Introduction to Data
Structures
Data
Structure is a way of collecting and organising data in such a way that we can
perform operations on these data in an effective way. Data Structures is about
rendering data elements in terms of some relationship, for better organization
and storage. For example, we have data player's name "Virat" and age
26. Here "Virat" is of String data type and 26 is of integer data type.
We
can organize this data as a record like Player record. Now we can collect
and store player's records in a file or database as a data structure. For
example: "Dhoni" 30, "Gambhir" 31, "Sehwag" 33
In
simple language, Data Structures are structures programmed to store ordered
data, so that various operations can be performed on it easily.
Basic types of Data
Structures
As
we discussed above, anything that can store data can be called as a data
strucure, hence Integer, Float, Boolean, Char etc, all are data structures.
They are known as Primitive Data Structures.
Then
we also have some complex Data Structures, which are used to store large and
connected data. Some example of Abstract Data Structure are :
·
Linked
List
·
Tree
·
Graph
·
Stack,
Queue etc.
All
these data structures allow us to perform different operations on data. We
select these data structures based on which type of operation is required. We
will look into these data structures in more details in our later lessons.
What is Algorithm ?
An
algorithm is a finite set of instructions or logic, written in order, to
accomplish a certain predefined task. Algorithm is not the complete code or
program, it is just the core logic(solution) of a problem, which can be
expressed either as an informal high level description as pseudocode or using a flowchart.
An
algorithm is said to be efficient and fast, if it takes less time to execute
and consumes less memory space. The performance of an algorithm is measured on
the basis of following properties :
1. Time Complexity
2. Space Complexity
Space Complexity
Its
the amount of memory space required by the algorithm, during the course of its
execution. Space complexity must be taken seriously for multi-user systems and
in situations where limited memory is available.
An
algorithm generally requires space for following components :
·
Instruction Space : Its the space required to
store the executable version of the program. This space is fixed, but varies
depending upon the number of lines of code in the program.
·
Data Space : Its the space required to
store all the constants and variables value.
·
Environment Space : Its the space required to
store the environment information needed to resume the suspended function.
Time Complexity
Time
Complexity is a way to represent the amount of time needed by the program to
run to completion. We will study this in details in our section.
NOTE: Before going deep into data structure, you
should have a good knowledge of programming either in C or in C++ or Java.
Time Complexity of
Algorithms
Time
complexity of an algorithm signifies the total time required by the program to
run to completion. The time complexity of algorithms is most commonly expressed
using the big O notation.
Time
Complexity is most commonly estimated by counting the number of elementary
functions performed by the algorithm. And since the algorithm's performance may
vary with different types of input data, hence for an algorithm we usually use
the worst-case Time complexity of an algorithm because that
is the maximum time taken for any input size.
Calculating Time Complexity
Now
lets tap onto the next big topic related to Time complexity, which is How to
Calculate Time Complexity. It becomes very confusing some times, but we will
try to explain it in the simplest way.
Now
the most common metric for calculating time complexity is Big O notation. This
removes all constant factors so that the running time can be estimated in
relation to N, as N approaches infinity. In general you
can think of it like this :
statement;
Above
we have a single statement. Its Time Complexity will be Constant. The running time of the
statement will not change in relation to N.
for(i=0; i < N; i++)
{
statement;
}
The
time complexity for the above algorithm will be Linear. The running time of the
loop is directly proportional to N. When N doubles, so does the running time.
for(i=0; i < N; i++)
{
for(j=0; j <
N;j++)
{
statement;
}
}
This
time, the time complexity for the above code will be Quadratic. The running time of the
two loops is proportional to the square of N. When N doubles, the running time
increases by N * N.
while(low <= high)
{
mid = (low +
high) / 2;
if (target <
list[mid])
high = mid - 1;
else if (target
> list[mid])
low = mid + 1;
else break;
}
This
is an algorithm to break a set of numbers into halves, to search a particular
field(we will study this in detail later). Now, this algorithm will have a Logarithmic Time Complexity. The
running time of the algorithm is proportional to the number of times N can be
divided by 2(N is high-low here). This is because the algorithm divides the
working area in half with each iteration.
void quicksort(int list[], int left, int right)
{
int pivot =
partition(list, left, right);
quicksort(list,
left, pivot - 1);
quicksort(list,
pivot + 1, right);
}
Taking
the previous algorithm forward, above we have a small logic of Quick Sort(we
will study this in detail later). Now in Quick Sort, we divide the list into
halves every time, but we repeat the iteration N times(where N is the size of
list). Hence time complexity will be N*log( N ). The running time consists
of N loops (iterative or recursive) that are logarithmic, thus the algorithm is
a combination of linear and logarithmic.
NOTE : In general, doing something with every item
in one dimension is linear, doing something with every item in two dimensions
is quadratic, and dividing the working area in half is logarithmic.
Types of Notations for Time
Complexity
Now we will discuss and understand the various notations
used for Time Complexity.
1. Big Oh denotes "fewer than
or the same as" <expression> iterations.
2. Big Omega denotes "more than
or the same as" <expression> iterations.
3. Big Theta denotes "the same
as" <expression> iterations.
4. Little Oh denotes "fewer than"
<expression> iterations.
5. Little Omega denotes "more than"
<expression> iterations.
Understanding Notations of
Time Complexity with Example
O(expression) is the set of functions
that grow slower than or at the same rate as expression.
Omega(expression) is the set of functions
that grow faster than or at the same rate as expression.
