Data structures and algorithms made easy java

DATA STRUCTURES
AND
ALGORITHMS
MADE EASY IN JAVA

-To All My Readers

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Copyright ©2012 CareerMonk Publications. All rights reserved.

All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical
means, including information storage and retrieval systems, without written permission from the publisher or
author.

Acknowledgements

I would like to express my gratitude to many people who saw me through this book, to all those who provided
support, talked things over, read, wrote, offered comments, allowed me to quote their remarks and assisted in
the editing, proofreading and design. In particular, I would like to thank the following individuals.

, for encouraging me when I was at . He is the first person who taught me
the importance of and its by giving good books on them.

[ ], for his encouragement during my stay at
.

[ , ], [ , ], . . .
[ , , ], [ , ] and
[ ] for spending time in reviewing, testing code and for providing me the
valuable suggestions.

[ ], [ , ] for
spending their valuable time in reviewing the book, suggestions and encouragement.

& [Founders of ], for approaching me for teaching Data Structures and
Algorithms at their training centers. They are the primary reason for initiation of this book.

, and have contributed greatly to the quality of this book. I thank all of you
for your help and suggestions.

Last but not least, I would like to thank of ,
& [ ], [ of ],
[ , ],
[ ], [ , ]& for helping me and
my family during our studies.

-
M-Tech,
Founder of

A stone is broken by the last stroke. This does not mean that first stroke was useless.
Success is a result of continuous daily effort. --

Stand up, be bold, be strong. Take the whole responsibility on your own shoulders,
and know that you are the creator of your own destiny. --

Preface
Dear Reader,

Please Hold on! I know many people do not read preface. But I would like to strongly recommend reading
preface of this book at least. This preface has from regular prefaces.

Main objective of the book is not to give you the theorems and proofs about and .
I have followed a pattern of improving the problem solutions with different complexities (for each problem,
you observe multiple solutions with different improved complexities). Basically, it‟s an enumeration of possible
solutions. With this approach, even if we get a new question it gives us a way to think about all possible
solutions. This book is very much useful for interview preparation, competitive exams preparation, campus
preparations.

As a if you read complete book with good understanding, I am sure you will challenge the
interviewers and that is the objective of this book.

If you read as an , you will give better lectures with easy go approach and as a result your students
will feel proud for selecting Computer Science / Information Technology as their degree.

This book is very much useful for the of and during their academic
preparations. All the chapters of this book contain theory and their related problems as many as possible. There
a total of approximately algorithmic puzzles and all of them are with solutions.

If you read as a preparing for competition exams for Computer Science/Information Technology], the
content of this book covers the topics in full details. While writing the book, an intense care has
been taken to help students who are preparing for these kinds of exams.

In all the chapters you will see more importance given to problems and analyzing them instead of concentrating
more on theory. For each chapter, first you will see the basic required theory and then followed by problems.

For many of the problems, solutions are provided with different complexities. We start with
solution and slowly move towards the possible for that problem. For each problem
we will try to understand how much time the algorithm is taking and how much memory the algorithm is
taking.

It is that, at least one complete reading of this book is required to get full understanding of all
the topics. In the subsequent readings, you can directly go to any chapter and refer. Even though, enough
readings were given for correcting the errors, due to human tendency there could be some minor typos in the
book. If any such typos found, they will be updated at . I request you to constantly
monitor this site for any corrections, new problems and solutions. Also, please provide your valuable
suggestions at: .

Wish you all the best. Have a nice reading.

-
M-Tech,
Founder of

Other Titles by Narasimha Karumanchi
Success keys for Big Job Hunters

Data Structures and Algorithms for GATE

Data Structures and Algorithms Made Easy (C/C++)
Coding Interview Questions
Peeling Design Patterns

Table of Contents
1. Introduction ----------------------------------------------------------------------- 9

