3) Numerical Solution of ODEs Flashcards
(41 cards)
What is a Lipschitz condition for a function
When can the Lipschitz constant L be taken as the partial derviate with respect to y
What does Picard’s theorem state about the existence and uniqueness of solutions to differential equations
What is Euler’s method for solving differential equations
What is a one-step method and a multistep method in numerical integration
One-step Method - the step from (xn,yn) to (xn+1 ,yn+1) is computed using only the current values (xn ,yn), with no use of previously computed information (xk ,yk) for k < n
Multistep Method - use previously computed values
What is the general form of a one-step method
What is the Backward Euler method
How does the Backward Euler method fit into the definition of a one-step method
What is the Trapezium method for solving differential equations
What is the local truncation error for a one-step method, and what does it mean for the method to be of order p
How do we prove that Euler’s method has order p=1
How do we show that the trapezium rule has order p=2
What is the order of accuracy and the local truncation error for Euler’s method, the backward Euler method, and the trapezium method
What does it mean for a method to be consistent
A method is consistent if it has order p≥1. This means that the local truncation error goes to zero as the step size h→0
What is global error
The quantity en = y(xn) - yn
What does it mean for a method to be convergent
What is the global error for Euler’s method at any grid point xn
How do we derive the global error bound for Euler’s method
What is Heun’s method (also known as the improved Euler method)
What is the general form of a 2-stage Runge–Kutta method and the general m-stage Runge–Kutta method
How do you do the Taylor expansion of a multivariable function
f(xn + Δx,yn +Δy) =f(x ,yn)+Δx⋅fx (xn,yn)+Δy⋅fy(xn,yn)
What are the conditions for the 2-stage Runge–Kutta method to be of order 2
What are embedded Runge–Kutta methods and how do they help with error estimation
What is the general form of Linear Multistep (LM) methods