(a) Implement the trapeziodal method (see p423) to solve the stiff system 7.97, with the initial condition y(0.2) = [0.33530156, 0.33501848, 0.0003807 ], going to t=5 with h=.05. Use a fixed number of Newton iterations. Since the DE happens to be linear, Newton will actually solve the system in 1 iteration. Plot your solution.

(b) Compare with using Euler's method with the same step size.

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(a) Implement the trapeziodal method (see p423) to solve the stiff system,

$$Y^\prime = \begin{bmatrix} x^\prime\\ y^\prime\\ z^\prime \end{bmatrix} = \begin{bmatrix} -y\\ x\\ -100(z-x^2-y^2) \end{bmatrix}.$$with the initial condition $Y(0) = [3,0,18]^T$, going to t=5 with h=.05. Use a fixed number of Newton iterations. Plot your solution.

(b) Compare with using Euler's method with the same step size.

Compose your formal answer using GoodNotes, Notability, OneNote, Xournal, or a similar tool of your choice, with text, code, graphics all displayed in an integrated way so that your "story" can be read in one pass. Upload this to Gradescope.

Also upload your code to UBlearns.

You need to create functions like:

In [1]:

```
def f(t,y):
...
def Df(t,y):
...
```

To solve the matrix-vector equation Ax=b:

`np.linalg.solve(A, b)`

To get the identity matrix:

`np.eye(3)`

Calculate the Newton step $s = -Dg^{-1} g$ by solving $Dg\ s = -g$ for $s$.

Don't confuse the jacobian of the function $g$ whose root you want to find with the jacobian of $f$ (the right hand side of the differential equation), though the former is expressed simply in terms of the latter.

In [ ]:

```
```