Algorithms for Data Science Homework 1

Engineering and Applied Science Programs for Professionals

685.621 Algorithms for Data Science
Homework 1
Assigned at the start of Module 1
Due at the end of Module 2
Total Points 100/100
Collaboration groups will be set up in Blackboard by the end of the week. Make sure your group
starts an individual thread for each collaborative problem and subproblem. You are required to participate in each of the collaborative problem and subproblem.
Self-Study Problems
All of the following problems come from the textbook and have solutions posted on the web at
http://mitpress.mit.edu/algorithms.
You are permitted to use this site to examine solutions for these problems as a means of selfchecking your solutions. These problems will not be graded.
Problems: 2.2-2, 2.3-5, 3.1-2, 12.1-2, 12.3-3, 21.2-6.
Problems for Grading
1. Problem 1 Chapter 2 Note this is a Collaborative Problem
20 Points Total
Use induction to prove Pn
i=1 i
3 =

n(n+1)
2
2
.
2. Problem 2 Parts a, b, c, d, e and f
30 Points Total 5 Points Each
Although merge sort runs in Θ(nlgn) worst-case time and insertion sort runs in Θ(n
2
) worst-case
time, the constant factors in insertion sort can make it faster in practice for small problem sizes
on many machines. Thus, it makes sense to coarsen the leaves of the recursion by using insertion
sort within merge sort when subproblems become sufficiently small. Consider a modification to
merge sort in which n/k sublists of length k are sorted using insertion sort and then merged
using the standard merging mechanism, where k is a value to be determined.
(a) Use insertion sort to sort the unsorted array < 40, 17, 45, 82, 62, 32, 30, 44, 93, 10 >. Make
sure to show the array after every pass.
(b) Use merge sort to sort the unsorted array < 75, 56, 85, 90, 49, 26, 12, 48, 40, 47 >. Make sure
to show the steps of splitting the array then merging the array.
(c) Show that insertion sort can sort the n/k sublists, each of length k, in Θ(nk) worst-case time.
(d) Show how to merge the sublists in Θ(nlg(n/k)) worst-case time.
(e) Given that the modified algorithm runs in Θ(nk + nlg(n/k)) worst-case time, what is the
largest value of k as a function of n for which the modified algorithm has the same running time
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as standard merge sort, in terms of Θ-notation?
(f) How should we choose k in practice?
3. Problem 3
15 Points Total
Write a Θ(m + n) algorithm that prints the in-degree and the out-degree of every vertex in an
m-edge, n-vertex directed graph where the directed graph is represented using adjacency lists.
4. Problem 4 Chapter 12 Binary Search Trees
25 Points Total 5 Points Each
Exercise 12.2-1. Suppose that we have numbers between 1 and 1000 in a binary search tree
and we want to search for the number 363. Which of the following sequences could not be the
sequence of nodes examined?
i. 2, 252, 401, 398, 330, 397, 363.
ii. 924, 220, 911, 244, 898, 258, 362, 363.
iii. 925, 202, 911, 240, 912, 245, 363.
iv. 2, 399, 387, 219, 266, 382, 381, 278, 363.
v. 935, 278, 347, 621, 299, 392, 358, 363.
5. Problem 5 Sorting Iris Plants Note this is a Collaborative Problem
10 Points
The Iris Plants Database contains 3 classes of 50 instances each, where each class refers to a type
of Iris plant. Five attributes/features were collected for each plant instance. The dataset can be
downloaded from iris.arff on the Sample Weka Data Sets webpage (https://storm.cis.fordham.edu/ gweiss/datamining/datasets.html). Develop an algorithm to sort the five features in the dataset to determine
if any of the five sorted features can separate the three plant types. Show the efficiency of your
sorting algorithm based on its ability to sort the five sets of features.
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