Theta(expression) consist of all the
functions that lie in both O(expression) and Omega(expression).
Suppose
you've calculated that an algorithm takes f(n) operations, where,
f(n) = 3*n^2 + 2*n + 4.
// n^2 means square of n
Since
this polynomial grows at the same rate as n^2, then you could say that
the function f lies in the setTheta(n^2).
(It also lies in the sets O(n^2) and Omega(n^2) for the same reason.)
The
simplest explanation is, because Theta denotes the same as the expression. Hence, as f(n) grows by a factor of n^2, the time complexity can
be best represented as Theta(n^2).
Introduction to Sorting
Sorting
is nothing but storage of data in sorted order, it can be in ascending or
descending order. The term Sorting comes into picture with the term Searching.
There are so many things in our real life that we need to search, like a
particular record in database, roll numbers in merit list, a particular
telephone number, any particular page in a book etc.
Sorting arranges data in a sequence
which makes searching easier. Every record which is going to be sorted will
contain one key. Based on the key the record will be sorted. For example,
suppose we have a record of students, every such record will have the following
data:
·
Roll
No.
·
Name
·
Age
·
Class
Here
Student roll no. can be taken as key for sorting the records in ascending or
descending order. Now suppose we have to search a Student with roll no. 15, we
don't need to search the complete record we will simply search between the
Students with roll no. 10 to 20.
Sorting Efficiency
There
are many techniques for sorting. Implementation of particular sorting technique
depends upon situation. Sorting techniques mainly depends on two parameters.
First parameter is the execution time of program, which means time taken for
execution of program. Second is the space, which means space taken by the
program.
Types of Sorting Techniques
There
are many types of Sorting techniques, differentiated by their efficiency and
space requirements. Following are some sorting techniques which we will be
covering in next sections.
1. Bubble Sort
2. Insertion Sort
3. Selection Sort
4. Quick Sort
5. Merge Sort
6. Heap Sort
7.
Bubble Sorting
8.
Bubble Sort is an algorithm which is
used to sort N elements that are given in
a memory for eg: an Array withN number of elements. Bubble
Sort compares all the element one by one and sort them based on their values.
9.
It
is called Bubble sort, because with each iteration the smaller element in the
list bubbles up towards the first place, just like a water bubble rises up to
the water surface.
10.
Sorting
takes place by stepping through all the data items one-by-one in pairs and
comparing adjacent data items and swapping each pair that is out of order.
11. 
12.
13.
Sorting using Bubble Sort Algorithm
14.
Let's
consider an array with values {5, 1, 6, 2, 4, 3}
15.int a[6] = {5, 1, 6, 2, 4, 3};
16.int i, j, temp;
17.for(i=0; i<6, i++)
18.{
19.
for(j=0; j<6-i-1; j++)
20. {
21.
if( a[j] > a[j+1])
22. {
23.
temp = a[j];
24.
a[j] = a[j+1];
25.
a[j+1] = temp;
26. }
27. }
28.}
29.//now you can print the sorted array after
this
30.
Above
is the algorithm, to sort an array using Bubble Sort. Although the above logic
will sort and unsorted array, still the above algorithm isn't efficient and can
be enhanced further. Because as per the above logic, the for loop will keep
going for six iterations even if the array gets sorted after the second
iteration.
31.
Hence
we can insert a flag and can keep checking whether swapping of elements is
taking place or not. If no swapping is taking place that means the array is
sorted and wew can jump out of the for loop.
32.int a[6] = {5, 1, 6, 2, 4, 3};
33.int i, j, temp;
34.for(i=0; i<6, i++)
35.{
36.
for(j=0; j<6-i-1; j++)
37. {
38.
int flag = 0; //taking a
flag variable
39.
if( a[j] > a[j+1])
40. {
41.
temp = a[j];
42.
a[j] = a[j+1];
43.
a[j+1] = temp;
44.
flag = 1; //setting flag
as 1, if swapping occurs
45. }
46. }
47.
if(!flag) //breaking
out of for loop if no swapping takes place
48. {
49.
break;
50. }
51.}
52.
In
the above code, if in a complete single cycle of j iteration(inner for loop),
no swapping takes place, and flag remains 0, then we will break out of the for
loops, because the array has already been sorted.
53.
54.
Complexity Analysis of Bubble Sorting
55.
In
Bubble Sort, n-1 comparisons will be done in 1st pass, n-2 in 2nd pass, n-3 in
3rd pass and so on. So the total number of comparisons will be
56.(n-1)+(n-2)+(n-3)+.....+3+2+1
57.Sum = n(n-1)/2
58.i.e O(n2)
59.
Hence
the complexity of Bubble Sort is O(n2).
60.
The
main advantage of Bubble Sort is the simplicity of the algorithm.Space
complexity for Bubble Sort is O(1), because only single
additional memory space is required for temp variable
61.
Best-case Time Complexity will be O(n), it is when the list is
already sorted.
Insertion Sorting
It
is a simple Sorting algorithm which sorts the array by shifting elements one by
one. Following are some of the important characteristics of Insertion Sort.
1. It has one of the simplest
implementation
2. It is efficient for smaller
data sets, but very inefficient for larger lists.
3. Insertion Sort is adaptive,
that means it reduces its total number of steps if given a partially sorted
list, hence it increases its efficiency.
4. It is better than Selection
Sort and Bubble Sort algorithms.
5. Its space complexity is
less, like Bubble Sorting, inerstion sort also requires a single additional
memory space.
