Restructure Newton's method (Case Study: Approximating Square Roots) by decomposing it into three cooperating functions: newton, limitReached, and improveEstimate.

Database System Concepts
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ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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The instructions are on the left, the code is on the right

 

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Instructions
newton.py
1 # Modify the code below
2 II I| ||
Restructure Newton's method (Case Study: Approximating Square Roots)
3 Program: newton.py
by decomposing it into three cooperating functions: newton ,
4 Author: Ken
limitReached , and improveEstimate.
5 Compute the square root of a number.
6 1. The input is a number.
7 2. The outputs are the program's estimate of the square root
using Newton's method of successive approximations, and
Python's own estimate using math.sqrt.
The newton function can use either the recursive strategy of Project 2 or
8.
the iterative strategy of the Approximating Square Roots Case Study. The
task of testing for the limit is assigned to a function named
limitReached , whereas the task of computing a new approximation is
11
assigned to a function named improveEstimate . Each function expects
12 import math
the relevant arguments and returns an appropriate value.
13
14 # Receive the input number from the user
15 x = float(input("Enter a positive number: "))
An example of the program input and output is shown below:
16
17 # Initialize the tolerance and estimate
18 tolerance = 0.000001
Enter a positive number or enter/return to quit: 2
19 estimate = 1.0
20
The program's estimate is 1.4142135623746899
21 # Perform the successive approximations
Python's estimate is
1.4142135623730951
22 while True:
Enter a positive number or enter/return to quit
23
estimate = (estimate + x / estimate) / 2
24
abs(x
estimate ** 2)
difference
if difference <= tolerance:
%3D
25
26
break
Grading
27
28 # Output the result
29 print ("The program's estimate is", estimate)
30 print("Python's estimate is
', math.sqrt(x))
Transcribed Image Text:Instructions newton.py 1 # Modify the code below 2 II I| || Restructure Newton's method (Case Study: Approximating Square Roots) 3 Program: newton.py by decomposing it into three cooperating functions: newton , 4 Author: Ken limitReached , and improveEstimate. 5 Compute the square root of a number. 6 1. The input is a number. 7 2. The outputs are the program's estimate of the square root using Newton's method of successive approximations, and Python's own estimate using math.sqrt. The newton function can use either the recursive strategy of Project 2 or 8. the iterative strategy of the Approximating Square Roots Case Study. The task of testing for the limit is assigned to a function named limitReached , whereas the task of computing a new approximation is 11 assigned to a function named improveEstimate . Each function expects 12 import math the relevant arguments and returns an appropriate value. 13 14 # Receive the input number from the user 15 x = float(input("Enter a positive number: ")) An example of the program input and output is shown below: 16 17 # Initialize the tolerance and estimate 18 tolerance = 0.000001 Enter a positive number or enter/return to quit: 2 19 estimate = 1.0 20 The program's estimate is 1.4142135623746899 21 # Perform the successive approximations Python's estimate is 1.4142135623730951 22 while True: Enter a positive number or enter/return to quit 23 estimate = (estimate + x / estimate) / 2 24 abs(x estimate ** 2) difference if difference <= tolerance: %3D 25 26 break Grading 27 28 # Output the result 29 print ("The program's estimate is", estimate) 30 print("Python's estimate is ', math.sqrt(x))
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