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1. Algebraic System

An algebraic system is a non-empty set together with one or more operations (like addition or multiplication) defined on it.
Example: A set AAA with an operation ∗*∗ is written as (A,∗)(A, *)(A,∗).


2. Semigroup

A semigroup is a set SSS with a binary operation ∗*∗ that is associative:

(a∗b)∗c=a∗(b∗c),∀a,b,c∈S(a * b) * c = a * (b * c), \quad \forall a,b,c \in S(a∗b)∗c=a∗(b∗c),∀a,b,c∈S


3. Monoid

A monoid is a semigroup that has an identity element eee, such that:

a∗e=e∗a=a,∀a∈Sa * e = e * a = a, \quad \forall a \in Sa∗e=e∗a=a,∀a∈S


4. Group

A group is a monoid in which every element has an inverse.
So it satisfies:

  1. Closure
  2. Associativity
  3. Identity element
  4. Inverse element

5. Abelian Group

An Abelian group (or commutative group) is a group where the operation is commutative:

a∗b=b∗a,∀a,b∈Ga * b = b * a, \quad \forall a,b \in Ga∗b=b∗a,∀a,b∈G


6. Generation of Subgroups

If HHH is a subgroup of GGG, then generation means forming HHH from a subset A⊆GA \subseteq GA⊆G.
The subgroup generated by AAA, denoted ⟨A⟩\langle A \rangle⟨A⟩, is the smallest subgroup containing AAA.


7. Cyclic Group

A cyclic group is a group generated by a single element ggg:

G=⟨g⟩={gn∣n∈Z}G = \langle g \rangle = \{ g^n \mid n \in \mathbb{Z} \}G=⟨g⟩={gn∣n∈Z}


8. Coset

Let HHH be a subgroup of GGG and a∈Ga \in Ga∈G.

  • Left coset: aH={ah∣h∈H}aH = \{ ah \mid h \in H \}aH={ah∣h∈H}
  • Right coset: Ha={ha∣h∈H}Ha = \{ ha \mid h \in H \}Ha={ha∣h∈H}

9. Normal Subgroup

A subgroup NNN of GGG is normal if:

gN=Ng,∀g∈GgN = Ng, \quad \forall g \in GgN=Ng,∀g∈G

Denoted by N⊴GN \trianglelefteq GN⊴G


10. Ring

A ring is a set RRR with two operations (+ and ×) such that:

  • RRR is an Abelian group under addition
  • Multiplication is associative
  • Distributive laws hold:

a(b+c)=ab+ac,(a+b)c=ac+bca(b+c) = ab + ac, \quad (a+b)c = ac + bca(b+c)=ab+ac,(a+b)c=ac+bc


11. Field

A field is a ring in which:

  • Every non-zero element has a multiplicative inverse
  • Multiplication is commutative

12. Integral Domain

An integral domain is a commutative ring with:

  • No zero divisors (i.E., ab=0⇒a=0ab = 0 \Rightarrow a = 0ab=0⇒a=0 or b=0b = 0b=0)
  • Multiplicative identity


Here are clear definitions followed by the proof:


Definitions

1. Tree In Graph Theory, a tree is a connected graph with no cycles

  • It connects all its vertices

  • It has no closed loops


2. Binary Tree


A binary tree is a hierarchical data structure in which each node has at most two children, usually called:

  • left child

  • right child


3. M-ary Tree An M-ary tree (or n-ary tree) is a tree in which each node has at most M children

  • If ( M = 2 ), it becomes a binary tree

  • Used in structures like heaps and search trees


4. Spanning Tree Given a connected graph, a spanning tree is a subgraph that:

  • includes all vertices of the original graph

  • is a tree (no cycles, connected)

  • has the minimum number of edges needed to connect all vertices


5. Root In a rooted tree, the root is the topmost node from which all nodes descend

  • It has no parent

  • Every other node is reachable from it


Theorem

A tree with ( n ) vertices has exactly ( n – 1 ) edges.


Proof (by Induction)

Base Case

For ( n = 1 ):

  • A tree with one vertex has 0 edges

  • ( n – 1 = 1 – 1 = 0 ) ✔️ True


Inductive Hypothesis

Assume that any tree with ( k ) vertices has ( k – 1 ) edges.


Inductive Step

Consider a tree with ( k + 1 ) vertices.