6. It is Stable, as it does not change the
relative order of elements with equal keys
How Insertion Sorting Works
Sorting using Insertion
Sort Algorithm
int a[6] = {5, 1, 6, 2, 4, 3};
int i, j, key;
for(i=1; i<6; i++)
{
key = a[i];
j = i-1;
while(j>=0
&& key < a[j])
{
a[j+1] = a[j];
j--;
}
a[j+1] = key;
}
Now
lets, understand the above simple insertion sort algorithm. We took an array
with 6 integers. We took a variable key, in which we put each
element of the array, in each pass, starting from the second element, that is a[1].
Then
using the while loop, we iterate, until j becomes equal to zero or we
find an element which is greater than key, and then we insert the
key at that position.
In
the above array, first we pick 1 as key, we compare it with 5(element before
1), 1 is smaller than 5, we shift 1 before 5. Then we pick 6, and compare it
with 5 and 1, no shifting this time. Then 2 becomes the key and is compared
with, 6 and 5, and then 2 is placed after 1. And this goes on, until complete
array gets sorted.
Complexity Analysis of
Insertion Sorting
Worst
Case Time Complexity : O(n2)
Best
Case Time Complexity : O(n)
Average
Time Complexity : O(n2)
Space
Complexity : O(1)
Selection Sorting
Selection
sorting is conceptually the most simplest sorting algorithm. This algorithm
first finds the smallest element in the array and exchanges it with the element
in the first position, then find the second smallest element and exchange it
with the element in the second position, and continues in this way until the
entire array is sorted.
How Selection Sorting Works
In
the first pass, the smallest element found is 1, so it is placed at the first
position, then leaving first element, smallest element is searched from the
rest of the elements, 3 is the smallest, so it is then placed at the second
position. Then we leave 1 nad 3, from the rest of the elements, we search for
the smallest and put it at third position and keep doing this, until array is
sorted.
Sorting using Selection
Sort Algorithm
void selectionSort(int a[], int size)
{
int i, j, min,
temp;
for(i=0; i <
size-1; i++ )
{
min = i; //setting min as i
for(j=i+1; j
< size; j++)
{
if(a[j] <
a[min]) //if element at j is less than
element at min position
{
min =
j; //then set min as j
}
}
temp = a[i];
a[i] = a[min];
a[min] = temp;
}
}
Complexity Analysis of
Selection Sorting
Worst
Case Time Complexity : O(n2)
Best
Case Time Complexity : O(n2)
Average
Time Complexity : O(n2)
Space
Complexity : O(1)
Quick Sort Algorithm
Quick
Sort, as the name suggests, sorts any list very quickly. Quick sort is not stable
search, but it is very fast and requires very less aditional space. It is based
on the rule of Divide and Conquer(also called partition-exchange sort). This algorithm divides
the list into three main parts :
1. Elements less than the
Pivot element
2. Pivot element
3. Elements greater than the
pivot element
In
the list of elements, mentioned in below example, we have taken 25 as pivot. So after the
first pass, the list will be changed like this.
6 8 17 14 25 63 37 52
Hnece
after the first pass, pivot will be set at its position, with all the elements
smaller to it on its left and all the elements larger than it on the right. Now 6
8 17 14 and 63
37 52 are considered as two separate lists, and same logic is
applied on them, and we keep doing this until the complete list is sorted.
How Quick Sorting Works
Sorting using Quick Sort
Algorithm
/* a[] is the
array, p is starting index, that is 0,
and r is the last index of array. */
void quicksort(int
a[], int p, int r)
{
if(p < r)
{
int q;
q = partition(a,
p, r);
quicksort(a, p,
q);
quicksort(a,
q+1, r);
}
}
int partition(int
a[], int p, int r)
{
int i, j, pivot,
temp;
pivot = a[p];
i = p;
j = r;
while(1)
{
while(a[i] <
pivot && a[i] != pivot)
i++;
while(a[j] >
pivot && a[j] != pivot)
j--;
if(i < j)
{
temp = a[i];
a[i] = a[j];
a[j] = temp;
}
else
{
return j;
}
}
}
Complexity Analysis of
Quick Sort
Worst
Case Time Complexity : O(n2)
Best
Case Time Complexity : O(n log n)
Average
Time Complexity : O(n log n)
Space
Complexity : O(n log n)
·
Space
required by quick sort is very less, only O(n log n) additional space is
required.
·
Quick
sort is not a stable sorting technique, so it might change the occurence of two
similar elements in the list while sorting.
Merge Sort Algorithm
Merge
Sort follows the rule of Divide and Conquer. But it doesn't divides
the list into two halves. In merge sort the unsorted list is divided into N
sublists, each having one element, because a list of one element is considered
sorted. Then, it repeatedly merge these sublists, to produce new sorted
sublists, and at lasts one sorted list is produced.
Merge
Sort is quite fast, and has a time complexity of O(n log n). It is also a stable sort,
which means the "equal" elements are ordered in the same order in the
sorted list.
How Merge Sort Works
Like
we can see in the above example, merge sort first breaks the unsorted list into
sorted sublists, and then keep merging these sublists, to finlly get the
complete sorted list.
