Homework 2

COSC 3320

Algorithms and Data Structures

Academic Honesty Policy

All submitted work should be your own. Copying or using other people’s work (including from the

Web) will result in −MAX points, where MAX is the maximum possible number of points for that

assignment. Repeat offenses will result in a failing grade for the course and will be reported to the

Chair. If you have any questions, please reach out to the professor and the TAs. The best way to

ask is on Piazza.

By submitting this assignment, you affirm that you have followed the Academic Honesty Policy.

Your submission must be typed. We prefer you use LATEX to type your solutions — LATEX is the

standard way to type works in mathematical sciences, like computer science, and is highly recommended;

for more information on using LATEX, please see this post on Piazza — but any method of typing your

solutions (e.g., MS Word, Google Docs, Markdown) is acceptable. Your submission must be in pdf

format. The assignment can be submitted up to two days late for a penalty of 10% per day. A

submission more than two days late will receive a zero.

Reading

Chapters 5 and 6. In particular, several worked exercises with solutions are provided at the end of

each chapter. Attempting to solve the worked exercises before seeing their solutions is a good

learning technique.

The exercises below are from the book. The book is updated periodically, so be sure to use the latest

version.

Exercises

5.18, 5.23, 6.11, 6.20

Justify your answers. Show appropriate work.

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1 Class Questions

1.1 February 09

Question 1

Show that the depth of the tree presented in class is O

log3/2 n

.

Solution. TYPE SOLUTION HERE.

Question 2

Prove the correctness of the Select algorithm by mathematical induction.

Solution. TYPE SOLUTION HERE.

1.2 February 23

Question 1

Show that the algorithm mult given in class takes O

n

2

operations. You can assume that we are

multiplying two n-bit numbers (i.e., binary numbers each of length n bits). Adding or multiplying

two bits is counted as one operation.

Solution. TYPE SOLUTION HERE.

2 Textbook Exercises

Exercise 5.18

Consider the max-sum problem: Given an array A of size n containing positive and negative

integers, determine indices i and j, 1 ≤ i ≤ j ≤ n, such that A[i] + A[i + 1] + … + A[j] is a

maximum.

(a) Consider the array A = [5, 10, −15, 20, −4, 6, 4, 8, −10, 20]. Find the indices i and j that give

the maximum sum and state the maximum sum.

(b) Give a divide and conquer algorithm that runs in O(n log n) time. Assume, that adding or

comparing two numbers takes constant time.

(Hint: Split the array into two (almost) equal parts and recursively solve the problem on the

two parts. The non-trivial part is combining the solutions — note that it is possible that

the indices i and j that give the optimal sum might be on opposite parts.)

Solution. TYPE SOLUTION HERE.

Exercise 5.23

You are given an unsorted array of n ≥ 1 integers. Give an efficient divide and conquer algorithm

to output an element in the array that occurs more than 3 times, if such an element exists. Show

the correctness of your algorithm and analyze its run time.

Solution. TYPE SOLUTION HERE.

Exercise 6.11

Consider the following variant of the max-sum problem.

Given an array A of size n containing positive and negative integers, the goal is to determine

indices i and j, 1 ≤ i ≤ j ≤ n, such that A[i] + A[i + 1] + … + A[j] − A[k] is a maximum, where

k can be any index between i and j, i.e., i ≤ k ≤ j. In other words, we are allowed to exclude up

to one element (any one) in the contiguous subarray A[i], . . . , A[j], including A[i] or A[j]. Note

that a solution subarray can be contiguous as well — this is captured b allowing k to be A[i] or

A[j], which means that the subarray is contiguous.

Give a dynamic programming (DP) algorithm that computes the optimum value of the modified

max-sum problem, i.e., we are interested in just computing the value of the optimum solution.

1. Specify the subproblems, clearly explaining your notation.

2. Give a recursive formulation that relates how you can solve larger-sized subproblems from

smaller-sized subproblems. Also mention the base cases.

3. Implement your formulation by a DP algorithm and a memoized recursive algorithm.

4. Analyze the runtime of your algorithms.

Solution. TYPE SOLUTION HERE.

Exercise 6.20

You are given a stick of length n units. Your goal is to cut the stick into different pieces (each

piece should be of integer length only) so that the total value of all the pieces is maximized. A

piece of length i units has value vi

, for i = 1, 2, . . . , n. You should design a dynamic programming

algorithm that determines the maximum total value that is possible.

(i) Consider the sequence of 7 numbers: 2, 3, 4, 5, 6, 7, 9. Let the i

th number in the sequence

represents the value of piece of length i, i.e., the value of piece of length 1 is 2, of length 2 is

3, length 3 is 4 and so on. What is the best way to cut the pieces for a stick of length 7?

What is the maximum value ?

(ii) Define the subproblems for your DP solution on a general instance of the problem where the

stick is of length n and a piece of length i has value vi

.

(iii) Give a recursive formulation to solve the subproblems. Don’t forget the base cases.

(iv) What is the running time of your solution?

(v) Write a DP algorithm (give pseudocode) that outputs the maximum value.

(vi) Describe an algorithm to output the sizes of the pieces of the cut that corresponds to the

maximum value.

Solution. TYPE SOLUTION HERE.

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