**I UNIT-Linear structures**

**(LINKED LISTS, STACKS & QUEUES)**

**1.**

**Define algorithm.**

An algorithm is
a step-by-step recipe for solving an instance of a problem. It is a precise
procedure for solving a problem in finite number of steps. An algorithm states
the actions to be executed and the order in which these actions are to be
executed. An algorithm is also defined as a well-ordered collection of clear
and simple instructions of definite and effectively computable operations that,
when executed, produces a result and stops executing at some point in a finite
amount of time rather than just going on and on infinitely.

**2.**

**List the properties of an algorithm.**

The properties
of an algorithm includes,

·
An algorithm
takes zero or more inputs.

·
An algorithm
results in one or more outputs.

·
All operations
can be carried out in a finite time.

·
An algorithm
should be efficient and flexible.

·
It should use
less memory space as much as possible.

·
An algorithm
must terminate after a finite number of steps.

·
Each step in the
algorithm after a finite number of steps.

·
Each step in the
algorithm must be easily understood for someone reading it.

·
An algorithm
should be concise and compact to facilitate verification of their correctness.

**3.**

**Define efficiency of an algorithm.**

Efficiency of an
algorithm denotes the rate at which an algorithm solves a problem of size n. it
is measured by the amount of resources it uses, the time and the space. The
time refers to the number of steps the algorithm executes while the space
refers to the number of unit memory storage it requires.

**4.**

**Define run time. What is its impact in calculating time complexity?**

Run time is the
time to execute the compiled program. The run time of an algorithm depends upon
the number of instructions present in the algorithm. The run time is in the
control of the programmer, as the compiler is going to compile only the same
number of statements, irrespective of the type of compiler used.

**5.**

**Define time complexity of an algorithm.**

Time complexity
of an algorithm is the amount of time (or the number of steps) needed by a
program to complete its task.

**6.**

**Define worst case of an algorithm.**

Worst case is
the longest time that an algorithm will use over all instances of size n for a
given problem to produce a desired result.

**7.**

**Define space complexity of an algorithm.**

Space complexity
of a program is the amount of memory used at once by the algorithm until it
completes its execution.

**8.**

**State the different memory spaces occupied by an algorithm.**

The following
are the different spaces considered for determining the amount of memory used
by the algorithm, instruction space, data space and environment space.

**9.**

**Define divide and conquer algorithm.**

Divide and
conquer is based on dividing the problem into several, smaller sub instances,
solving them independently and then combining the sub
instance solutions so as to yield a solution for the original instance.

**10.**

**Mention some of the problems that implements divide and conquer algorithm.**

Problems that
implements divide and conquer algorithm are quick sort, binary search, merge
sort and strassen’s matrix multiplication.

**11.**

**Define data structures.**

Data structure
defines a way of organizing all data items that consider not only the elements
stored but also stores the relationship between the elements.

**12.**

**Define static data structures.**

A data structure
formed when the number of data items are known in advance is referred as static
data structure or fixed size data structure.

**13.**

**List some of the static data structures in C.**

Some of the
static data structures in C refer to arrays, pointers, structures, etc.,

**14.**

**Define dynamic data structures.**

A data structure
formed when the number of data items are not known in advance is known as
dynamic data structure or variable size data structure.

**15.**

**List some of the dynamic data structures in C.**

Some of the
dynamic data structures in C refers to linked lists, stacks, queues, trees.
Etc.,

**16.**

**What are the different types of data structure?**

·
Linear data
structure.

·
Non-linear data
structure

**17.**

**Define linear data structures.**

Linear data
structures are data structures having a linear relationship between its
adjacent elements. Linked lists are examples of linear data structures.

**18.**

**Define non-linear data structures.**

Non-linear data
structures are data structures that don’t have a linear relationship between its
adjacent elements but have a hierarchical relationship between the elements.
Trees and graphs are examples of non-linear data structures.

**19.**

**State the different types of linked lists.**

The different types
of linked list include single linked list, double linked list and circular
linked list.

**20.**

**State the different types of circular linked lists.**

The different types
of circular linked list include circular singly linked list and circular doubly
linked list.