Sorting using Merge Sort
Algorithm
/* a[] is the
array, p is starting index, that is 0,
and r is the last index of array. */
Lets take a[5] = {32, 45, 67, 2, 7} as the array to be
sorted.
void mergesort(int
a[], int p, int r)
{
int q;
if(p < r)
{
q = floor(
(p+r) / 2);
mergesort(a, p,
q);
mergesort(a,
q+1, r);
merge(a, p, q,
r);
}
}
void merge(int
a[], int p, int q, int r)
{
int b[5]; //same size of a[]
int i, j, k;
k = 0;
i = p;
j = q+1;
while(i <= q
&& j <= r)
{
if(a[i] <
a[j])
{
b[k++] =
a[i++]; // same as b[k]=a[i]; k++;
i++;
}
else
{
b[k++] =
a[j++];
}
}
while(i <= q)
{
b[k++] =
a[i++];
}
while(j <= r)
{
b[k++] =
a[j++];
}
for(i=r; i >=
p; i--)
{
a[i] =
b[--k]; // copying back the sorted
list to a[]
}
}
Complexity Analysis of
Merge Sort
Worst
Case Time Complexity : O(n log n)
Best
Case Time Complexity : O(n log n)
Average
Time Complexity : O(n log n)
Space
Complexity : O(n)
·
Time
complexity of Merge Sort is O(n Log n) in all 3 cases (worst, average and best)
as merge sort always divides the array in two halves and take linear time to
merge two halves.
·
It
requires equal amount of additional space as the unsorted list. Hence its not
at all recommended for searching large unsorted lists.
·
It
is the best Sorting technique for sorting Linked Lists.
Heap Sort Algorithm
Heap
Sort is one of the best sorting methods being in-place and with no quadratic
worst-case scenarios. Heap sort algorithm is divided into two basic parts :
·
Creating
a Heap of the unsorted list.
·
Then
a sorted array is created by repeatedly removing the largest/smallest element
from the heap, and inserting it into the array. The heap is reconstructed after
each removal.
What is a Heap ?
Heap
is a special tree-based data structure, that satisfies the following special
heap properties :
1. Shape Property : Heap data structure is
always a Complete Binary Tree, which means all levels of the tree are fully
filled.
2. Heap Property : All nodes are either [greater than or equal to] or [less than or equal to] each of its children. If
the parent nodes are greater than their children, heap is called a Max-Heap, and if the parent nodes
are smalled than their child nodes, heap is called Min-Heap.
How Heap Sort Works
Initially
on receiving an unsorted list, the first step in heap sort is to create a Heap
data structure(Max-Heap or Min-Heap). Once heap is built, the first element of
the Heap is either largest or smallest(depending upon Max-Heap or Min-Heap), so
we put the first element of the heap in our array. Then we again make heap
using the remaining elements, to again pick the first element of the heap and
put it into the array. We keep on doing the same repeatedly untill we have the
complete sorted list in our array.
In
the below algorithm, initially heapsort() function is called, which
calls buildheap() to build heap, which inturn
uses satisfyheap() to build the heap.
Sorting using Heap Sort
Algorithm
/* Below program
is written in C++ language */
void heapsort(int[], int);
void buildheap(int [], int);
void satisfyheap(int [], int, int);
void main()
{
int a[10], i,
size;
cout <<
"Enter size of list"; //
less than 10, because max size of array is 10
cin >>
size;
cout <<
"Enter" << size << "elements";
for( i=0; i <
size; i++)
{
cin >>
a[i];
}
heapsort(a,
size);
getch();
}
void heapsort(int
a[], int length)
{
buildheap(a,
length);
int heapsize, i,
temp;
heapsize = length
- 1;
for( i=heapsize;
i >= 0; i--)
{
temp = a[0];
a[0] =
a[heapsize];
a[heapsize] =
temp;
heapsize--;
satisfyheap(a,
0, heapsize);
}
for( i=0; i <
length; i++)
{
cout <<
"\t" << a[i];
}
}
void buildheap(int
a[], int length)
{
int i, heapsize;
heapsize = length
- 1;
for(
i=(length/2); i >= 0; i--)
{
satisfyheap(a,
i, heapsize);
}
}
void satisfyheap(int
a[], int i, int heapsize)
{
int l, r,
largest, temp;
l = 2*i;
r = 2*i + 1;
if(l <=
heapsize && a[l] > a[i])
{
largest = l;
}
else
{
largest = i;
}
if( r <=
heapsize && a[r] > a[largest])
{
largest = r;
}
if(largest != i)
{
temp = a[i];
a[i] =
a[largest];
a[largest] =
temp;
satisfyheap(a,
largest, heapsize);
}
}
Complexity Analysis of Heap
Sort
Worst
Case Time Complexity : O(n log n)
Best
Case Time Complexity : O(n log n)
Average
Time Complexity : O(n log n)
Space
Complexity : O(n)
·
Heap
sort is not a Stable sort, and requires a constant space for sorting a list.
·
Heap
Sort is very fast and is widely used for sorting.
·
Searching Algorithms on Array
·
Before
studying searching algorithms on array we should know what is an algorithm?
·
An algorithm is a step-by-step procedure
or method for solving a problem by a computer in a given number of steps. The
steps of an algorithm may include repetition depending upon the problem for
which the algorithm is being developed. The algorithm is written in human
readable and understandable form. To search an element in a given array, it can
be done in two ways Linear search and Binary search.
·
·
Linear Search
·
A
linear search is the basic and simple search algorithm. A linear search
searches an element or value from an array till the desired element or value is
not found and it searches in a sequence order. It compares the element with all
the other elements given in the list and if the element is matched it returns
the value index else it return -1. Linear Search is applied on the unsorted or
unordered list when there are fewer elements in a list.
·
·
Example with Implementation
·
To
search the element 5 it will go step by step in a sequence order.
