Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations

Similar documents
Fourth Order RK-Method

Differential Equations

Lecture 20/Lab 21: Systems of Nonlinear ODEs

EECS 700: Exam # 1 Tuesday, October 21, 2014

Review Higher Order methods Multistep methods Summary HIGHER ORDER METHODS. P.V. Johnson. School of Mathematics. Semester

Differential Equations

Chapter 8. Numerical Solution of Ordinary Differential Equations. Module No. 1. Runge-Kutta Methods

Numerical Algorithms as Dynamical Systems

SYSTEMS OF ODES. mg sin ( (x)) dx 2 =

Solution to Homework #4 Roy Malka

Mini project ODE, TANA22

Ordinary Differential Equations

Graded Project #1. Part 1. Explicit Runge Kutta methods. Goals Differential Equations FMN130 Gustaf Söderlind and Carmen Arévalo

Two-Point Boundary Value Problem and Optimal Feedback Control based on Differential Algebra

Exam in TMA4215 December 7th 2012

Numerical Methods for ODEs. Lectures for PSU Summer Programs Xiantao Li

Physics: spring-mass system, planet motion, pendulum. Biology: ecology problem, neural conduction, epidemics

Nonlinear dynamics & chaos BECS

Chapter 11 ORDINARY DIFFERENTIAL EQUATIONS

BIFURCATION PHENOMENA Lecture 1: Qualitative theory of planar ODEs

Computational Methods CMSC/AMSC/MAPL 460. Ordinary differential equations

Stability of Nonlinear Systems An Introduction

AIMS Exercise Set # 1

Unit Ten Summary Introduction to Dynamical Systems and Chaos

Coupled differential equations

Lotka Volterra Predator-Prey Model with a Predating Scavenger

Lecture 42 Determining Internal Node Values

MATH10001 Mathematical Workshop Difference Equations part 2 Non-linear difference equations

ODE Runge-Kutta methods

. For each initial condition y(0) = y 0, there exists a. unique solution. In fact, given any point (x, y), there is a unique curve through this point,

Derivation of Itô SDE and Relationship to ODE and CTMC Models

Chaos in multiplicative systems

CHAPTER 2 POLYNOMIALS KEY POINTS

MATH 250 Homework 4: Due May 4, 2017

2D-Volterra-Lotka Modeling For 2 Species

Practice Problems for Final Exam

Simple ODE Solvers - Derivation

Math 216 Final Exam 14 December, 2012

Nonlinear Systems and Control Lecture # 12 Converse Lyapunov Functions & Time Varying Systems. p. 1/1

Astronomy 8824: Numerical Methods Notes 2 Ordinary Differential Equations

RATIO BASED PARALLEL TIME INTEGRATION (RAPTI) FOR INITIAL VALUE PROBLEMS

CDS 101 Precourse Phase Plane Analysis and Stability

B5.6 Nonlinear Systems

Calculus C (ordinary differential equations)

Physics 115/242 Comparison of methods for integrating the simple harmonic oscillator.

Introduction to standard and non-standard Numerical Methods

Lecture 10: Singular Perturbations and Averaging 1

Modeling & Simulation 2018 Lecture 12. Simulations

AM205: Assignment 3 (due 5 PM, October 20)

Introduction to Dynamical Systems Basic Concepts of Dynamics

MATH 104: INTRODUCTORY ANALYSIS SPRING 2008/09 PROBLEM SET 8 SOLUTIONS

Differential Equations

The Big, Big Picture (Bifurcations II)

Backward error analysis

Differential Equations FMNN10 Graded Project #1 c G Söderlind 2017

Second Order ODEs. CSCC51H- Numerical Approx, Int and ODEs p.130/177

1. < 0: the eigenvalues are real and have opposite signs; the fixed point is a saddle point

Math 225 Differential Equations Notes Chapter 1

Degree Master of Science in Mathematical Modelling and Scientific Computing Mathematical Methods I Thursday, 12th January 2012, 9:30 a.m.- 11:30 a.m.

7 Planar systems of linear ODE

CHAPTER 80 NUMERICAL METHODS FOR FIRST-ORDER DIFFERENTIAL EQUATIONS

Ordinary Differential Equations (ODEs)

CPS 5310: Parameter Estimation. Natasha Sharma, Ph.D.