  • A fundamental property of trees:
    Every tree has at least one leaf (a vertex with degree 1)

  • Remove one leaf and its edge:

    • Remaining graph has ( k ) vertices

    • By hypothesis → it has ( k – 1 ) edges

  • Add back the removed vertex and edge:

    • Total edges = ( (k – 1) + 1 = k )

Thus, a tree with ( k + 1 ) vertices has ( k = (k+1 – 1) ) edges.


Definitions

1. Tree In Graph Theory, a tree is a connected graph with no cycles

  • It connects all its vertices

  • It has no closed loops


2. Binary Tree


binary tree is a hierarchical data structure in which each node has at most two children, usually called:
  • left child

  • right child


3. M-ary Tree An M-ary tree (or n-ary tree) is a tree in which each node has at most M children

  • If M=2M=2, it becomes a binary tree

  • Used in structures like heaps and search trees


4. Spanning Tree Given a connected graph, a spanning tree is a subgraph that:

  • includes all vertices of the original graph

  • is a tree (no cycles, connected)

  • has the minimum number of edges needed to connect all vertices


5. Root In a rooted tree, the root is the topmost node from which all nodes descend

  • It has no parent

  • Every other node is reachable from it


Theorem

A tree with nn vertices has exactly n−1n−1 edges.


Proof (by Induction)

Base Case

For n=1n=1:

  • A tree with one vertex has 0 edges

  • n−1=1−1=0n−11−10 ✔️ True


Inductive Hypothesis

Assume that any tree with kk vertices has k−1k−1 edges.


Inductive Step

Consider a tree with k+1k+1 vertices.

  • A fundamental property of trees:
    Every tree has at least one leaf (a vertex with degree 1)

  • Remove one leaf and its edge:

    • Remaining graph has kk vertices

    • By hypothesis → it has k−1k−1 edges

  • Add back the removed vertex and edge:

    • Total edges = (k−1)+1=k(k−11=k

Thus, a tree with k+1k+1 vertices has k=(k+1−1)k=(1−1 edges.


Conclusion

By induction, a tree with nn vertices has:

n−1 edgesn−1 edges

If you want, I can also show a visual example or give an alternative proof using graph properties.

Define with example: isomorphism of graphs. Simple graph, degree of a vertex, complete graph, Directed graph , Connected graph, tree, Cyclic graph, Null graph and Strongly connected graph , Loop, Null Graph , Pendent vertex, isolated vertex , weighted graph , Path , Circuit

Here are concise definitions with examples for each term:


1. Isomorphism of Graphs

Two graphs are isomorphic if there is a one-to-one correspondence between their vertices and edges that preserves adjacency.

Example:


Graph G1G1: A—B—C
Graph G2G2: X—Y—Z
Mapping: A→X, B→Y, C→Z → same structure ⇒ isomorphic

2. Simple Graph

A graph with:

  • no loops

  • no multiple edges between the same pair of vertices

Example:


 Triangle with vertices A, B, C and edges AB, BC, CA

3. Degree of a Vertex

The degree is the number of edges incident to a vertex.

Example:


If vertex A connects to B, C, D → degree of A = 3

4. Complete Graph

A graph where every pair of vertices is connected.

Example:


For 4 vertices (called K4K4), every vertex connects to the other 3

5. Directed Graph (Digraph)

A graph where edges have a direction.

Example:


A → B, B → C (edges are arrows, not just lines)

6. Connected Graph

A graph in which there is a path between every pair of vertices.

Example:
A—B—C—D is connected


7. Tree

connected graph with no cycles.

Example:
A—B, A—C, B—D (no loops, fully connected)


8. Cyclic Graph

A graph that contains at least one cycle.

Example:
A—B—C—A (forms a loop)


9. Null Graph

A graph with vertices but no edges.

Example:
Vertices: A, B, C but no connections


10. Strongly Connected Graph

(For directed graphs)
A graph is strongly connected if every vertex is reachable from every other vertex following direction.

Example:


A → B → C → A (you can reach any vertex from any other)

11. Loop

An edge that connects a vertex to itself.

Example:
A → A


12. Pendent Vertex (Leaf)

A vertex with degree 1.

Example:
In A—B—C, vertices A and C are pendent


13. Isolated Vertex

A vertex with degree 0 (no edges).

Example:


Vertex D in a graph where A—B—C are connected but D is separate

14. Weighted Graph

A graph where edges have weights (costs, distances, etc.).

Example:


A—B (weight 5), B—C (weight 3)

15. Path

A sequence of vertices where each adjacent pair is connected by an edge.

Example:


A → B → C → D

16. Circuit (Cycle)

A path that starts and ends at the same vertex with no repeated edges.

Example:


A → B → C → A