·
·
function
findIndex(values, target)
·
{
·
for(var i = 0; i < values.length; ++i)
·
{
·
if (values[i] == target)
·
{
·
return i;
·
}
·
}
·
return -1;
·
}
·
//call the function findIndex with array and
number to be searched
·
findIndex([
8 , 2 , 6 , 3 , 5 ] , 5) ;
·
·
Binary Search
·
Binary
Search is applied on the sorted array or list. In binary search, we first
compare the value with the elements in the middle position of the array. If the
value is matched, then we return the value. If the value is less than the
middle element, then it must lie in the lower half of the array and if it's
greater than the element then it must lie in the upper half of the array. We
repeat this procedure on the lower (or upper) half of the array. Binary Search
is useful when there are large numbers of elements in an array.
·
·
Example with Implementation
·
To
search an element 13 from the sorted array or list.
·
·
function
findIndex(values, target)
·
{
·
return binarySearch(values, target, 0, values.length - 1);
·
};
·
·
function
binarySearch(values, target, start, end) {
·
if (start > end) { return -1; } //does not exist
·
·
var middle = Math.floor((start + end) / 2);
·
var value = values[middle];
·
·
if (value > target) { return binarySearch(values, target, start, middle-1); }
·
if (value < target) { return binarySearch(values, target, middle+1, end); }
·
return middle; //found!
·
}
·
·
findIndex([2,
4, 7, 9, 13, 15], 13);
·
In
the above program logic, we are first comparing the middle number of the list,
with the target, if it matches we return. If it doesn't, we see whether the
middle number is greater than or smaller than the target.
·
If
the Middle number is greater than the Target, we start the binary search again,
but this time on the left half of the list, that is from the start of the list
to the middle, not beyond that.
·
If
the Middle number is smaller than the Target, we start the binary search again,
but on the right half of the list, that is from the middle of the list to the
end of the list.
Stacks
Stack
is an abstract data type with a bounded(predefined) capacity. It is a simple
data structure that allows adding and removing elements in a particular order.
Every time an element is added, it goes on the top of the stack, the only
element that can be removed is the element that was at the top of the stack,
just like a pile of objects.
Basic features of Stack
1. Stack is an ordered list of
similar data type.
2. Stack is a LIFO structure. (Last in First
out).
3. push() function is used to insert
new elements into the Stack and pop() is used to delete an
element from the stack. Both insertion and deletion are allowed at only one end
of Stack called Top.
4. Stack is said to be in Overflow state when it is completely
full and is said to be in Underflow state if it is completely
empty.
Applications of Stack
The
simplest application of a stack is to reverse a word. You push a given word to
stack - letter by letter - and then pop letters from the stack.
There
are other uses also like : Parsing, Expression Conversion(Infix to Postfix, Postfix
to Prefix etc) and many more.
Implementation of Stack
Stack
can be easily implemented using an Array or a Linked List. Arrays are quick,
but are limited in size and Linked List requires overhead to allocate, link,
unlink, and deallocate, but is not limited in size. Here we will implement
Stack using array.
/* Below program
is written in C++ language */
Class Stack
{
int top;
public:
int a[10]; //Maximum size of Stack
Stack()
{
top = -1;
}
};
void Stack::push(int x)
{
if( top >= 10)
{
cout <<
"Stack Overflow";
}
else
{
a[++top] = x;
cout <<
"Element Inserted";
}
}
int Stack::pop()
{
if(top < 0)
{
cout <<
"Stack Underflow";
return 0;
}
else
{
int d =
a[--top];
return d;
}
}
void Stack::isEmpty()
{
if(top < 0)
{
cout <<
"Stack is empty";
}
else
{
cout <<
"Stack is not empty";
}
}
|
Position of Top
|
Status of Stack
|
|
-1
|
Stack is Empty
|
|
0
|
Only one element in Stack
|
|
N-1
|
Stack is Full
|
|
N
|
Overflow state of Stack
|
Analysis of Stacks
Below
mentioned are the time complexities for various operations that can be
performed on the Stack data structure.
·
Push Operation : O(1)
·
Pop Operation : O(1)
·
Top Operation : O(1)
·
Search Operation : O(n)
Queue Data Structures
Queue
is also an abstract data type or a linear data structure, in which the first
element is inserted from one end called REAR(also called tail), and the
deletion of exisiting element takes place from the other end called as FRONT(also called head). This
makes queue as FIFO data structure, which means that element inserted first
will also be removed first.
The
process to add an element into queue is called Enqueue and the process of removal
of an element from queue is called Dequeue.
Basic features of Queue
1. Like Stack, Queue is also
an ordered list of elements of similar data types.
2. Queue is a FIFO( First in
First Out ) structure.
3. Once a new element is
inserted into the Queue, all the elements inserted before the new element in
the queue must be removed, to remove the new element.
4. peek( ) function is oftenly used to
return the value of first element without dequeuing it.
Applications of Queue
Queue,
as the name suggests is used whenever we need to have any group of objects in
an order in which the first one coming in, also gets out first while the others
wait for there turn, like in the following scenarios :
1. Serving requests on a single
shared resource, like a printer, CPU task scheduling etc.
2. In real life, Call Center
phone systems will use Queues, to hold people calling them in an order, until a
service representative is free.
3. Handling of interrupts in
real-time systems. The interrupts are handled in the same order as they arrive,
First come first served.
