Non-Linear Data Structures

TreeIn computers, a tree is an abstract model of a hierarchical structure

A tree consists of nodes with a parent-child relation

Applications: Organization charts, File systems, Programming

environments A

B

E F

I J K

C

G H

D

Ancestors of a node: parent, grandparent etc.

Depth of a node: number of ancestors

Height of a tree: maximum depth of any node (3)

Descendant of a node: child, grandchild etc.

Subtree: tree consisting of a node and its descendants

Siblings: Children of the same parent

Root: node without parent (A)

Internal node: node with at least one child

(A, B, C, F)

External node (or leaf node ): node without

children (E, I, J, K, G, H, D)

Operations on a tree

size()

isEmpty()

root()

parent(p)

children(p)

isInternal(p)

isExternal(p)

isRoot(p)

swapElements(p, q)

replaceElement(p, o)

Binary Tree is a tree in which each internal node has at

most two children

Ordered Binary Tree

The children of a node are an ordered pair and are referred as left child and right child

Alternative recursive definition: a binary tree is either a tree consisting of a single node, or whose root has an ordered pair of children, each of which is a binary tree

Complete Binary tree which consists of each internal node having exactly two children.

Perfect or full binary tree will have each internal node having two children and all leaf nodes will be at the same level

Arithmetic expressions, decision processes, searching

Binary Tree n number of nodes

e number of external nodes

i number of internal nodes

h height

h i For Binary trees

Properties: For Complete Binary Tree

e = i + 1

n = 2e – 1

h (n - 1)/2

e 2h

h log2 e

h log2 (n + 1) - 1

Traversals in a tree

A traversal visits the nodes of a tree in a systematic manner

In a preorder traversal, a node is visited before its descendants

Application: print a structured document

In a postorder traversal, a node is visited after its

descendants

Application: compute space used by files in a

directory and its subdirectories

Specialization of an inorder traversal

Preorder abcdfge Postorder cfgdbea Inorder cbfdgae

Evaluating arithmetic operation is equivalent to a postorder traversal

a

b

c d

f g

e

Given pre and inorder

Preorder abcdfge Inorder cbfdgae

From preorder we know that a is the root, now find a in the inorder traversal

Now I know e is the right subtree

We are left with the subtree

bcdfg cbfdg

dfg fdg

Given pre and post order

We cannot find inorder because there can be two trees with the same pre and post order

pre a b c

post c b a

If each internal node of the binary tree has at least two children then the tree can be determined from pre and post order traversals.

pre post

a b c d f g e c f g d b e a from this I know that a is the root then I know that e is the right child of a from post order and from Preorder I see that there is nothing after e so e is the leaf

b c d f g c f g d b

Now I see that b is the root of the left sub tree and d is the right children ( from post order) and then from inorder I see that c is the only child of d which is the left child and so on.

Successor

Given x, find the node with the

smallest key greater then key[x]

we can distinguish two cases depending upon the right subtree of x

case 1: Right subtree of x is nonempty, then successor is leftmost node in the right subtree

case 2: The right subtree of x is empty, then the successor is the closest ancestor v of x such that x is descended from the left child of v.If there is no such ancestor then successor is undefined.

Insertion

take an element whose left and right children are null and insert it into T

find place in T where z belongs (as if searching for z)

and add z

Runtime on a tree of height h is O(h)

Deletion case 1

if x has no children : just remove x

Deletion Case 2

if x has exactly one child then to delete x simply make p[x] point to that child

Deletion Case 3

if x has two children, then to delete it we have to find its predecessor(going left and finding the rightmost node) or successor y

Replace x with y (it will have at most one child) and delete y

Deletion

Running time for delete in worst case is O(h)

Inorder traversal of a BST gives a sorted list and taken O(n) time.

