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Chapter 1

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Algorithm

Instruction:- Set of operations

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Problem for finding GCD

First Method

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Second method

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Third Method

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Method for finding Prime Number

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Fundamentals of algorithmicproblem solving

Understanding the problem Ascertaining the capabilities of a computationaldevice. Choosing between exact and approximateproblem solving. Deciding an appropriate Data Structure Algorithm design techniques.

Methods of specifying an algorithm. Proving an algorithms correctness Analyzing an algorithm. Coding an algorithm

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Understand the given problem completely. Read the problem description carefully Ask doubts about the problem Do few examples Think about special cases like boundary value Know about the already known algorithms for solving that

problem

Clear about the input to an algorithm.

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Step 1- Understanding the problem

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Sequential Algorithms: Instructions areexecuted one after another and one operationat a time. Algorithms designed to be executed

on such machine are called sequentialalgorithms

Parallel Algorithms: Modern computers that

can execute instructions in parallel. Algorithmsdesigned to be executed on such machine arecalled parallel algorithms

Step 2- Ascertaining the Capabilities ofa computational device

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Exact Algorithm: Solving problem exactly Approx. Algm. : Solving problem

approximately Why Approx. Algm.? Some problems cannot be solved exactly

Solving problem exactly can be unacceptablyslow because of the problem complexity.

Step 3- Choosing between exact andapproximate problem solving

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A Data structure is defined as a particularscheme of organizing related data items.

Decide on the suitable data structure

Step 4- Deciding on appropriate DataStructures

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An algorithm design technique is a general approach tosolve problems algorithmically.The various design techniques are:Brute force

Divide and conquerDecrease and conquerTransform and conquerGreedy approachDynamic programmingBacktracking and Branch and boundSpace and time tradeoffs

Step 5- Algorithm Design techniques

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There are 3 methods: Natural language

Easy but ambiguous Pseudo code

Mixture of programming language and naturallanguage

Flow chart It is pictorial representation of the algorithm. Here symbols are used.

Step 6- Method of specifying analgorithm

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Correctness: Prove that the algm. Yields the requiredresult for every legitimate input in a finite amount oftime.A common technique for proving correctness is to use

mathematical inductionIn order to show the algm. Is incorrect, we have to take

just one instance of its input for which the algm. Fails.If a algm. is found to be incorrect, then reconsider oneor more those decisionsFor approx. algms. We should show that the errorproduced by the algm does not exceed the predefinedlimit.

Step 7- Proving an algorithmscorrectness

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Analyze the following qualities of the algorithmEfficiency:

Time efficiency and Space efficiency

Simplicity Simple algorithms mean easy to understand and easy to

program.

Generality Design an algm. for a problem in more general terms. In

some cases, designing more general algm. Isunnecessary or difficult.

OptimalityThe algorithm should produce optimal results.

Step 8- Analyzing an algorithm

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Algorithms are designed to be implementedas computer programs.

Use code tuning technique. For eg., replacingcostlier multiplication operation with cheaperaddition operation.

Step 9- Coding an algorithm

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Fundamental data structures

list array

linked list

string

stack

queue priority queue/heap

graphtree and binary tree

set and dictionary

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Linear Data Structures

Arrays A sequence of n items of the same

data type that are stored contiguouslyin computer memory and madeaccessible by specifying a value of thearrays index.

Linked List A sequence of zero or more nodes

each containing two kinds ofinformation: some data and one ormore links called pointers to othernodes of the linked list.

Singly linked list (next pointer) Doubly linked list (next + previous

pointers)

Arraysfixed length (need preliminaryreservation of memory)contiguous memory locations

direct accessInsert/deleteLinked Lists

dynamic lengtharbitrary memory locations

access by following linksInsert/delete

a1 ana2 .

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Stacks and Queues

Stacks A stack of plates

insertion/deletion can be done only at the top. LIFO

Two operations (push and pop) Queues

A queue of customers waiting for services Insertion/enqueue from the rear and deletion/dequeue from the

front. FIFO

Two operations (enqueue and dequeue)

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Priority Queue and Heap

Priority queues (implemented using heaps ) A data structure for maintaining a set of elements, each associated

with a key/priority, with the following operations

Finding the element with the highest priority Deleting the element with the highest priority

Inserting a new element Scheduling jobs on a shared computer

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Graphs Formal definition

A graph G = is defined by a pair of two sets: a finiteset V of items called vertices and a set E of vertex pairscalled edges .

Undirected and directed graphs ( digraphs ). maximum number of edges in an undirected

graph with |V| vertices Complete graphs

A graph with every pair of its vertices connected by anedge is called complete, K |V|

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Graph Representation Adjacency matrix

n x n boolean matrix if |V| is n. The element on the ith row and jth column is 1 if

theres an edge from ith vertex to the jth vertex;otherwise 0.

The adjacency matrix of an undirected graph issymmetric. Adjacency linked lists

A collection of linked lists, one for each vertex, thatcontain all the vertices adjacent to the lists vertex.

Which data structure would you use if the graph is a 100-node starshape?

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Weighted Graphs

Weighted graphs Graphs or digraphs with numbers assigned to the edges.

- A dense graph is a graph in which the numberof edges is close to the maximal number ofedges. The opposite, a graph with only a fewedges, is a sparse graph .

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G h P ti P th d C ti it

https://en.wikipedia.org/wiki/Graph_(mathematics)https://en.wikipedia.org/wiki/Graph_(mathematics)https://en.wikipedia.org/wiki/Graph_(mathematics)

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Graph Properties -- Paths and Connectivity

Paths

A path from vertex u to v of a graph G is defined as asequence of adjacent (connected by an edge) verticesthat starts with u and ends with v.

Simple paths : All edges of a path are distinct. Path lengths: the number of edges, or the number of

vertices 1. Connected graphs

A graph is said to be connected if for every pair of itsvertices u and v there is a path from u to v.

Connected component The maximum connected subgraph of a given graph.

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Trees Trees

A tree (or free tree ) is a connected acyclic graph. Forest: a graph that has no cycles but is not

necessarily connected. Properties of trees

- For every two vertices in a tree there always existsexactly one simple path from one of these verticesto the other.

Rooted trees : The above property makes it possible to select an

arbitrary vertex in a free tree and consider it as the root of the socalled rooted tree. Levels in a rooted tree.

|E| = |V| - 1

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Rooted Trees (I) Ancestors

For any vertex v in a tree T , all the vertices on thesimple path from the root to that vertex are calledancestors.

Descendants All the vertices for which a vertex v is an ancestor

are said to be descendants of v . Parent, child and siblings

If (u, v) is the last edge of the simple path from theroot to vertex v , u is said to be the parent of v and v is called a child of u .

Vertices that have the same parent are calledsiblings.

Leaves A vertex without children is called a leaf.

Subtree A vertex v with all its descendants is called the

subtree of T rooted at v .

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Rooted Trees (II)

Depth of a vertex The length of the simple path from the root to the vertex.

Height of a tree The length of the longest simple path from the root to a leaf.

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h = 2

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Ordered Trees Ordered trees

An ordered tree is a rooted tree in which all the children ofeach vertex are ordered. Binary trees

A binary tree is an ordered tree in which every vertex has nomore than two children and each children is designated s

either a left child or a right child of its parent. Binary search trees Each vertex is assigned a number. A number assigned to each parental vertex is larger than all

the numbers in its left subtree and smaller than all the

numbers in its right subtree. log2n h n 1, where h is the height of a binary tree and n the size.

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