Implementation of Queue
Queue
can be implemented using an Array, Stack or Linked List. The easiest way of
implementing a queue is by using an Array. Initially the head(FRONT) and the tail(REAR) of the queue points
at the first index of the array (starting the index of array from 0). As we add
elements to the queue, the tail keeps on moving ahead, always pointing to the
position where the next element will be inserted, while the head remains at the
first index.
When
we remove element from Queue, we can follow two possible approaches (mentioned
[A] and [B] in above diagram). In [A] approach, we remove the element at head position, and then one by
one move all the other elements on position forward. In approach [B] we remove
the element from head position and then move head to the next position.
In
approach [A] there is an overhead of shifting the elements one position forward
every time we remove the first element. In approach [B] there is no such
overhead, but whener we move head one position ahead, after removal of first
element, the size on Queue is reduced by one space each time.
/* Below program is wtitten in C++ language */
#define SIZE 100
class Queue
{
int a[100];
int rear; //same as tail
int front; //same as head
public:
Queue()
{
rear = front =
-1;
}
void enqueue(int
x); //declaring enqueue, dequeue and
display functions
int dequeue();
void display();
}
void Queue :: enqueue(int x)
{
if( rear =
SIZE-1)
{
cout <<
"Queue is full";
}
else
{
a[++rear] = x;
}
}
int queue :: dequeue()
{
return
a[++front]; //following approach [B],
explained above
}
void queue :: display()
{
int i;
for( i = front; i
<= rear; i++)
{
cout <<
a[i];
}
}
To
implement approach [A], you simply need to change the dequeue method, and
include a for loop which will shift all the remaining elements one position.
return a[0];
//returning first element
for (i = 0; i < tail-1; i++) //shifting all other elements
{
a[i]= a[i+1];
tail--;
}
Analysis of Queue
·
Enqueue
: O(1)
·
Dequeue
: O(1)
·
Size
: O(1)
·
Queue Data Structure using Stack
·
A
Queue is defined by its property of FIFO, which means First in First Out, i.e
the element which is added first is taken out first. Hence we can implement a
Queue using Stack for storage instead of array.
·
For
performing enqueue we require only one stack
as we can directly push data into stack, but to
performdequeue we will require two Stacks, because we need
to follow queue's FIFO property and if we directly popany data element out of
Stack, it will follow LIFO approach(Last in First Out).
·
·
Implementation of Queue using Stacks
·
In
all we will require two Stacks, we will call them InStack and OutStack.
·
class
Queue {
·
public:
·
Stack S1, S2;
·
//defining methods
·
·
void enqueue(int x);
·
·
int dequeue();
·
}
·
·
We
know that, Stack is a data structure, in which data can be added using push() method and data can be
deleted using pop() method. To learn about
Stack, follow the link : Stack Data Structure
·
·
Adding Data to Queue
·
As
our Queue has Stack for data storage in place of arrays, hence we will be
adding data to Stack, which can be done using the push() method, hence :
·
void
Queue :: enqueue(int x) {
·
S1.push(x);
·
}
·
·
Removing Data from Queue
·
When
we say remove data from Queue, it always means taking out the First element
first and so on, as we have to follow the FIFO approach. But if we simply
perform S1.pop() in our dequeue method, then it will remove
the Last element first. So what to do now?
·
·
int
Queue :: dequeue() {
·
while(S1.isEmpty()) {
·
x = S1.pop();
·
S2.push();
·
}
·
·
//removing the element
·
x = S2.pop();
·
·
while(!S2.isEmpty()) {
·
x = S2.pop();
·
S1.push(x);
·
}
·
·
return x;
·
}
Introduction to Linked
Lists
Linked
List is a linear data structure and it is very common data structure which
consists of group of nodes in a sequence which is divided in two parts. Each
node consists of its own data and the address of the next node and forms a
chain. Linked Lists are used to create trees and graphs.
Advantages of Linked Lists
·
They
are a dynamic in nature which allocates the memory when required.
·
Insertion
and deletion operations can be easily implemented.
·
Stacks
and queues can be easily executed.
·
Linked
List reduces the access time.
Disadvantages of Linked
Lists
·
The
memory is wasted as pointers require extra memory for storage.
·
No
element can be accessed randomly; it has to access each node sequentially.
·
Reverse
Traversing is difficult in linked list.
Applications of Linked
Lists
·
Linked
lists are used to implement stacks, queues, graphs, etc.
·
Linked
lists let you insert elements at the beginning and end of the list.
·
In
Linked Lists we don’t need to know the size in advance.
Types of Linked Lists
·
Singly Linked List : Singly linked lists contain
nodes which have a data part as well as an address part i.e. next, which points
to the next node in sequence of nodes. The operations we can perform on singly
linked lists are insertion, deletion and traversal.
·
Doubly Linked List : In a doubly linked list,
each node contains two links the first link points to the previous node and the
next link points to the next node in the sequence.
·
Circular Linked List : In the circular linked list
the last node of the list contains the address of the first node and forms a
circular chain.
Linear Linked List
The
element can be inserted in linked list in 2 ways :
·
Insertion
at beginning of the list.
·
Insertion
at the end of the list.
We
will also be adding some more useful methods like :
·
Checking
whether Linked List is empty or not.