**21.**

**List the basic operations carried out in a linked list.**

The basic
operations carried out in a linked list includes,

·
Searching an
element in a list.

·
Finding the
successor element of a node.

·
Finding the predecessor
element of a node.

·
Appending a
linked list to another existing list.

·
Splitting a
linked list in to two lists.

·
Arranging a
linked list in ascending order.

**22.**

**List the advantages in using a linked list.**

The advantages in
using a linked list are,

·
It is not
necessary to specify the number of elements in a linked list during its
declaration.

·
Linked list can
grow and shrink in size depending upon the insertion and deletion that occurs
in the list.

·
Insertions and
deletions at any place can be handled easily and efficiently.

·
A linked list
does not waste any memory space.

**23.**

**List out the disadvantages in using a linked list.**

The
disadvantages in using a linked list are,

·
Searching a
particular element in a list is difficult and time consuming.

·
A linked list
will use more storage space than an array to store the same number of elements
(therefore each element in a list needs additional memory space for storing the
address of the next node).

**24.**

**Define Abstract Data Type(ADT).**

An abstract data
type is a set of operations for which the implementation of the data structure
is not specified anywhere in the program.

**25.**

**State the difference between arrays and linked list.**

**Arrays linked list**

Size of any array is fixed. Size
of a list is variable.

It is necessary to specify the
number it is not necessary to
specify the in an array number of, elements during
declaration. Number of
elements in during declaration.

Insertion and deletions are somewhat Insertions
and deletions are carried in an difficult
out easily. Array.

It
occupies less memory than a linked list
It occupies more
memory. for
the same number of elements.

**26.**

**What are the applications of Arrays?**

·
Parallel Arrays
to store records

·
Sparse Matrices

·
Matrix
operations

**27.**

**Mention some of the application of linked list.**

Some of the
applications of linked lists are,

·
Polynomial
Manipulation

·
Stacks

·
Queues

**28.**

**Define a stack.**

Stack is an
ordered collection of elements in which insertions and deletions are restricted
to one end. The end from which elements are added and/or removed is referred to
as top of the stack. Stacks are also referred as “piles” and “push-down lists”.

**29.**

**List out the basic operations that can be performed on a stack and a queue.**

The basic
operations that can be performed on a stack and queue are,

·
Push operation

·
Pop operation

·
Peek operation

·
Empty check

·
Full occupied
check

·
Count no. of
nodes.

·
View contents.

**30.**

**State the different ways of representing expressions.**

The different
ways of representing expressions are,

·
Infix Notation

·
Prefix Notation

·
Postfix Notation

**31.**

**State the advantages of using infix notations.**

The advantages
of using infix notations are,

·
It is the
mathematical way of representing the expression.

·
It’s easier to
see visually which operation is done from first to LAST.

·

**32.**

**State the advantages of using postfix notations.**

The advantages
of using postfix notations are,

·
You need not
worry about the rules of precedence.

·
You need not
worry about the rules for right to left associativity.

·
You need not
need parenthesis to override the above rules.

**33.**

**State the rules to be followed during infix to postfix conversions.**

The rules to be
followed during infix to postfix conversions are,

·
Fully
parenthesize the expression starting from left to right (During parenthesizing,
the operators having higher precedence are first parenthesized).

·
Move the
operators one by one to their right, such that each operator replaces their
corresponding right parenthesized).

·
The part of the
expression, which has been converted into postfix, is to be treated as single
operand.

·
Once the
expression is converted into postfix form, remove all parentheses.

34.

**State the difference between stacks and linked lists.**
The difference
between stacks and linked lists is that insertions and deletions may occur anywhere
in a linked list, but only at the top of the stack.

**35.**

**Mention the advantages of representing stacks using linked lists than arrays.**

The advantages
of representing stacks using linked lists than arrays.

·
It is not
necessary to specify the number of elements to be sorted in a stack during its declaration
(since memory is allocated dynamically at run time when an element is added to
the stack).

·
Insertions and
deletions can be handled easily and efficiently.

·
Linked list
representation of stacks can grow and shrink in size without wasting memory
space, depending upon the insertion and deletion that occurs in the list.

