**Deep Dive into Algorithms**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 32.5 Hours | 11.5 GB

Deep Dive into Algorithms

An algorithmic paradigm or algorithm design paradigm is a generic model or framework which underlies the design of a class of algorithms. An algorithmic paradigm is an abstraction higher than the notion of an algorithm, just as an algorithm is an abstraction higher than a computer program.

- How does one calculate the running time of an algorithm?
- How can we compare two different algorithms?
- How do we know if an algorithm is ‘optimal’?

What you’ll learn

- Students will learn various Backtracking Problems along with implementation using C language
- Students will learn various Dynamic Programming Problems along with implementation using C language
- Students will learn various Graph Algorithms along with implementation using C language
- Branch and Bound
- Divide and Conquer
- Greedy Algorithm
- Pattern Matching
- Searching and Sorting

**Table of Contents**

**Backtracking**

1 Introduction

2 Concept of N Queen Problem

3 Implementation of N Queen Problem

4 Time Complexity Analysis of N Queen Problem

5 Concept of Knight’s Tour Problem

6 Implementation of Knight’s Tour Problem

7 Time Complexity Analysis of Knight’s Tour Problem

8 Concept Explanation of Rat in a Maze Problem

9 Implementation of Rat in a Maze

10 Time Complexity Analysis of Rat in a Maze

11 Concept Explanation of Subset Sum

12 Implementation of Subset Sum Problem

13 Time and Space Complexity Analysis of Subset Sum Problem

14 Concept Explanation of M-Coloring Problem

15 Implementation of M Coloring Problem

16 Time and Space Complexity Analysis of M Coloring Problem

17 Concept Explanation of Hamiltonian Cycle Problem

18 Implementation of Hamiltonian Cycle

19 Time and Space Complexity Analysis of Hamiltonian Cycle

20 Concept Explanation of Sudoku Solver

21 Implementation of Sudoku Solver

22 Time and Space Complexity Analysis of Sudoku Solver

23 Sieve of Eratosthene

24 Implementation of Sieve of Eratosthene

25 Concept Explanation of Sieve of Sundaram

26 Implementation of Sieve of Sundaram

27 Time and Space Complexity Analysis of Sieve of Eratosthene and Sieve of Sundaram

28 Sieve of Eratosthene in O(N) Time Complexity

29 Implementation of Sieve of Eratosthene in O(N) Time Complexity

30 Prime Numbers after P with Sum S

31 Implementation of Prime Numbers after P with Sum S

32 Time and Space Complexity Analysis of Prime Numbers after P with Sum S

**Dynamic Programming**

33 Introduction to Dynamic Programming – Part 1

34 Introduction to Dynamic Programming – Part 2

35 Kanpsack Problem

36 Implementation of 01 Knapsack Problem

37 Printing items in 01 Knapsack Problem

38 Implementation of printing items in 01 Knapsack Problem

39 Minimum Cost Path

40 Implementation of Minimum Cost Path

41 Tracing the path of Minimum Cost Path

42 Implementation of Tracing the Path of Minimum Cost Path

43 Subset Sum Problem

44 Implementation of Subset Sum Problem

45 Printing items in Subset Sum Problem

46 Implementation of printing items in Subset Sum

47 Maximum Size Square Sub Matrix with all 1s

48 Implementation of Maximum Size Square Sub Matrix with all 1s

49 Longest Increasing Subsequence

50 Implementation of Longest Increasing Subsequence

51 Printing items in Longest Increasing Subsequence

52 Implementation of Printing items in Longest Increasing Subsequence

53 Longest Common Subsequence

54 Implementation of Longest Common Subsequence

55 Tracing the String in Longest Common Subsequence

56 Implementation of Tracing the String in Longest Common Subsequence

**Range Query Algorithms**

57 Introduction and Brute Force Approach of Range Minimum Query

58 Implementation of Range Minimum Query ( Brute Force Approach )

59 Dynamic Programming Approach for Range Minimum Query

60 Implementation of Range Minimum Query ( Dynamic Programming Approach )

61 Introduction to Segment Tree

62 Constructing Segment Tree

63 Implementation of Constructing Segment Tree

64 Range Minimum Query on the Constructed Segment Tree

65 Implementation of Range Minimum Query on Constructed Segment Tree

66 Range Minimum Query Using Sparse Table

67 Performing RMQ on Constructed Sparse Table

68 How to efficiently fill Sparse Table

69 Implementation of RMQ using Sparse Table

**Graph Algorithms**

70 How to represent a adjacency list for an directed and undirected graph

71 Implementation of Adjacency List for Directed and Undirected Graph

72 HierHolzer’s Algorithm

73 Implementation of HierHolzer’s Algorithm

74 Union Find Algorithm

75 Implementation of Union Find Algorithm

76 Topological Sorting

77 Dijkstra’s Algorithm

78 Bellman Ford Algorithm

79 Ford Fulkerson Method Edmond Karg Maximum Flow Algorithm

80 Kargers Algorithm for Minimum Cut

81 Kruskal’s Algorithm for Minimum Spanning Tree

82 Prim’s Algorithm for Minimum Spanning Tree

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