·
Searching
any element in the Linked List
·
Deleting
a particular Node from the List
Before
inserting the node in the list we will create a class Node. Like shown below :
class Node
{
public:
int data;
//pointer to the next node
node* next;
node() {
data = 0;
next = NULL;
}
node(int x) {
data = x;
next = NULL;
}
}
We
can also make the properties data and next as private, in that case we
will need to add the getter and setter methods to access them. You can add the
getters and setter like this :
int getData()
{
return data;
}
void setData(int
x) {
this.data = x;
}
node* getNext()
{
return next;
}
void setNext(node
*n) {
this.next = n;
}
Node
class basically creates a node for the data which you enter to be included into
Linked List. Once the node is created, we use various functions to fit in that
node into the Linked List.
Linked List class
As
we are following the complete OOPS methodology, hence we will create a separate
class for Linked List, which will have all its methods. Following
will be the Linked List class :
class LinkedList
{
public:
node *head;
//declaring the functions
//function to add Node at front
int
addAtFront(node *n);
//function to check whether Linked list is
empty
int isEmpty();
//function to add Node at the End of list
int addAtEnd(node
*n);
//function to search a value
node* search(int
k);
//function to delete any Node
node*
deleteNode(int x);
LinkedList() {
head = NULL;
}
}
Insertion at the Beginning
Steps
to insert a Node at beginning :
1. The first Node is the Head
for any Linked List.
2. When a new Linked List is
instantiated, it just has the Head, which is Null.
3. Else, the Head holds the
pointer to the first Node of the List.
4. When we want to add any
Node at the front, we must make the head point to it.
5. And the Next pointer of the
newly added Node, must point to the previous Head, whether it be NULL(in case
of new List) or the pointer to the first Node of the List.
6. The previous Head Node is
now the second Node of Linked List, because the new Node is added at the front.
int LinkedList :: addAtFront(node *n) {
int i = 0;
//making the next of the new Node point to Head
n->next = head;
//making the new Node as Head
head = n;
i++;
//returning the position where Node is added
return i;
}
Inserting at the End
Steps
to insert a Node at the end :
1. If the Linked List is empty
then we simply, add the new Node as the Head of the Linked List.
2. If the Linked List is not
empty then we find the last node, and make it' next to the new Node, hence
making the new node the last Node.
int LinkedList :: addAtEnd(node *n) {
//If list is empty
if(head == NULL)
{
//making the new Node as Head
head = n;
//making the next pointe of the new Node as
Null
n->next = NULL;
}
else {
//getting the last node
node *n2 = getLastNode();
n2->next = n;
}
}
node* LinkedList :: getLastNode() {
//creating a pointer pointing to Head
node* ptr = head;
//Iterating over the list till the node whose
Next pointer points to null
//Return that node, because that will be the
last node.
while(ptr->next!=NULL) {
//if Next is not Null, take the pointer one
step forward
ptr = ptr->next;
}
return ptr;
}
Searching for an Element in
the List
In
searhing we do not have to do much, we just need to traverse like we did while
getting the last node, in this case we will also compare the data of the Node. If we get the
Node with the same data, we will return it, otherwise we will make our pointer
point the next Node, and so on.
node* LinkedList :: search(int x) {
node *ptr = head;
while(ptr != NULL
&& ptr->data != x) {
//until we reach the end or we find a Node
with data x, we keep moving
ptr = ptr->next;
}
return ptr;
}
Deleting a Node from the
List
Deleting
a node can be done in many ways, like we first search the Node with data which we want to delete and
then we delete it. In our approach, we will define a method which will take the data to be deleted as argument,
will use the search method to locate it and will then remove the Node from the
List.
To
remove any Node from the list, we need to do the following :
·
If
the Node to be deleted is the first node, then simply set the Next pointer of
the Head to point to the next element from the Node to be deleted.
·
If
the Node is in the middle somewhere, then find the Node before it, and make the
Node before it point to the Node next to it.
node* LinkedList :: deleteNode(int x) {
//searching the Node with data x
node *n = search(x);
node *ptr = head;
if(ptr == n) {
ptr->next = n->next;
return n;
}
else {
while(ptr->next != n) {
ptr = ptr->next;
}
ptr->next = n->next;
return n;
}
}
Checking whether the List
is empty or not
We
just need to check whether the Head of the List is NULL or not.
int LinkedList :: isEmpty() {
if(head == NULL)
{
return 1;
}
else { return 0; }
}
Now
you know a lot about how to handle List, how to traverse it, how to search an
element. You can yourself try to write new methods around the List.
If
you are still figuring out, how to call all these methods, then below is how
your main() method will look like. As
we have followed OOP standards, we will create the objects of LinkedList class to initialize our
List and then we will create objects of Node class whenever we want to
add any new node to the List.
int main() {
LinkedList L;
//We will ask value from user, read the value
and add the value to our Node
int x;
cout <<
"Please enter an integer value : ";
cin >> x;
Node *n1;
//Creating a new node with data as x
n1 = new Node(x);
//Adding the node to the list
L.addAtFront(n1);
}
Similarly
you can call any of the functions of the LinkedList class, add as many Nodes
you want to your List.
Circular Linked List
Circular Linked List is little
more complicated linked data structure. In the circular linked list we can
insert elements anywhere in the list whereas in the array we cannot insert
element anywhere in the list because it is in the contiguous memory. In the
circular linked list the previous element stores the address of the next
element and the last element stores the address of the starting element. The
elements points to each other in a circular way which forms a circular chain.
The circular linked list has a dynamic size which means the memory can be
allocated when it is required.
Application of Circular Linked List
·
The real life application where the circular linked list is used
is our Personal Computers, where multiple applications are running. All the
running applications are kept in a circular linked list and the OS gives a
fixed time slot to all for running. The Operating System keeps on iterating
over the linked list until all the applications are completed.
