# Day 19

## Today

• Analysis of algorithms

## For Next time

• Reading for next time: Chapter 19 (no reading journal, but be sure to read this chapter. It has some great stuff in it that you’ll want to know about)
• Reflection on the AR is due

## Appendix B Debrief

With two folks sitting around you, discuss the following questions:

1. What are some of the challenges in comparing the efficiency of two algorithms?
2. How does order of growth analysis address these challenges?
3. In what situations might order of growth analysis be misleading (or at least tell an incomplete story)?
4. Review your answers to Appendix B Problem 1 (from the reading journal). If there is confusion about one of the answers, take some time to discuss it at your table in more detail (or use the whiteboard). If any questions come up that you’d like to raise with the whole class, there will be some time to do so following your small group discussions.

## Practice with Order of Growth

Suppose we are given two python functions do_procedure_f1 and do_procedure_f2. Each function processes a list L in some fashion (what these programs do is unimportant for this exercise). We are told that the order of growth of these procedures is:

• do_procedure_f1 is O(n) (where n is the length of the input list L)
• do_procedure_f2 is O(1) (where n is the length of the input list L)

What are the order of growths of the following computations?

def run_computation_1(L):
do_procedure_f1(L)
do_procedure_f2(L)

def run_computation_2(L):
do_procedure_f1(L[0:5])
do_procedure_f2(L)

def run_computation_3(L):
for i in range(len(L)):
do_procedure_f1(L)

def run_computation_4(L):
for i in range(len(L)):
do_procedure_f2(L)

def run_computation_5(L):
if len(L) % 2 == 0:
do_procedure_f1(L)
else:
do_procedure_f2(L)

def run_computation_6(L):
if len(L) == 1:
return 1
else:
do_procedure_f2(L)
run_computation_6(L[0:len(L)/2])


## Order of Growth for Basic Python Operations

You have read Think Python Appendix B.1 and B.2. One of the most important takeaways is the listing of the order of growth for various operations on Python data structures. Here are some key points:

• Removing an element from the end of a list is constant time
• Adding an element to the end of the list is constant time (on average)
• Testing if an element is in a list is linear time, O(n)
• Looking up the value stored with a given key in a dictionary is constant time
• Looking up an element stored in a list at a particular location is constant time

## Empirical Analysis of Order of Growth

You should have received an invitation to a CoCalc project that Allen created. Next, you’ll be doing some exercises from this notebook.

This is an experiment, so as a backup in case you have issues with CoCalc, the notebook is available as part of the SoftDes website.

A couple of things to keep in mind when interpreting the results graphically:

• A straight line on a log-log plot does not mean the relationship is linear
• The slope of a straight line on a log-log is the exponent of the power relationship
• A slope of 1 on these plots in this notebook implies a constant-time algorithm (doing a constant-time operation to n things takes n time units). A slope of 2 implies a linear-time algorithm.