MA108 ODE: Picard s Theorem

Similar documents
Section 2.8: The Existence and Uniqueness Theorem

First and Second Order Differential Equations Lecture 4

y + p(t)y = g(t) for each t I, and that also satisfies the initial condition y(t 0 ) = y 0 where y 0 is an arbitrary prescribed initial value.

Ordinary Differential Equations

Ordinary Differential Equation Theory

First Order ODEs, Part II

First Order Differential Equations Lecture 3

Chapter 2: First Order DE 2.4 Linear vs. Nonlinear DEs

Problem 1 In each of the following problems find the general solution of the given differential

Section 2.1 (First Order) Linear DEs; Method of Integrating Factors. General first order linear DEs Standard Form; y'(t) + p(t) y = g(t)

Picard s Existence and Uniqueness Theorem

Ch 6.4: Differential Equations with Discontinuous Forcing Functions

ODEs Cathal Ormond 1

Chapter 4: Higher Order Linear Equations

2tdt 1 y = t2 + C y = which implies C = 1 and the solution is y = 1

Solutions to Math 53 First Exam April 20, 2010

Partial proof: y = ϕ 1 (t) is a solution to y + p(t)y = 0 implies. Thus y = cϕ 1 (t) is a solution to y + p(t)y = 0 since

Linear Independence and the Wronskian

Lecture Notes for Math 251: ODE and PDE. Lecture 7: 2.4 Differences Between Linear and Nonlinear Equations

Theory of Ordinary Differential Equations

On linear and non-linear equations.(sect. 2.4).

On linear and non-linear equations. (Sect. 1.6).

First Order ODEs, Part I

Homogeneous Equations with Constant Coefficients

Lecture Notes in Mathematics. Arkansas Tech University Department of Mathematics

MATH 308 Differential Equations

Lectures in Differential Equations

Metric Spaces and Topology

Mathematical Models. MATH 365 Ordinary Differential Equations. J. Robert Buchanan. Spring Department of Mathematics

Mathematical Models. MATH 365 Ordinary Differential Equations. J. Robert Buchanan. Fall Department of Mathematics

The Laplace Transform

Lecture 16. Theory of Second Order Linear Homogeneous ODEs

Linear Variable coefficient equations (Sect. 2.1) Review: Linear constant coefficient equations

THE INVERSE FUNCTION THEOREM

SPACES ENDOWED WITH A GRAPH AND APPLICATIONS. Mina Dinarvand. 1. Introduction

2nd-Order Linear Equations

Lecture Notes 1. First Order ODE s. 1. First Order Linear equations A first order homogeneous linear ordinary differential equation (ODE) has the form

Lecture 31. Basic Theory of First Order Linear Systems

(1 + 2y)y = x. ( x. The right-hand side is a standard integral, so in the end we have the implicit solution. y(x) + y 2 (x) = x2 2 +C.

MATH 353 LECTURE NOTES: WEEK 1 FIRST ORDER ODES

Section 3.1 Second Order Linear Homogeneous DEs with Constant Coefficients

Page 684. Lecture 40: Coordinate Transformations: Time Transformations Date Revised: 2009/02/02 Date Given: 2009/02/02

2.1 Differential Equations and Solutions. Blerina Xhabli

The integrating factor method (Sect. 1.1)

MATH 2413 TEST ON CHAPTER 4 ANSWER ALL QUESTIONS. TIME 1.5 HRS.

Linear Variable coefficient equations (Sect. 1.2) Review: Linear constant coefficient equations

Stochastic Differential Equations.

Lecture notes on Ordinary Differential Equations. S. Sivaji Ganesh Department of Mathematics Indian Institute of Technology Bombay

Ordinary Differential Equations

DIFFERENTIAL INEQUALITIES AND EXISTENCE THEORY FOR DIFFERENTIAL, INTEGRAL, AND DELAY EQUATIONS. T.A. Burton

MATH1013 Calculus I. Introduction to Functions 1

Non-linear wave equations. Hans Ringström. Department of Mathematics, KTH, Stockholm, Sweden

UNIVERSITY OF MANITOBA

Math 23 Practice Quiz 2018 Spring

APPLICATIONS OF INTEGRATION

SMA 208: Ordinary differential equations I

Old Math 330 Exams. David M. McClendon. Department of Mathematics Ferris State University

Math Homework 3 Solutions. (1 y sin x) dx + (cos x) dy = 0. = sin x =

Notes on uniform convergence

2.1 Dynamical systems, phase flows, and differential equations

Synchronization, Chaos, and the Dynamics of Coupled Oscillators. Supplemental 1. Winter Zachary Adams Undergraduate in Mathematics and Biology

Section 2.1 Differential Equation and Solutions

APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS

Fall 2001, AM33 Solution to hw7

Essential Ordinary Differential Equations

Additional Homework Problems

Series Solutions. 8.1 Taylor Polynomials

2. First Order Linear Equations and Bernoulli s Differential Equation

z x = f x (x, y, a, b), z y = f y (x, y, a, b). F(x, y, z, z x, z y ) = 0. This is a PDE for the unknown function of two independent variables.