·
Another example can be Multiplayer games. All the Players are kept
in a Circular Linked List and the pointer keeps on moving forward as a player's
chance ends.
·
Circular Linked List can also be used to create Circular Queue. In
a Queue we have to keep two pointers, FRONT and REAR in memory all the time,
where as in Circular Linked List, only one pointer is required.
Implementing Circular Linked List
Implementing a circular linked
list is very easy and almost similar to linear linked list implementation, with
the only difference being that, in circular linked list the last Node will have it's next point to the Head of the List. In Linear linked list the
last Node simply holds NULL in it's next pointer.
So this will be oue Node class,
as we have already studied in the lesson, it will be used to form the List.
class Node {
public:
int data;
//pointer to the next node
node* next;
node() {
data = 0;
next = NULL;
}
node(int x) {
data = x;
next = NULL;
}
}
Circular Linked List
Circular Linked List class will
be almost same as the Linked List class that we studied in the previous lesson,
with a few difference in the implementation of class methods.
class CircularLinkedList {
public:
node *head;
//declaring the functions
//function to add Node at front
int addAtFront(node *n);
//function to check whether Linked list is empty
int isEmpty();
//function to add Node at the End of list
int addAtEnd(node *n);
//function to search a value
node* search(int k);
//function to delete any Node
node* deleteNode(int x);
CircularLinkedList() {
head = NULL;
}
}
Insertion at the Beginning
Steps to insert a Node at
beginning :
1.
The first Node is the Head for any Linked List.
2.
When a new Linked List is instantiated, it just has the Head,
which is Null.
3.
Else, the Head holds the pointer to the fisrt Node of the List.
4.
When we want to add any Node at the front, we must make the head
point to it.
5.
And the Next pointer of the newly added Node, must point to the
previous Head, whether it be NULL(in case of new List) or the pointer to the
first Node of the List.
6.
The previous Head Node is now the second Node of Linked List,
because the new Node is added at the front.
int CircularLinkedList :: addAtFront(node *n) {
int i = 0;
/* If the list is empty */
if(head == NULL) {
n->next = head;
//making the new Node as Head
head = n;
i++;
}
else {
n->next = head;
//get the Last Node and make its next point to new Node
Node* last = getLastNode();
last->next = n;
//also make the head point to the new first Node
head = n;
i++;
}
//returning the position where Node is added
return i;
}
Insertion at the End
Steps to insert a Node at the end
:
1.
If the Linked List is empty then we simply, add the new Node as
the Head of the Linked List.
2.
If the Linked List is not empty then we find the last node, and
make it' next to the new Node, and make the next of the Newly added Node point
to the Head of the List.
int CircularLinkedList :: addAtEnd(node *n) {
//If list is empty
if(head == NULL) {
//making the new Node as Head
head = n;
//making the next pointer of the new Node as Null
n->next = NULL;
}
else {
//getting the last node
node *last = getLastNode();
last->next = n;
//making the next pointer of new node point to head
n->next = head;
}
}
Searching for an Element in the List
In searhing we do not have to do
much, we just need to traverse like we did while getting the last node, in this
case we will also compare the data of the Node. If we get the Node with
the same data, we will return it, otherwise we will make our pointer point the
next Node, and so on.
node* CircularLinkedList :: search(int x) {
node *ptr = head;
while(ptr != NULL && ptr->data != x) {
//until we reach the end or we find a Node with data x, we keep moving
ptr = ptr->next;
}
return ptr;
}
Deleting a Node from the List
Deleting a node can be done in
many ways, like we first search the Node with data which we want to delete and then we
delete it. In our approach, we will define a method which will take the data to be deleted as argument, will use
the search method to locate it and will then remove the Node from the List.
To remove any Node from the list,
we need to do the following :
·
If the Node to be deleted is the first node, then simply set the
Next pointer of the Head to point to the next element from the Node to be
deleted. And update the next pointer of the Last Node as well.
·
If the Node is in the middle somewhere, then find the Node before
it, and make the Node before it point to the Node next to it.
·
If the Node is at the end, then remove it and make the new last
node point to the head.
node* CircularLinkedList :: deleteNode(int x) {
//searching the Node with data x
node *n = search(x);
node *ptr = head;
if(ptr == NULL) {
cout << "List is empty";
return NULL;
}
else if(ptr == n) {
ptr->next = n->next;
return n;
}
else {
while(ptr->next != n) {
ptr = ptr->next;
}
ptr->next = n->next;
return n;
}
}
Verify that the network adapter is properly installed and detected by the computer with no conflicts. In Microsoft Windows, open the Device Manager and verify there are no errors. "Network adapters" should be present for each network adapter installed in the computer, similar to the example on the right.
If this is a wired network, verify that the network cable is properly connected and make sure the LEDs next to the network jack are properly illuminated. For example, a network card with a solid green LED or light usually indicates that the card is either connected or receiving a signal. If the green light is flashing, this is an indication of data being sent or received. The picture to the right is an example of a LAN port with two LED indicators next to the RJ-45 port. With this port, one LED will light up if connected properly and the other will flash when transmitting data.
If you're using a laptop with a wireless network, look for the laptop's Wi-Fi button and make sure it is turned on. Many laptops have a Wi-Fi button that allows the wireless network to be turned on and off. The Wi-Fi button is often located just above the keyboard or on the front edge of the laptop, but it also may be integrated with a F key as well. The pictures to the right are examples of a Wi-Fi button and Wi-Fi indicator on a F key that are enabled.