2. Recursion and Backtracking ------------------------------------------------------32

3. Linked Lists -----------------------------------------------------------------------37

4. Stacks ------------------------------------------------------------------------------72

5. Queues ----------------------------------------------------------------------------97

6. Trees ----------------------------------------------------------------------------- 107

7. Priority Queue and Heaps ------------------------------------------------------ 175

8. Disjoint Sets ADT --------------------------------------------------------------- 193

9. Graph Algorithms -------------------------------------------------------------- 202

10. Sorting--------------------------------------------------------------------------- 247

11. Searching ------------------------------------------------------------------------ 270

12. Selection Algorithms [Medians] ------------------------------------------------ 295

13. Symbol Tables------------------------------------------------------------------- 304

14. Hashing ------------------------------------------------------------------------- 306

15. String Algorithms --------------------------------------------------------------- 322

16. Algorithms Design Techniques ------------------------------------------------ 346

17. Greedy Algorithms ------------------------------------------------------------- 349

18. Divide and Conquer Algorithms ----------------------------------------------- 359

19. Dynamic Programming --------------------------------------------------------- 373

20. Complexity Classes ------------------------------------------------------------- 407

21. Miscellaneous Concepts -------------------------------------------------------- 414

Data structures and algorithms made easy java

Data Structures and Algorithms Made Easy in Introduction

Chapter-1
INTRODUCTION

The objective of this chapter is to explain the importance of analysis of algorithms, their notations, relationships and
solving as many problems as possible. We first concentrate on understanding the basic elements of algorithms,
importance of analysis and then slowly move towards analyzing the algorithms with different notations and finally the
problems. After completion of this chapter you should be able to find the complexity of any given algorithm
(especially recursive functions).

1.1 Variables
Before going to the definition of variables, let us relate them to old mathematical equations. All of us have solved
many mathematical equations since childhood. As an example, consider the below equation:

We don‟t have to worry about the use of above equation. The important thing that we need to understand is, the
equation has some names ( and ) which hold values (data). That means, the ( and ) are the place holders
for representing data. Similarly, in computer science we need something for holding data and are the
facility for doing that.

1.2 Data types
In the above equation, the variables and can take any values like integral numbers ( , etc...), real numbers
( etc…) or just and . To solve the equation, we need to relate them to kind of values they can take and
is the name being used in computer science for this purpose.
A in a programming language is a set of data with values having predefined characteristics. Examples of
data types are: integer, floating point unit number, character, string etc...
Computer memory is all filled with zeros and ones. If we have a problem and wanted to code it, it‟s very difficult to
provide the solution in terms of zeros and ones. To help users, programming languages and compilers are providing the
facility of data types.
For example, takes bytes (actual value depends on compiler), takes bytes etc… This says that, in memory
we are combining bytes ( bits) and calling it as . Similarly, combining bytes ( bits) and calling it as
. A data type reduces the coding effort. Basically, at the top level, there are two types of data types:
 System defined data types (also called data types)
 User defined data types

System defined data types (Primitive data types)
Data types which are defined by system are called data types. The primitive data types which are provided
by many programming languages are: Boolean, byte, short, int, float, double, etc…The number of bits allocated for
each primitive data type depends on the programming languages, compiler and operating system. For the same
primitive data type, different languages may use different sizes. Depending on the size of the data types the total
available values (domain) will also changes.
For example, “ ” may take bytes or bytes. If it takes bytes ( bits) then the total possible values are - to
+ (- -1). If it takes, bytes ( bits), then the possible values are between to
(- -1). Same is the case with remaining data types too.

1.1 Variables 9


User defined data types
If the system defined data types are not enough then most programming languages allows the users to define their own
data types called as user defined data types. Good example of user defined data types are: structures in and
classes in . For example, in the below case, we are combining many system defined data types and called it as user
defined data type with name “ ”. This gives more flexibility and comfort in dealing with computer memory.
public class newType {
public int data1;
public int data 2;
private float data3;
…
private char data;
//Operations
}

1.3 Data Structure
Based on the above discussion, once we have data in variables, we need some mechanism for manipulating that data to
solve problems. is a particular way of storing and organizing data in a computer so that it can be used
efficiently. That means, a is a specialized format for organizing and storing data. General data
structure types include arrays, files, linked lists, stacks, queues, trees, graphs and so on. Depending on the organization
of the elements, data structures are classified into two types:
1) Elements are accessed in a sequential order but it is not compulsory to store all
elements sequentially (say, Linked Lists). : Linked Lists, Stacks and Queues.
2) : Elements of this data structure are stored/accessed in a non-linear order.
: Trees and graphs.