TP 3:Runge-Kutta Methods-Solar System-The Method of Least Squares

Stability and bifurcation in a two species predator-prey model with quintic interactions

Computational Physics (6810): Session 8

Ordinary Differential Equations (ODEs)

Introduction to the Numerical Solution of SDEs Selected topics

δ Substituting into the differential equation gives: x i+1 x i δ f(t i,x i ) (3)

10.34: Numerical Methods Applied to Chemical Engineering. Lecture 19: Differential Algebraic Equations

Propagation of Uncertainties in Nonlinear Dynamic Models

CS520: numerical ODEs (Ch.2)

PowerPoints organized by Dr. Michael R. Gustafson II, Duke University

Differential Equations DIRECT INTEGRATION. Graham S McDonald

Solution: (a) Before opening the parachute, the differential equation is given by: dv dt. = v. v(0) = 0

Streamline calculations. Lecture note 2

Fundamentals Physics

Exercises, module A (ODEs, numerical integration etc)

Numerical Methods for the Solution of Differential Equations

MAS212 Scientific Computing and Simulation

154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below.

Introduction to multiscale modeling and simulation. Explicit methods for ODEs : forward Euler. y n+1 = y n + tf(y n ) dy dt = f(y), y(0) = y 0

Exam in TMA4195 Mathematical Modeling Solutions

(1) Rate of change: A swimming pool is emptying at a constant rate of 90 gal/min.

A Model of Evolutionary Dynamics with Quasiperiodic Forcing

CHALMERS, GÖTEBORGS UNIVERSITET. EXAM for DYNAMICAL SYSTEMS. COURSE CODES: TIF 155, FIM770GU, PhD

Numerical Methods for Ordinary Differential Equations

1. (i) Determine how many periodic orbits and equilibria can be born at the bifurcations of the zero equilibrium of the following system:

Cyber-Physical Systems Modeling and Simulation of Continuous Systems

Example Chaotic Maps (that you can analyze)

STABILITY. Phase portraits and local stability

The Definition and Numerical Method of Final Value Problem and Arbitrary Value Problem Shixiong Wang 1*, Jianhua He 1, Chen Wang 2, Xitong Li 1

Alberto Bressan. Department of Mathematics, Penn State University

Initial Value Problems

Checking the Radioactive Decay Euler Algorithm

Tangent Plane. Nobuyuki TOSE. October 02, Nobuyuki TOSE. Tangent Plane

Numerical Methods for Differential Equations Mathematical and Computational Tools

Nonlinear Dynamics. Moreno Marzolla Dip. di Informatica Scienza e Ingegneria (DISI) Università di Bologna.

1. Consider the initial value problem: find y(t) such that. y = y 2 t, y(0) = 1.

MA 138 Calculus 2 for the Life Sciences Spring 2016 Final Exam May 4, Exam Scores. Question Score Total

Transcription:

Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations S. Y. Ha and J. Park Department of Mathematical Sciences Seoul National University Sep 23, 2013

Contents 1 Logistic Map 2 Euler and Runge-Kutta Method Euler Method Runge-Kutta Method Euler vs. Runge-Kutta R-K Method for System ODEs 3 Lotka-Volterra Equations

Logistic Map Definition Let f : [0, 1] [0, 1] such that f(x) = rx(1 x) where r [0, 4]. Then f is called a logistic map. 3 / 25

Logistic Map f(x) = rx(1 x) Intersections are fixed points. 4 / 25

Two iterations of logistic map f 2 (x) Intersections are fixed points and period-2 points. 5 / 25

Three iterations of logistic map f 3 (x) Intersections are fixed points and period-3 points. 6 / 25

Comparison of five iterations of logistic map f 5 (x) with repect to r r=2.5 r=3 r=3.5 r=4 7 / 25

Bifurcation of positions of fixed points and period-2 points The figure indicates positions of x satisfying x = f 2 (x) with respect to 0 r 4. 8 / 25

Doubling iterations The figure indicates positions of x satisfying x = f 8 (x) with respect to 2.8 r 3.6. 9 / 25

Let x 0 = 0.5 and x n = f n (x 0 ). Plot positions of x n while n [500, 1500]. The figure indicates positions that x n wander with respect to 3.5 r 4. 10 / 25

Euler Method Euler and Runge-Kutta Method Let given ODE be ẋ = f(x, t) and t = h. Explicit Euler Method dx dt x n+1 x n h x n+1 x n = f(x n, t n ) h x n+1 = x n + hf(x n, t n ), t n+1 = t n + h h must be small. Implicit Euler Method x n+1 x n = f(x n+1, t n+1 ) h x n+1 = g(x n, t n ), t n+1 = t n + h It is difficult to find out g. 11 / 25