The Fundamental Theorem of Calculus: Suppose f continuous on [a, b]. 1.) If G(x) = x. f(t)dt = F (b) F (a) where F is any antiderivative

Non-asymptotic Analysis of Bandlimited Functions. Andrei Osipov Research Report YALEU/DCS/TR-1449 Yale University January 12, 2012

be the set of complex valued 2π-periodic functions f on R such that

4. Higher Order Linear DEs

Linear ODEs. Existence of solutions to linear IVPs. Resolvent matrix. Autonomous linear systems

b) The system of ODE s d x = v(x) in U. (2) dt

= 2e t e 2t + ( e 2t )e 3t = 2e t e t = e t. Math 20D Final Review

AN EFFECTIVE METRIC ON C(H, K) WITH NORMAL STRUCTURE. Mona Nabiei (Received 23 June, 2015)

Existence and uniqueness: Picard s theorem

Calculus and Parametric Equations

MA5510 Ordinary Differential Equation, Fall, 2014

Math Ordinary Differential Equations

DYNAMICAL SYSTEMS AND NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS

Agenda Sections 2.4, 2.5

US01CMTH02 UNIT-3. exists, then it is called the partial derivative of f with respect to y at (a, b) and is denoted by f. f(a, b + b) f(a, b) lim

Math 342 Partial Differential Equations «Viktor Grigoryan

Math 353 Lecture Notes Week 6 Laplace Transform: Fundamentals

Study of Existence and uniqueness of solution of abstract nonlinear differential equation of finite delay

MATH 819 FALL We considered solutions of this equation on the domain Ū, where

Solutions Definition 2: a solution

Solutions of Math 53 Midterm Exam I

DIfferential equations of fractional order have been the

Solving the Heat Equation (Sect. 10.5).

Sign the pledge. On my honor, I have neither given nor received unauthorized aid on this Exam : 11. a b c d e. 1. a b c d e. 2.

Lecture 2. Introduction to Differential Equations. Roman Kitsela. October 1, Roman Kitsela Lecture 2 October 1, / 25

MATH 173: PRACTICE MIDTERM SOLUTIONS

The Laplace Transform (Sect. 4.1). The Laplace Transform (Sect. 4.1).

Series Solutions Near an Ordinary Point

Solutions to Homework 1, Introduction to Differential Equations, 3450: , Dr. Montero, Spring y(x) = ce 2x + e x

First Order Differential Equations

Lecture 6. Differential Equations

Transcription:

MA18 ODE: Picard s Theorem Preeti Raman IIT Bombay MA18

Existence and Uniqueness The IVP s that we have considered usually have unique solutions. This need not always be the case. MA18

Example Example: Consider the IVP dy dx = y 1 3 ; y() =. This is a separable first order DE. Hence, separating the variables, we get: y 1 3 dy = dx; i.e., 3 2 y 2 3 = x +c, or [ ]3 2 2 y = (x +c). 3 The initial condition y() = gives c =. Hence [ ]3 2x 2 y = φ(x) = ; x 3 is a solution of the given IVP. MA18

Example continued [ ]3 2x 2 y = φ(x) = ; x 3 is a solution of the IVP dy dx = y 1 3 ; y() =. Check: [ ]3 2x 2 y = φ(x) = ; x 3 is also a solution of the IVP. Check: y = ψ(x) is also a solution of the IVP. Check: For any a >, { if x [,a) y = φ a (x) = ± [ 2 3 (x a)]3 2 if x a is continuous, differentiable, and gives a solution of the given IVP. MA18

Existence and Uniqueness That is, we get infinitely many solutions of the given IVP. MA18

Example Example: Consider the IVP dy dx = 3x2 +4x +2 ; y() = 1. 2y 2 This is a separable non-linear first order DE. Hence, separating the variables, we get: Integrating this equation, we get (2y 2)dy = (3x 2 +4x +2)dx. y 2 2y = x 3 +2x 2 +2x +c. The initial condition y() = 1 implies that c = 1. Hence i.e., (y 1) 2 = x 3 +2x 2 +2x, y = 1± x 3 +2x 2 +2x. This equation gives us two functions satisfying the given IVP. MA18

Existence and Uniqueness Consider the IVP y 1 = f(t,y); y(t ) = y. We study the existence and uniqueness of solutions for the above IVP. By a linear translation of coordinates, we may assume that the initial condition given is y() =. Theorem Consider the IVP Suppose f and f y y 1 = f(t,y); y() =. are continuous on the rectangle a t a, b y b. Then there is an interval I = [ h,h] [ a,a] and a function y = φ(t) : I R which is the unique solution of the IVP. MA18

Existence and Uniqueness Note: Compare the example given earlier with the hypotheses of the above theorem. MA18