1.4 Abstract Data Types (ADTs)
Before defining abstract data types, let us consider the different view of system defined data types. We all know that,
by default, all primitive data types (int, float, et..) supports basic operations like addition, subtraction etc… The system
is providing the implementations for the primitive data types. For user defined data types also we need to define
operations. The implementation for these operations can be done when we want to actually use them. That means, in
general user defined data types are defined along with their operations.
To simplify the process of solving the problems, we generally combine the data structures along with their operations
and are called (ADTs). An ADT consists of parts:
1. Declaration of data
2. Declaration of operations
Commonly used ADTs : Linked Lists, Stacks, Queues, Priority Queues, Binary Trees, Dictionaries, Disjoint Sets
(Union and Find), Hash Tables, Graphs, and many other. For example, stack uses LIFO (Last-In-First-Out) mechanism
while storing the data in data structures. The last element inserted into the stack is the first element that gets deleted.
Common operations of it are: creating the stack, pushing an element onto the stack, popping an element from stack,
finding the current top of the stack, finding number of elements in the stack etc...
While defining the ADTs do not care about implementation details. They come in to picture only when we want to
use them. Different kinds of ADTs are suited to different kinds of applications, and some are highly specialized to
specific tasks. By the end of this book, we will go through many of them and you will be in a position to relate the data
structures to the kind of problems they solve.

1.5 What is an Algorithm?
Let us consider the problem of preparing an omelet. For preparing omelet, general steps we follow are:

1.3 Data Structure 10


1) Get the frying pan.
2) Get the oil.
a. Do we have oil?
i. If yes, put it in the pan.
ii. If no, do we want to buy oil?
1. If yes, then go out and buy.
2. If no, we can terminate.
3) Turn on the stove, etc...
What we are doing is, for a given problem (preparing an omelet), giving step by step procedure for solving it. Formal
definition of an algorithm can be given as:
An algorithm is the step-by-step instructions to solve a given problem.
Note: we do not have to prove each step of the algorithm.

1.6 Why Analysis of Algorithms?
To go from city to city there can be many ways of accomplishing this: by flight, by bus, by train and also by
cycle. Depending on the availability and convenience we choose the one which suits us. Similarly, in computer science
there can be multiple algorithms exist for solving the same problem (for example, sorting problem has many
algorithms like insertion sort, selection sort, quick sort and many more). Algorithm analysis helps us determining
which of them is efficient in terms of time and space consumed.

1.7 Goal of Analysis of Algorithms
The goal of is to compare algorithms (or solutions) mainly in terms of running time but also
in terms of other factors (e.g., memory, developers effort etc.)

1.8 What is Running Time Analysis?
It is the process of determining how processing time increases as the size of the problem (input size) increases. Input
size is number of elements in the input and depending on the problem type the input may be of different types. In
general, we encounter the following types of inputs.
 Size of an array
 Polynomial degree
 Number of elements in a matrix
 Number of bits in binary representation of the input
 Vertices and edges in a graph

1.9 How to Compare Algorithms?
To compare algorithms, let us define few .
Execution times? as execution times are specific to a particular computer.
Number of statements executed? , since the number of statements varies with the
programming language as well as the style of the individual programmer.
Ideal Solution? Let us assume that we expressed running time of given algorithm as a function of the input size
(i.e., ) and compare these different functions corresponding to running times. This kind of comparison is
independent of machine time, programming style, etc...

1.10 What is Rate of Growth?
The rate at which the running time increases as a function of input is called . Let us assume that you
went to a shop for buying a car and a cycle. If your friend sees you there and asks what you are buying then in general

1.6 Why Analysis of Algorithms? 11


we say This is because, cost of car is too big compared to cost of cycle (approximating the cost of cycle
to cost of car).

For the above example, we can represent the cost of car and cost of cycle in terms of function and for a given function
ignore the low order terms that are relatively insignificant (for large value of input size, ). As an example in the
below case, , , and are the individual costs of some function and approximate it to . Since, is the
highest rate of growth.