·
Multiple stacks
can be represented efficiently using a chain for each stack.

**36.**

**Define a queue.**

Queue is an
ordered collection of elements in which insertions and deletions are restricted
to one end. The end from which elements are added and/or removed is referred to
as the rear end, and the end from which deletions are made is referred to as
the front end.

**37.**

**Define a priority queue.**

Priority queue
is a collection of elements, each containing a key referred as the priority for
that element. Elements can be inserted in any order (ie. of alternating
priority), but are arranged in order of their priority value in the queue. The
elements are deleted from the queue in the order of their priority (i.e., the
elements with the highest priority is deleted first). The elements with the
same priority are given equal importance and processed accordingly.

**38.**

**State the difference between queues and linked lists.**

The difference
between queues and linked lists is that insertions and deletions may occur
anywhere in linked list, but in queues insertions can be made only in the front
end.

**39.**

**Define a dequeue.**

Dequeue(Double-ended
queue) is another form of a queue in which insertions and deletions are made at
both the front and rear ends of the queue. There are two variations of a dequeue,
namely, input restricted dequeue and output restricted dequeue. The input restricted
dequeue allows insertion at one end (it can be either front or rear) only. The
output restricted dequeue allows deletion at one end (it can be either front or
rear) only.

**40.**

**What are the applications of Queue?**

·
Job Scheduling

·
Threading

**41.**

**Define Input Restricted Queue.**

A queue which
satisfies the following properties

·
Insertion takes
place at one end

·
Deletion is
allowed at both ends

**42.**

**Define Output Restricted Queue.**

A queue where
deletion is allowed at one end and insertion takes place at both ends.

**43.**

**What are the steps to be involved while deleting a node in a singly linked list.**

·
Find the
position P before which the node has to be deleted i.e., find previous(X,L).

·
Change the link
of the previous node to the link of the node to be deleted.

PÃ Next=PÃ NextÃ Next

**44.**

**What are the applications of priority queue?**

·
Operating system

·
External sorting

·
Greedy
algorithms

·
Event simulation

**II UNIT-TREE STRUCTURES**

**1.**

**Define a tree.**

A tree is a non-linear, two-dimensional
data structure, which represents hierarchical relationship between individual
data items.

**2.**

**Define the height of a Tree.**

The height of a tree is the length of
the longest path from the root to a leaf.

Eg

Level
0

Level 1

Level 2

**3.**

**Define a path in a tree.**

A path in a tree is a sequence of
distinct nodes in which successive nodes are connected by edges in the tree.

**4.**

**Define terminal nodes in a tree.**

A node that has no children is called as
a terminal node. It is also referred as a leaf node. These nodes have degree
has zero.

**5.**

**Define non-terminal nodes in a tree.**

All intermediate nodes that traverse the
given tree from its root node to the terminal nodes are referred as a
non-terminal node.

**6.**

**Define a binary tree.**

A binary tree is a tree in which all the
leaves are on the same level and every non-leaf node has exactly two children.

**7.**

**Define a full binary tree.**

A full binary tree is a tree in which
all leaves are on the same level and every non-leaf node has exactly two
children.

**8.**

**Define a complete binary tree.**

A complete binary tree is a tree in
which every non-leaf node has exactly two children not necessarily to be on the
same level.

**9.**

**Define a right-skewed binary tree.**

A right-skewed binary tree is a tree,
which has only right child nodes.

**10.**

**State the properties of a binary tree.**

The properties of a binary tree
includes,

·
The maximum
number of nodes on level n of a binary tree is 2

^{n}-1, where n≥1.
·
The maximum
number of nodes in a binary tree of height n is 2

^{n}-1,where n≥1.
·
For any
non-empty tree, n

_{l}=n_{d}+1 where n_{l}is the number of leaf nodes and n_{d }is the number of nodes of degree 2.**11.**

**What are the different ways of representing a binary tree?**

The different ways of representing a
binary tree includes,

·
Linear
representation using arrays.

·
Linked
representation using pointers.

**12.**

**What is meant by binary tree traversal?**

Traversing a binary tree, means moving
through all the nodes in the binary tree, visiting each node in the tree only
once.

