CSC338. Homework 8
Due Date: Wednesday March 18, 9pm
Please see the guidelines at https://www.cs.toronto.edu/~lczhang/338/homework.html
What to Hand In
Please hand in 2 files:
• Python File containing all your code, named hw8.py.
• PDF file named hw8_written.pdf containing your solutions to the written parts of the assignment. Your
solution can be hand-written, but must be legible. Graders may deduct marks for illegible or poorly presented solutions.
If you are using Jupyter Notebook to complete the work, your notebook can be exported as a .py file (File -> Download As -> Python). Your code will be auto-graded using Python 3.6, so please make sure that your code runs. There will be a 20% penalty if you need a remark due to small issues that renders your code untestable.
Make sure to remove or comment out all matplotlib or other expensive code before submitting your homework!
Submit the assignment on MarkUs by 9pm on the due date. See the syllabus for the course policy regarding late assignments. All assignments must be done individually.
import math
import numpy as np
Question 1. Fixed-Point Iteration – 3pts
In homework 7, we considered using fixed-point iteration to find a root of the equation f (x) = x2 − 3x + 2 = 0 using these functions:
1. g1(x) = x2+2 √3
2. g2(x)= 3x−2 3. g3(x) = 3 − 2
x
Analyze the convergence properties of each of the corresponding fixed-point iteration schemes for the root x = 2 by analyzing gi′(x). Do you expect the fix-point iteration to diverge or converge? Do these expectations match your empirical results from homework 7?
Question 2. Newton’s Method
Part (a) – 4 pts
Write a function newton to find a root of f(x) using Newton’s Method. This Python function should take as argument both the mathematical function f and its derivative df, and return a list of successively better estimates of a root of f obtained from applying Newton’s method.
def newton(f, df, x, n=5):
“”” Return a list of successively better estimate of a root of `f` obtained from applying Newton’s method. The argument `df` is the derivative of the function `f`. The argument `x` is the initial estimate of the root. The length of the returned list is `n + 1`.
Precondition: f is continuous and differentiable
df is the derivative of f
>>> def f(x):
1
… return x * x – 4 * np.sin(x)
>>> def df(x):
… return 2 * x – 4 * np.cos(x)
>>> newton(f, df, 3, n=5)
[3,
2.1530576920133857,
1.9540386420058038,
1.9339715327520701,
1.933753788557627,
1.9337537628270216]
“””
Part (b) – 2 pts
Use your function from part (a) to solve for a root of
f(x)=x2 −3x+2=0
Start with x0 = 3, and stop when the root is accurate to at least 8 significant decimal digits.
Show your work in your python file, and store the root you find in the variable newton_root.
def f(x):
return x ** 2 – 3 * x + 2
newton_root = None Part (c) – 6 pts
Consider the following non-linear equations hi(x) = 0.
1. h1(x)=x3 −5×2 +8x−4 2. h2(x) = xcos(20x) − x
3. h3(x)=e−2x +ex −x−4
Write out the statement for updating the iterate xk using Newton’s method for solving each of the equations hi(x) = 0. For each hi, do you expect Newton’s method to converge?
Include your solution in your PDF writeup.
Part (d) – 3 pt
Use the newton function to try and solve hi(x) = 0, for n = 100 iterations, starting with x = 1.5.
Save the return values of calls to the function newton to the variables newton_h1, newton_h2, newton_h3,.
newton_h1 = None
newton_h2 = None
newton_h3 = None
Question 3. Secant Method
Part (a) [5 pt]
Write a function secant to find a root of f(x) using the secant method. The function should return a list of successively better estimates of a root of f obtained from applying the secant method.
2
def secant(f, x0, x1, n=5):
“”” Return a list of successively better estimate of a root of `f` obtained from applying secant method. The arguments `x0` and `x1` are the two starting guesses. The length of the returned list is `n + 2`.
>>> secant(lambda x: x ** 2 + x – 4, 3, 2, n=6)
[3,
2,
1.6666666666666667,
1.5714285714285714,
1.5617977528089888,
1.5615533980582523,
1.561552812843596,
1.5615528128088303]
“””
Part (b) [1 pt]
Use the secant function to find a root of f(x) = x3 + x2 + x − 4, accurate up to 8 significant decimal digits. Show your work in your Python file. You can choose starting positions x0 and x1.
Save the result in the variable secant_root.
secant_root = None
Part (c) [4 pt]
Show that the iterative method
This is the formula given in lecture:
xk+1 = xk−1f(xk) − xkf(xk−1) f(xk) − f(xk−1)
is mathematically equivalent to the secant method for solving a scalar nonlinear equation f(x) = 0. Include your solution in your PDF writeup.
Part (d) [2 pt]
When implemented in finite-precision floating-point arithmetic, what advantages or disadvantages does the formula given in part (c) have compared with the formula for the secant method given in lecture?
xk+1 =xk −f(xk) xk −xk−1 f(xk) − f(xk−1)
Include your solution in your PDF writeup.
3