some calculus courses
More informationStudents need encouragement. So if a student gets an answer right, tell them it was a lucky guess. That way, they develop a good, lucky feeling.
Chapter 8 Analytic Functions Stuents nee encouragement. So if a stuent gets an answer right, tell them it was a lucky guess. That way, they evelop a goo, lucky feeling. 1 8.1 Complex Derivatives -Jack
More informationHigher. Further Calculus 149
hsn.uk.net Higher Mathematics UNIT 3 OUTCOME 2 Further Calculus Contents Further Calculus 49 Differentiating sinx an cosx 49 2 Integrating sinx an cosx 50 3 The Chain Rule 5 4 Special Cases of the Chain
More informationIMPLICIT DIFFERENTIATION
IMPLICIT DIFFERENTIATION CALCULUS 3 INU0115/515 (MATHS 2) Dr Arian Jannetta MIMA CMath FRAS Implicit Differentiation 1/ 11 Arian Jannetta Explicit an implicit functions Explicit functions An explicit function
More information11.7. Implicit Differentiation. Introduction. Prerequisites. Learning Outcomes
Implicit Differentiation 11.7 Introuction This Section introuces implicit ifferentiation which is use to ifferentiate functions expresse in implicit form (where the variables are foun together). Examples
More informationθ x = f ( x,t) could be written as
9. Higher orer PDEs as systems of first-orer PDEs. Hyperbolic systems. For PDEs, as for ODEs, we may reuce the orer by efining new epenent variables. For example, in the case of the wave equation, (1)
More informationOptimization Notes. Note: Any material in red you will need to have memorized verbatim (more or less) for tests, quizzes, and the final exam.
MATH 2250 Calculus I Date: October 5, 2017 Eric Perkerson Optimization Notes 1 Chapter 4 Note: Any material in re you will nee to have memorize verbatim (more or less) for tests, quizzes, an the final
More informationSurvey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013
Survey Sampling Kosuke Imai Department of Politics, Princeton University February 19, 2013 Survey sampling is one of the most commonly use ata collection methos for social scientists. We begin by escribing
More information