Description
CSS 340: Applied Algorithmics
Purpose
This lab will serve two purposes. First, it will provide hands-on experience for utilizing many of
the sorting algorithms we will introduce in the class. Second, it will viscerally demonstrate the
cost of O(n2
) v. O(n logn) algorithms. It will also clearly show that algorithms with the same
complexity may have different running times.
Problem:
You will write a program which implements the following sorts and compares the performance
for operations on lists of integers of growing sizes 10, 100, 1000, 5000, 10000, 25000, etc….
You will graph the performance of the different sorts as a function of the size of the list.
1) BubbleSort
2) InsertionSort
3) MergeSort
4) Non-Recursive, one extra list MergeSort (We’ll call this improved version,
IterativeMergeSort from here on out in this homework)
5) QuickSort
6) ShellSort
Details:
IterativeMergeSort
In-place sorting refers to sorting that does not require extra memory. For example,
QuickSort performs partitioning operations by repeating a swapping operation on two data
items in a given list. This does not require an extra list.
MergeSort as shown in class and the book allocates a temporary list at each recursive
call. Due to this MergeSort is slower than QuickSort even though their running time is upperbounded to O(n log n).
We can improve the performance of MergeSort by utilizing a non-recursive method and
using only one additional list (instead of one list on each recursive call). In this improved
version of MergeSort, IterativeMergeSort, one would merge data from the original list into the
additional list and alternatively copy back and forth between the original and the additional
temporary list. Please re-read the last sentence as it is critical to the grading of the lab.
For the IterativeMergeSort we still need to allow data items to be copied between the
original and this additional list as many times as O(log n). However, given the iterative nature
we are not building up state on the stack.
Other Sorts
BubbleSort, InsertionSort, MergeSort, ShellSort and QuickSort are well documented and
you should implement them with the aid of examples in the Miller book and the slide decks.
Runtime Details
Your program, called Sorter, will take in up to three arguments:
1) sort type as a string of characters
2) the number of integers in the list
3) a string (PRINT) which will print the list pre-sort and post-sort. This argument
is optional.
Your program will create and sort an integer list of the size with the specified sort:
MergeSort, BubbleSort, InsertionSort, QuickSort, ShellSort or IterativeMergeSort.
Examples:
python Sorter MergeSort 100 (creates and sorts a of 100 using MergeSort)
python Sorter QuickSort 1000 (creates and sorts a list of 1000 using QuickSort)
python Sorter IterativeMergeSort 10000 (creates and sorts a list of 10000 using
the newly implemented non-recursive semi-in-place MergeSort)
python Sorter BubbleSort 25 PRINT (creates a list of 25 random integers and
sorts using the BubbleSort. The list is printed before and after the call)
What to turn in:
Turn in, in a .zip (not gz, etc..):
(1) sort.py which is a python module with sorts implemented
(2) Sorter.py which the driver function
(3) A separate report in word or pdf which includes: Graphs that compares the
performance among the different sorting algorithms with increasing data size. You
should increase the data size to clearly show the difference in performance of the
different sorts.