**13.**

**What are the different binary tree traversal techniques?**

The different binary tree traversal
techniques are,

·
Preorder
traversal

·
Inorder
traversal

·
Postorder
traversal

·
Levelorder
traversal

**14.**

**What are the tasks performed while traversing a binary tree?**

The tasks performed while traversing a
binary tree are,

·
Visiting a node.

·
Traverse the
left subtree

·
Traverse the
right subtree.

**15.**

**What are the tasks performed during preorder traversal?**

The tasks performed during preorder traversal,

·
Process the root
node

·
Traverse the
left subtree

·
Traverse the
right subtree.

**16.**

**What are the tasks performed during inorder traversal.**

The tasks performed during inorder
traversal,

·
Traverse the
left subtree.

·
Process the root
node.

·
Traverse the
right subtree.

**17.**

**What are the tasks performed during postorder traversal?**

The tasks performed during postorder
traversal,

·
Traverse the
left subtree

·
Traverse the
root node.

·
Traverse the
right subtree.

**18.**

**What are the tasks performed during levelorder traversal?**

The tasks performed during level order
traversal,

·
Process the root
node at level1

·
Traverse the
next level(i.e., level 2), below the root node.

·
Process the
nodes from left to right in that level.

·
Similarly
traverse the next level and process the nodes from left to right and continue
till the end of levels.

**19.**

**State the merits and demerits of linear representation of binary trees.**

The merits of linear representation of
binary trees includes

·
Storage method
is easy and can be easily implemented in arrays.

·
When the location
of a parent/child node is known other
one can be determined easily.

·
It requires
static memory allocation so it is easily implemented in all programming
language.

The demerits of linear representation of
binay trees includes,

·
Insertions and
deletions in a node, taker an excessive amount of processing time, due to data
movement up and down the array.

**20.**

**State the merits and demerits of linked representation of a binary tree.**

The merits of linked representation of
binary trees includes

·
Insertions and
deletions in a node, involves no data movement except the rearrangement of
pointers, hence less processing time.

The demerits of linked representation of
binary trees includes,

·
Given a node
structure, it is difficult to determine its parent node.

·
Memory spaces
are wasted for storing null pointers for the nodes, which have one or no
subtrees.

·
It requires
dynamic memory allocation, which is not possible in some programming languages.

**21.**

**What do you mean by general trees.**

General trees are a tree with nodes having
any number of children.

**22.**

**How do you convert general trees to binary trees?**

General trees can be converted to binary
trees using leftmost child and right siblings representation.

**23.**

**Define a binary search tree.**

A binary search tree is a special binary
tree, which is either empty or if it is empty it should satisfy the following
characteristics.

·
Every node has a
value and no two nodes should have the same value(i.e., the values in the
binary search tree are distinct.

·
The values in
any left subtree is less than the value of its parent node.

·
The values in
any right subtree is greater than the value of its parent node.

·
The left and
right subtrees of each node are again binary search trees.

**24.**

**What are the basic operations performed in a binary search tree.**

The basic operations performed in a
binary search tree are,

·
Creation of a
binary search tree

·
Insertion of a
node

·
Deletion of a
node.

·
Searching a node

·
Modification of
a node.

·
View the
contents of the binary search tree.

**25.**

**What are the basic operations performed while creation of a binary search tree.**

The basic operations performed while
creating a binary search tree are,

·
Creating a node.

·
Read details for
the node from the user

·
Insert the node
in the binary search tree.

**26.**

**What are the various positions from which nodes can deleted?**

The various positions from which nodes
can be deleted are,

·
Deleting the
leaf node.

·
Deleting the
node with only one child.

·
Deleting the
node with two children.

**27.**

**Define threaded binary tree.**

In linked representation of binary
trees, we can see that all leaf nodes and some non-leaf nodes have NULL values.
Instead of storing NULL values in the left and right pointer fields, we can
store some useful information such as inorder predecessor in the left pointer
field and inorder successor in the right pointer field. These links are
considered as threads. A binary tree, which implements these threads are
referred to as threaded binary tree.

**28.**

**What are the different types of threaded binary tree?**

The different types of threaded binary
trees are,

·
Left-inorder
threaded binary tree.

