Picard iteration on systems of DEs

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In summary, the conversation discusses finding an approximate solution for the system u' = v, v' = −u with initial conditions u(0) = 1 and v(0) = 0 by performing 4 steps of Picard iteration. The correct method for extending Picard iteration to systems is explained and demonstrated.
  • #1
TaliskerBA
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Homework Statement



For the system
u' = v, v' = −u
with initial conditions u(0) = 1 and v(0) = 0, find an approximate solution by
performing 4 steps of Picard iteration. Compare the results with the actual solution.

Homework Equations



In general:

y'= f(x,y), y(x0)= y0

y(x) = y(x0) + [tex]\int[/tex] f(t,y(t)) .dt with x and x0 the upper and lower points of the integral (couldn't work out how to format this in)

The Attempt at a Solution



I know how to do picard iteration for a single first-order equation but don't know how to extend it to systems. To be honest my attempt is probably so far off it's not worth writing but here it is anyway.

u(t) = (x0) + [tex]\int[/tex] v(s)ds with t and t0 the upper and lower points of the integral (I won't write this below but these will always be the lower and upper points). As v0 = 0 I proceed as follows:

u0 = 1 + [tex]\int[/tex] 0ds = 1
u1 = 1 + [tex]\int[/tex] 1.ds = 1 + t
u2 = 1 + [tex]\int[/tex](1 + s)ds = 1 + t + t2/2

etc.

I'm way off the answer given in the book. Please someone offer a rough explanation!

Thanks
 
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  • #2
bump. Help!
 
  • #3
Bumping is a good way to get yourself banned.

What you have done is wrong because you have neglected the "v" after the first step.
[itex]u_n= u_0+ \int v_{n-1}dx[/itex] and [itex]v_n= v(0)- \int u_{n-1}dx[/itex]
u(0)= 1 and v(0)= 0 so the first step gives
[itex]u_1(x)= 1+ \int 0dx= 1[/itex], [itex]v_(x)= 0- \int 1dx= -x[/itex].

Now, the second step:
[itex]u_2(x)= 1+ \int -xdx= 1- (1/2)x^2[/itex], [itex]v_2(x)= 0- \int 1 dx= -x[/itex]

Third step:
[itex]u_3(x)= 1+ \int -x dx= 1- (1/2)x^2[/itex], [itex]v_3= 0- \int 1-(1/2)x^2 dx= -x+ (1/6)x^3[/itex]
 
  • #4
Thanks very much for your help, I get it now. I'm sorry about bumping I didn't know it was against the rules. I won't do it again.
 

FAQ: Picard iteration on systems of DEs

What is the Picard iteration method for solving systems of differential equations?

The Picard iteration method is an iterative numerical method used to solve systems of differential equations. It involves breaking down the system into a sequence of simpler problems and solving each one iteratively to approximate the solution of the original system.

How does the Picard iteration method work?

The Picard iteration method works by starting with an initial guess for the solution of the system and then using this guess to calculate an improved estimate for the solution. This process is repeated until the solution converges to a desired level of accuracy.

What are the advantages of using Picard iteration for solving systems of DEs?

The advantages of using Picard iteration include its simplicity and efficiency. It is also a versatile method that can be applied to a wide range of systems of differential equations. Additionally, it is computationally less expensive compared to other numerical methods.

Are there any limitations or drawbacks to using the Picard iteration method?

One limitation of the Picard iteration method is that it may not always converge to a solution for certain types of systems of differential equations. This can be overcome by using other numerical methods or adjusting the initial guess. It can also be time-consuming for systems with a large number of equations.

How is the convergence of the Picard iteration method determined?

The convergence of the Picard iteration method is determined by monitoring the difference between successive iterations of the solution. If the difference decreases with each iteration, then the method is converging towards a solution. The desired level of accuracy can also be used to determine when the iteration process should stop.

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