Graph A graph is a pair(V,E) where V is set of nodes called

vertices E is a collection of pair of vertices callededges. vertices and edges are positions and storeelements

Directed Edge: ordered pair of vertices(u,v) wherefirst vertex u is the origin and second vertex v isthe destination

Undirected Edge:unordered pair of vertices forexample distance between two cities

In Directed graph all the edges are directed and inundirected graph all the edges are undirected

Electronic circuits, printed circuit boards,Integrated Circuits, Transportation network,Highway network, rail network, flight network,traffic network, electricity network, waternetwork, Computer networks, LAN, Web ,Databases- ER diagrams

Graphs

End vertices of an edge U and V are the endpoints of a

Edge incident on a vertex a, d,b are incident on v

adjacent vertices u and v are adjacent

Degree of a vertex : x has degree 5

If All vertices have the same degree it is called the regular graph.

parallel edges: h and i are parallel edges

Self loop : j is a self loop

Graphs path-sequence of alternating vertices and edges begins with a vertex and ends with a vertex each edge is preceded and followed by its

endpoints Simple path- a path such that all edges and

vertices are distinct v,b,x,h,z is a simple path u,c,w,e,x,g,y,f,w,d,v is not a simple path

cycle- circular sequence of alternating vertices and edges

each edge is preceded and followed by its endpoints Simple cycle : such that all its vertices and edges are

distinct v,b,x,g,y,f,w,c,u,a,v is a simple cycle u,c,w,e,x,g,y,f,w,d,v,a, u is not a simple cycle

Operations on a graph

incidentedges(v)

endvertices(e)

isdirected(e)

origin(e)

destination(e)

opposite(v,e)

areadjacent(v,w)

insertvertex(o)

insertedge(v,w,o)

insertdirectededge(v,w,o)

removevertex(v)

removeedge(e)

numvertices()

numedges()

Sub Graph: A Graph consisting of Subset of edges and subset of vertices of another graph is a sub graph of that graph.

A connected graph without cycles is a tree

A Collection of trees is called a forest.

m=no. of edges n= no. of vertices

for a trees m=n-1 if m<n-1 Graph is not connected

Incidence matrix

The graph is represented by a matrix of size |V| (number of vertices) by |E| (number of edges) where the entry [vertex, edge] contains the edge's endpoint data (simplest case: 1 -incident, 0 - not incident).

U V W X

A 1 1 0 0

B 0 1 0 1

C 1 0 1 0

D 0 1 1 0

E 0 0 1 1

Adjacency matrix

This is an n by n matrix A, where n is the number of verticesin the graph. If there is an edge from a vertex x to avertex y, then the element ax,y is 1 (or in general the numberof xy edges), otherwise it is 0. Every element in matrix canbe represented by a single bit. However in case of sparsegraphs it can be a waste of space and time. In computing,this matrix makes it easy to find subgraphs, and to reversea directed graph

U V W X

U 0 1 1 0

V 1 0 1 1

W 1 1 0 1

X 0 1 1 0

Adjacency List

an array indexed by vertex numbers points to a singly-linked list of the neighbors of each vertex.

In a complete graph each pair of vertices is joined by an edge, that is, the graph contains all possible edges.

In a bipartite graph, the vertices can be divided into two sets, W and X, so that every edge has one vertex in each of the two sets.

A planar graph can be drawn in a plane with no crossing edges (i.e., embedded in a plane).

Eulerian path is a path in a graph which visits each edge exactly once and returns to the starting vertex. A Graph has a Eulerian Path if all vertices have even degree.

Hamiltonian path (or traceable path) is a path in an undirectedgraph which visits each vertex exactly once. Hamiltoniancycle (or Hamiltonian circuit) is a cycle in an undirected graph whichvisits each vertex exactly once and also returns to the startingvertex.

Independent set or stable set is a set of vertices in a graph, no two ofwhich are adjacent. That is, it is a set I of vertices such that for everytwo vertices in I, there is no edge connecting the two.

A maximum independent set is a largest independent set for a givengraph G.

Maximum Independent set is { U,Z,Y}

Vertex Cover

Vertex cover of a graph is a set of vertices such that each edgeof the graph is incident to at least one vertex of the set. Avertex cover of a graph G is a set C of vertices such that eachedge of G is incident to at least one vertex in C. The set C issaid to cover the edges of G. Figure shows vertex covers in twographs (the set C is marked with red).

Minimum vertex cover is vertex cover of smallest possible size.

Clique

Clique in an undirected graph G = (V, E) is a subset ofthe vertex set such that for every two vertices in the subset,there exists an edge connecting the two.

A maximum clique is a clique of the largest possible size in agiven graph