Runge-Kutta Method 4th Order Runge-Kutta Method x n+1 x n h = 1 6 (k 1 + 2k 2 + 2k 3 + k 4 ) x n+1 = x n + 1 6 h(k 1 + 2k 2 + 2k 3 + k 4 ) weighted average of k 1, k 2, k 3, k 4 where, k1 = f(x n, t n ) k2 = f(x n + 1 2 hk 1, t n + 1 2 h) k3 = f(x n + 1 2 hk 2, t n + 1 2 h) k4 = f(x n + hk 3, t n + h) t n+1 = t n + h 12 / 25

Euler vs. Runge-Kutta Solve ẋ = x(1 x). Exact solution is x(t) = Trajectory of x(t) with x 0 = 0.05 x 0 e t 1 + x 0 (e t 1) 13 / 25

Euler vs. Runge-Kutta Let x 0 = 0.05, h = 0.01. Trajectories of x(t) with Euler and Runge-Kutta methods Euler method Runge-Kutta method 14 / 25

Euler vs. Runge-Kutta Difference from exact solution Error of Euler method is less than 2.5 10 3 Error of Runge-Kutta method is less than 2.5 10 11 15 / 25

Euler vs. Runge-Kutta Using 4th order Runge-Kutta method needs 4 times more calculations than using Euler method. Let h 1 = 0.01 for Runge-Kutta method and h 2 = 0.0025 for Euler method in order to adjust the number of calculations. Error of Euler method is less than 6 10 4 Error of Runge-Kutta method is less than 2.5 10 11 16 / 25

R-K Method for System ODEs Runge-Kutta Method for System ODEs A system ODE ẋ = f(x, y, t) ẏ = g(x, y, t) Runge-Kutta method for the system x n+1 = x n + 1 6 h(k 1 + 2k 2 + 2k 3 + k 4 ) y n+1 = y n + 1 6 h(l 1 + 2l 2 + 2l 3 + l 4 ) 17 / 25

R-K Method for System ODEs where k1 = f(x n, y n, t n ), l1 = g(x n, y n, t n ), k2 = f(x n + 1 2 hk 1, y n + 1 2 hl 1, t n + 1 2 h), l2 = g(x n + 1 2 hk 1, y n + 1 2 hl 1, t n + 1 2 h), k3 = f(x n + 1 2 hk 2, y n + 1 2 hl 2, t n + 1 2 h), l3 = g(x n + 1 2 hk 2, y n + 1 2 hl 2, t n + 1 2 h), k4 = f(x n + hk 3, y n + hl 3, t n + h), l4 = g(x n + hk 3, y n + hl 3, t n + h), t n+1 = t n + h. 18 / 25

R-K Method for System ODEs Higher order ODE can be transformed into a system ODE Let x 1 := x, x 2 := ẋ then ẍ + aẋ + b = 0 ẋ 1 = x 2 ẋ 2 = ax 1 b 19 / 25

Lotka-Volterra Equations Lotka-Volterra equation ẋ = x(a by) ẏ = y( c + dx) with positive a, b, c, d. We use 4th order Runge-Kutta method for calculations. 20 / 25

Set a = 4, b = 2, c = 3, d = 1 and h = 0.01. We plot 10000 iterations starting at square shaped points. We plot o shape at every 10 iterations. In this system, the phase follow the orbits counterclockwise. If the phase is far from the point (3, 2), the phase moves fast. 21 / 25

Lotka-Volterra equation with small perturbations. ẋ = x(a ε 1 x by) ẏ = y( c + dx ε 2 y) Set a = 4, b = 2, c = 3, d = 1, ε 1 = 0.0001, ε 2 = 0.0001. In this system, the phase moves to (3, 2) slowly. 22 / 25

Lotka-Volterra equation with intraspecific competition. ẋ = x(a ex by) ẏ = y( c + dx fy) Set a = 6, b = 3, c = 4, d = 1, e = 2, f = 1. 23 / 25

Lotka-Volterra equation with intraspecific competition. ẋ = x(a ex by) ẏ = y( c + dx fy) Set a = 18, b = 2, c = 1, d = 1, e = 3, f = 1. 24 / 25

Thank You 25 / 25