Example Example: Solve the IVP: y 1 = y 2 ; y() = 1 and find the interval in which the solution exists. As f(t,y) = y 2 & f y = 2y are continuous everywhere, the IVP has a unique solution on an interval containing t = by the above theorem. The given DE is separable and we have dy y 2 = dt. Thus, 1 y = t +c; i.e., y = 1 t +c. MA18

Example continued The initial condition y() = 1 gives c = 1. Hence y(t) = 1 1 t is the solution to the given DE. This function is unbounded as t 1. Hence, y : (,1) R defined by is the solution to the given IVP. y(t) = 1 1 t MA18

Corollary Consider the IVP y 1 +p(t)y = g(t); y(t ) = y, where p and g are continuous functions on an interval I with t I. Then there is a unique solution on I of the given IVP. Proof. Since y 1 = p(t)y g(t), it follows that f(t,y) = p(t)y g(t) and f y are both continuous on I R. By the existence and uniqueness theorem, the given IVP has a unique solution on a subinterval J I with t J. MA18

Picard s Iteration Method Picard s iteration method gives us a rough idea on how to construct solutions to IVP s. Consider the IVP y 1 = f(t,y); y() =. Suppose y = φ(t) is a solution to the IVP. Then, dφ dt = f(t,φ(t)), φ() =. That is, φ(t) = f(s,φ(s))ds; φ() =. The above equation is called an integral equation in the unknown function φ. MA18

Picard s Iteration Method Conversely, if the integral equation holds i.e., φ(t) = f(s,φ(s))ds; φ() =, then by the Fundamental Theorem of Calculus, dφ dt = f(t,φ(t)), so that y = φ(t) is a solution to the IVP y 1 = f(t,y); y() =. Thus, solving the integral equation is equivalent to solving the IVP. MA18

Picard s Iteration Method Picard s iteration describes a way to look for solutions of the integral equation φ(t) = f(s,φ(s))ds. We define iteratively a sequence of functions φ n (t) for every integer n as follows: Let φ (t) φ 1 (t) = φ 2 (t) = φ n+1 (t) = f(s,φ (s))ds f(s,φ 1 (s))ds f(s,φ n (s))ds. MA18

Picard s Iteration Method Note: Each φ n satisfies the initial condition φ n () =. None of the φ n may satisfy y 1 = f(t,y). Suppose for some n, φ n+1 = φ n. Then, φ n+1 = φ n = f(s,φ n (s))ds, and this implies d dt (φ n(t)) = f(t,φ n (t)) is a solution of the given IVP. In general, the sequence {φ n } may not terminate. In fact, all the φ n may not even be defined outside a small region in the domain. However, it is possible to show that, under the hypotheses of the above theorem, the sequence converges to a function φ(t) = lim n φ n(t) which is the unique solution to the given IVP. MA18

Example Example: Solve the IVP: y 1 = 2t(1+y); y() =. The corresponding integral equation is φ(t) = Let φ (t). Then, 2s(1+φ(s))ds. φ 1 (t) = φ 2 (t) = φ 3 (t) = 2sds = t 2, 2s(1+s 2 )ds = t 2 + t4 2, 2s(1+s 2 + s4 2 )ds = t2 + t4 2 + t6 6. MA18

Example continued We claim: φ n (t) = t 2 + t4 2 + t6 6 Use induction to prove this: +...+ t2n n!. φ n+1 (t) = = 2s(1+φ n (s))ds 2s (1+s 2 + s4 2 = t 2 + t4 2 + t6 6 ) s2n +...+ ds n! +...+ t2n n! + t2n+2 (n+1)!. Hence φ n (t) is the n-th partial sum of the series k=1 t 2k k!. MA18

Example continued Recall that φ n (t) is the n-th partial sum of the series Applying the ratio test, we get: t 2k+2 (k +1)! k! t 2k = t2 k +1 for all t as k. Thus, lim φ n(t) = n k=1 t 2k k! = et2 1. Hence, y(t) = e t2 1 is a solution of the IVP. k=1 t 2k k!. MA18

Example continued Uniqueness of solution: Suppose the IVP y 1 = 2t(1+y),y() = has two solutions φ and ψ.thus, both these satisfy the integral equation as well, i.e., φ(t) = It follows that 2s ( 1+φ(s) ) ds and ψ(t) = φ(t) ψ(t) 2s ( φ(s) ψ(s) ) ds. 2s ( 1+ψ(s) ) ds. Suppose we restrict the solutions to the interval t A/2, for an arbitrary constant A. Then φ(t) ψ(t) 2s φ(s) ψ(s) ds A φ(s) ψ(s) ds. MA18

Example continued Let φ(t) ψ(t) A U(t) = φ(s) ψ(s) ds. φ(s) ψ(s) ds. Then U() =, U(t) for all t and U (t) = φ(t) ψ(t).so, Thus: or equivalently, that U (t) AU(t). e At U (t) e At AU(t) [e At U(t)]. Integrating from to, t we get d ( ) e At U(s) ds ds which implies that e At U(t) for all t. Hence U(t) and U(t), and so φ(t) ψ(t) for all t. MA18