·
Right-inorder
threaded binary tree.

·
Fully-inorder
threaded binary tree.

**29.**

**Define Left-inorder threaded binary trees.**

If you use only the left pointer fields
(for storing inorder predecessor) as threads, then the binary tree is referred
to as left in-threaded binary tree.

**30.**

**Define Right-inorder threaded binary trees.**

If you use only the right pointer fields
(for storing inorder successor) as threads, then the binary tree is referred to
as right in-threaded binary tree.

**31.**

**Define Fully-inorder threaded binary trees.**

If you use only the right pointer fields
as threads, then the binary tree is referred to as fully in-threaded binary
tree.

**32.**

**Define balance factor of a node in an AVL tree.**

Balance factor of a node in an AVL tree
is defined as the difference between the height of the left subtree and the
height of the right subtree of that node.

**33.**

**Define an AVL tree.**

If the balance factors of all node in a
tree are 1, 0 and -1, then the tree is referred to as AVL tree, is also
referred to as balanced tree. The word AVL is an acronym for the Russian
mathematicians, Adel’son-Vel’skii and Landis, who first defined balanced trees.

**34.**

**What are the various ways of balancing an unbalanced tree?**

The various ways of balancing an
unbalanced tree are,

·
Left to Left
rotation

·
Left to Right
rotation

·
Right to Right
rotation

·
Right to Left
rotation

**35.**

**When do you apply left-to-left rotations in an unbalanced tree?**

We apply left-to-left rotations in an
unbalanced tree, if the new node is inserted at left subtree of left child to
the pivot node.

**36.**

**When do you apply left-to-right rotations in an unbalanced tree?**

We apply left-to-right rotations in an
unbalanced tree, if the new node is inserted at right subtree of left child to
the pivot node.

**37.**

**When do you apply right-to-left rotations in an unbalanced tree?**

We apply right-to-left rotations in an
unbalanced tree, if the new node is inserted at left subtree of right child to
the pivot node.

**38.**

**When do you apply right-to-right rotations in an unbalanced tree?**

We apply right-to-right rotations in an
unbalanced tree, if the new node is inserted at right subtree of left child to
the pivot node.

**39.**

**Define a binary heap.**

A binary heap is an array that is viewed
as a complete binary tree. A complete binary tree is completely filled on all
levels except possibly the lowest, and the lowest level is filled from the
left. The root of the tree is stored in an array element 0. The parent of the
node n is stored at node (n/2). The right child is stored at node 2n, and the
left child at node is stored at 2n+1.

**40.**

**Define a min-heap.**

The heap which satisfies the min-heap
property, i.e., if for every node n except the root, has a value greater than
its parent is referred to as min heap.

**41.**

**Define max-heap.**

The heap which satisfies the max-heap
property, i.e., if for every node n except the root, has a value lesser than
its parent is referred to as max heap.

**UNIT III-HASHING AND SETS**

**1.**

**What is Hashing?**

The implementation of hash table where
the data structure is an array of some fixed size containing the keys.

**2.**

**Give a simple hash function when the input keys are integers.**

Hash function=Key mod Tablesize

**3.**

**Compare the various hashing techniques.**

**Technique Load Factor**

Separate
chaining - close to 1

Open
Addressing - should not exceed 0.5

Rehashing - reasonable load factor

**UNIT IV- GRAPHS**

**1.**

**Define a graph.**

A graph is a non-linear data structure
that represents less relationship between its adjacent elements. There is no
hierarchical relationship between the adjacent elements in case of graphs.

**2.**

**List the two types of graphs.**

·
Directed graph

·
Undirected graph

**3.**

**Define undirected graph.**

If an edge between any two nodes in a
graph is not directionally oriented a graph is called as undirected graph. It
is also referred as unqualified graph.

**4.**

**Define directed graph.**

If an edge between any two nodes in a
graph is directionally oriented, a graph is called as directed graph; it is
also referred as a digraph.

**5.**

**Define a path in a graph.**

A path in a graph is defined as a
sequence of distinct vertices each adjacent to the next, except possibly the
first vertex and last vertex is different.