1.11 Commonly used Rate of Growths
Below diagram shows the relationship between different rates of growth.

D
e
c
r
e
a
s
i
n
g

R
a
t
e
s

O
f

G
r
o
w
t
h
√

1.11 Commonly used Rate of Growths 12


Below is the list of rate of growths which come across in remaining chapters.
Time complexity Name Example
Constant Adding an element to the front of a linked list
Logarithmic Finding an element in a sorted array
Linear Finding an element in an unsorted array
Linear Logarithmic Sorting n items by „divide-and-conquer‟-Mergesort
Quadratic Shortest path between two nodes in a graph
Cubic Matrix Multiplication
Exponential The Towers of Hanoi problem

1.12 Types of Analysis
To analyze the given algorithm we need to know on what inputs the algorithm is taking less time (performing well)
and on what inputs the algorithm is taking huge time.
We have already seen that an algorithm can be represented in the form of an expression. That means we represent the
algorithm with multiple expressions: one for case where it is taking the less time and other for case where it is taking
the more time. In general the first case is called the and second case is called the of the
algorithm. To analyze an algorithm we need some kind of syntax and that forms the base for asymptotic
analysis/notation. There are three types of analysis:
 Worst case
o Defines the input for which the algorithm takes huge time.
o Input is the one for which the algorithm runs the slower.
 Best case
o Defines the input for which the algorithm takes lowest time.
o Input is the one for which the algorithm runs the fastest.
 Average case
o Provides a prediction about the running time of the algorithm
o Assumes that the input is random

For a given algorithm, we can represent best, worst and average cases in the form of expressions. As an example, let
be the function which represents the given algorithm.
, for worst case
for best case
Similarly, for average case too. The expression defines the inputs with which the algorithm takes the average running
time (or memory).

1.13 Asymptotic Notation
Having the expressions for best, average case and worst cases, for all the three cases we need to identify the upper and
lower bounds. In order to represent these upper and lower bounds we need some kind syntax and that is the subject of
following discussion. Let us assume that the given algorithm is represented in the form of function .

1.14 Big-O Notation
This notation gives the upper bound of the given function. Generally, it is represented as O . That
means, at larger values of , the upper bound of is .
For example, if is the given algorithm, then is . That means, gives the
maximum rate of growth for at larger values of .

1.12 Types of Analysis 13


Let us see the O notation with little more detail. O notation defined as O there exist positive
constants and such that for all . is an asymptotic tight upper bound for .
Our objective is to give smallest rate of growth which is greater than or equal to given algorithms rate of
growth .
In general, we discard lower values of . That means the rate of growth at lower values of is not important. In the
below figure, is the point from which we need to consider the rate of growths for a given algorithm. Below the
rate of growths could be different.
Rate of Growth

Input Size,

Big-O Visualization
O is the set of functions with smaller or same order of growth as For example, O includes
O O O etc..
Note: Analyze the algorithms at larger values of only. What this means is, below we do not care for rate of
growth.
O O

O O

, . , .

Big-O Examples
Example-1 Find upper bound for
Solution: for all
∴ =O with c = 4 and
Solution: for all
∴ =O with and
Solution: for all
∴ =O with and
1.14 Big-O Notation 14


Solution: for all
∴ =O with and
Solution: for all
∴ =O with and
Solution: for all
∴ O with and

No Uniqueness?
There are no unique set of values for and in proving the asymptotic bounds. Let us consider, O
For this function there are multiple and values possible.
Solution1: for all and is a solution.
Solution2: for all and is also a solution.

1.15 Omega-Ω Notation
Similar to O discussion, this notation gives the tighter lower bound of the given algorithm and we represent it
as . That means, at larger values of , the tighter lower bound of is For example, if
, is

Rate of Growth

Input Size,

The notation can be defined as there exist positive constants and such that
for all is an asymptotic tight lower bound for Our objective is to give largest rate of growth
which is less than or equal to given algorithms rate of growth .

Ω Examples
Example-1 Find lower bound for
Solution: Such that: and =1
∴ with and =1
Example-2 Prove
Solution: Such that:

–
Since is positive –

1.15 Omega-Ω Notation 15

Data structures and algorithms made easy java

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