**6.**

**Define a cycle in a graph.**

A cycle is a path containing at least
three vertices such that the starting and the ending vertices are the same.

**7.**

**Define a strongly connected graph.**

A directed graph is said to be strongly
connected if, for every pair of distinct vertices there is a directed path from
every vertex to every other vertex. It is also referred as a complete graph.

**8.**

**Define a weakly connected graph.**

A directed graph is said to be weakly
connected graph, if any vertex doesn’t have a directed path to any other
vertices.

**9.**

**Define a weighted graph.**

A graph is said to be weighted graph if
every edge in the graph is assigned some weight or value. The weight of an edge
is a positive value that may be representing the distance between the vertices
or the weights of the edges along the path.

**10.**

**Define incidence matrix.**

Incidence matrix is a representation
used to represent a graph with zeros and ones. A graph containing n vertices
can be represented by a matrix with m rows and n columns. The matrix is formed
by storing 1 in its i

^{th }row and j^{th }column corresponding to the matrix, if there exists a i^{th }vertex, connected to one end of the j^{th }edge and a 0, if there is no i^{th }vertex, connected to any end of the j^{th }edge of the graph.**11.**

**Define adjacency matrix.**

Adjacency matrix is a representation
used to represent a graph with zeros and ones. A graph containing n vertices
can be represented by a matrix with n rows n columns. The matrix is formed by
storing 1 in its i

^{th}row and j^{th}coilumn of the matrix, if there exists an edge between i^{th }and j^{th }vertex of the graph, and a 0, if there is no edge between i^{th }and j^{th}vertex of the graph.**12.**

**Define adjacency list.**

A graph containing m vertices and n
edges can be represented using a linked list, referred to as adjacency list.

**13.**

**Define path matrix.**

A graph containing n vertices can be
represented by a matrix with n rows and n columns. The matrix is formed by
storing 1 in its i

^{th}row and j^{th }column of the matrix, if there exists an edge between i^{th }and j^{th}vertex of the graph, and a 0, if there is no edge between i^{th }and j^{th}vertex of the graph, such a matrix is referred to as path matrix.**14.**

**What is meant by traversing a graph? State the different ways of traversing a graph.**

Traversing a graph means visiting all
the nodes in the graph. In many practical applications traversing a graph is
important, such that each vertex is visited once systematically by traversing
through minimum number of paths. The two important graph traversal methods are,

·
Depth-first
traversal(or) Depth-first search(DFS)

·
Breath-first
traversal(or)Breath-first search(BFS)

**15.**

**What is meant by topological sorting?**

Topological sorting is an ordering of
the vertices in a directed acyclic graph, such that if there is a path from x
to y, then y appears after x in the ordering. As each vertices is visited, push
the vertex in to the stack. Finally print the stack elements in order.

**16.**

**What is the use of Dijkstra’s algorithm?**

Dijkstra’s algorithm is a greedy
algorithm to find the minimum distance from a node to all other nodes. Our aim
is to visit any two nodes, with a minimum weight, such that the distance
travelled is minimum. One such algorithm is referred as Dijkstra’s shortest
path algorithm. This is very much used in travelling salesman problem.

**17.**

**Define spanning trees?**

A spanning tree of a graph is just a sub
graph that contains all the vertices of the graph, but uses sufficient edges to
form a tree. A graph may have spanning trees.

**18.**

**Define minimum spanning trees?**

A minimum spanning tree is one of the
spanning trees of the graph which has the smallest sum of weights amongst all
spanning trees.

**19.**

**How can you minimum spanning trees from graphs?**

You can create minimum spanning trees
from graphs using two different greedy algorithms. They are,

·
Prim’s algorithm

·
Kruskal’s
algorithm

**20.**

**How can you create minimum spanning tree using prim’s algorithm?**

In Prim’s algorithm, for creating minimum spanning
tree, start with any node and include the other node in the spanning tree, on
the basis of its weight of their edges connected to that node, and move on
until it includes all the n vertices are connected by n-1 edges. The resulting
tree contains all the vertices present in the given graph, such that the weight
of the edges in the resulting tree is minimum. The tree produced by the above
algorithm, is the minimum spanning tree.

**21.**

**How can you create minimum spanning tree using Kruskal’s algorithm?**

In Kruskal’s algorithm, for creating minimum
spanning tree, sort the edges in the graph on increasing order by its weight.
Take the first edge from the ordered list and add the source vertex to form a
tree. Check whether the destination vertex to the tree. If it already exists,
move to the next edge in the ordered list. Repeat the steps until the tree
contains all the n vertices. The tree produced by the above algorithm, is the
minimum spanning tree.

**22.**

**Define NP-completeness?**

NP-completeness is a type of many important problems
that can’t be solved quickly. Knowing these problems is hard, and trying to
solve them, and do something better. NP stands for “nondeterministic polynomial
time” where nondeterministic is just a fancy way of talking about guessing a
solution. A problem is in NP if you can quickly(in polynomial time)test whether
a solution is correct(without about how it might be to find the solution0.
Problems in NP are still relatively easy. If only we could guess the right
solution, we could then quickly test it.

**23.**

**What is a Sink node?**

The node has no directed edge that comes out of the
node, then it is said to be sink node.

**24.**

**Explain the following terms i)Degree ii)indegree iii)outdegree**

i) Degree-it denotes the number of edges for a node

ii) In degree-Number of edges entering from the
vertex

iii)he number of edges
exiting from the vertex.

**25.**

**What is the Source node?**

The node which is used to shares the information is
called as source node.

**26.**

**What is Acyclic graph?**

If there is no path from any node back to itself
then the graph is said to be acyclic graph.

**27.**

**List the 2 important representations of graph.**

·
Matrix
representation

·
List structure

**28.**

**Give any three examples for shortest path problem.**

·
In computer
networking such as LAN, WAN, Inter Networking.

·
In telephone
cabeling graph theory is effectively used.

·
Job Scheduling
algorithms.

**29.**

**List the applications of MST.**

·
Connecting of
computers in the lab with the minimum length of wire.

·
Telephone
networking companies.

·
It provides way
for clustering points in space in to natural graphs.

**30.**

**Define the term Prim’s Algorithm.**

In a graph a pair with the minimum
weight is to be chosen. Then adjacent to these vertices is the edge having
minimum weight is selected ie., in each stage, one node is picked as the root,
then add an edge and thus an associated vertex to the tree.

**31.**

**Define DFS.**

DFS means Depth First search it is like
a preorder traversal of a tree. It is continuous searching for the unvisited
nodes in the forward direction based on the recursive process.

**32.**

**What is called Biconnectivity?**

A connected undirected graph is
biconnected if there are no vertices whose removal disconnects the rest of the
graph.

**33.**

**What do you mean by Complete Graph?**

In a graph if there exists the path from
any vertex to any other vertex, then the graph is called as Complete Graph.

**V UNIT**

**1.**

**Define greedy algorithm.**

Greedy algorithm
is a method that always takes the best immediate or local, solution while
finding an answer. The greedy algorithm repeatedly executes a procedure, until
it can’t be done any more with the hope that the outcome will lead to desired
outcome for the global problem.

**2.**

**Mention some of the problems that implements greedy algorithm.**

The problems
that implements greedy algorithm are, coin change, Egyptian fractions, Map
coloring and Knapsack problem.

**3.**

**Define dynamic programming algorithm.**

Dynamic
programming is a general class of algorithms, which solve problems by solving
smaller versions of the problem, saving the solutions to the small problems and
then combining them to solve the larger problem.

**4.**

**Define backtracking algorithm.**

Backtracking is
an algorithm for solving a series of sub-problems each of which may have
multiple possible solutions and where the solution chosen for one sub-problem
may effect the possible solutions of later sub-problems. The problems that
implements backtracking algorithm are, Towers of Hanoi and 8 Queens problem.

**5.**

**What are the factors affecting the efficiency of an algorithm?**

·
Redundant
computation

·
Referencing
array elements

·
Late termination

**6.**

**List any two example for NP-Complete problems**

·
Hamiltonian
Cycle.

·
Traveling
Salesman Problem

·
Knapsack problem

·
Bin Packing