DSA - Abdul Bari

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Currently Playing: 2.3.2 Recurrence Relation Dividing [ T(n)=T(n/2)+ n]. #2

Recurrence Relation for Dividing Functions Example : T(n)= T(n/2) + n solved using Recursion Tree and Back Substitution Method Courses on Udemy ================ Java Programming https://www.udemy.com/course/java-se-programming/?referralCode=C71BADEAA4E7332D62B6 Data Structures using C and C++ https://www.udemy.com/course/datastructurescncpp/?referralCode=BD2EF8E61A98AB5E011D C++ Programming https://www.udemy.com/course/cpp-deep-dive/?referralCode=E4246A516919D7E84225

Tracks in this Playlist

1. Introduction to Algorithms
1.1 Priori Analysis and Posteriori Testing
1.2 Characteristics of Algorithm
1.3 How Write and Analyze Algorithm
1.4 Frequency Count Method
1.5.1 Time Complexity #1
1.5.2 Time Complexity Example #2
1.5.3 Time Complexity of While and if #3
1.6 Classes of functions
1.7 Compare Class of Functions
1.8.1 Asymptotic Notations Big Oh - Omega - Theta #1
1.8.2 Asymptotic Notations - Big Oh - Omega - Theta #2
1.9 Properties of Asymptotic Notations
1.10.1 Comparison of Functions #1
1.10.2 Comparison of Functions #2
1.11 Best Worst and Average Case Analysis
1.12 Disjoint Sets Data Structure - Weighted Union and Collapsing Find
2 Divide And Conquer
2.1.1 Recurrence Relation (T(n)= T(n-1) + 1) #1
2.1.2 Recurrence Relation (T(n)= T(n-1) + n) #2
2.1.3 Recurrence Relation (T(n)= T(n-1) + log n) #3
2.1.4 Recurrence Relation T(n)=2 T(n-1)+1 #4
2.2 Masters Theorem Decreasing Function
2.3.1 Recurrence Relation Dividing Function T(n)=T(n/2)+1 #1
2.3.2 Recurrence Relation Dividing [ T(n)=T(n/2)+ n]. #2
2.3.3 Recurrence Relation [ T(n)= 2T(n/2) +n] #3
2.4.1 Masters Theorem in Algorithms for Dividing Function #1
2.4.2 Examples for Master Theorem #2
2.5 Root function (Recurrence Relation)
2.6.1 Binary Search Iterative Method
2.6.2 Binary Search Recursive Method
2.6.3 Heap - Heap Sort - Heapify - Priority Queues
2.7.1 Two Way MergeSort - Iterative method
2.7.2. Merge Sort Algorithm
2.7.3 MergeSort in-depth Analysis
2.8.1 QuickSort Algorithm
2.8.2 QuickSort Analysis
2.9 Strassens Matrix Multiplication
3. Greedy Method - Introduction
3.1 Knapsack Problem - Greedy Method
3.2 Job Sequencing with Deadlines - Greedy Method
3.3 Optimal Merge Pattern - Greedy Method
3.4 Huffman Coding - Greedy Method
3.5 Prims and Kruskals Algorithms - Greedy Method
3.6 Dijkstra Algorithm - Single Source Shortest Path - Greedy Method
4 Principle of Optimality - Dynamic Programming introduction
4.1 MultiStage Graph - Dynamic Programming
4.1.1 MultiStage Graph (Program) - Dynamic Programming
4.2 All Pairs Shortest Path (Floyd-Warshall) - Dynamic Programming
4.3 Matrix Chain Multiplication - Dynamic Programming
[New] Matrix Chain Multiplication using Dynamic Programming Formula
4.3.1 Matrix Chain Multiplication (Program) - Dynamic Programming
4.4 Bellman Ford Algorithm - Single Source Shortest Path - Dynamic Programming
4.5 0/1 Knapsack - Two Methods - Dynamic Programming
4.5.1 0/1 Knapsack Problem (Program) - Dynamic Programming
4.6 Optimal Binary Search Tree (Successful Search Only) - Dynamic Programming
4.6.2 [New] Optimal Binary Search Tree Successful and Unsuccessful Probability - Dynamic Programming
4.7 [New] Traveling Salesman Problem - Dynamic Programming using Formula
4.8 Reliability Design - Dynamic Programming
4.9 Longest Common Subsequence (LCS) - Recursion and Dynamic Programming
5.1 Graph Traversals - BFS & DFS -Breadth First Search and Depth First Search
5.2 Articulation Point and Biconnected Components
6 Introduction to Backtracking - Brute Force Approach
6.1 N Queens Problem using Backtracking
6.2 Sum Of Subsets Problem - Backtracking
6.3 Graph Coloring Problem - Backtracking
6.4 Hamiltonian Cycle - Backtracking
7 Branch and Bound Introduction
7.1 Job Sequencing with Deadline - Branch and Bound
7.2 0/1 Knapsack using Branch and Bound
7.3 Traveling Salesman Problem - Branch and Bound
8. NP-Hard and NP-Complete Problems
8.1 NP-Hard Graph Problem - Clique Decision Problem
9.1 Knuth-Morris-Pratt KMP String Matching Algorithm
9.2 Rabin-Karp String Matching Algorithm
10.1 AVL Tree - Insertion and Rotations
10.2 B Trees and B+ Trees. How they are useful in Databases
Asymptotic Notations - Simplified
Hashing Technique - Simplified
Shortest Path Algorithms (Dijkstra and Bellman-Ford) - Simplified
BFS DFS - Simplified
Tower of Hanoi Problem - Made Easy
Row-Major and Column-Major Mapping
Merge Sort Algorithm - Hindi
[New] Matrix Chain Multiplication using Dynamic Programming Formula
4.6.2 [New] Optimal Binary Search Tree Successful and Unsuccessful Probability - Dynamic Programming
Bresenham's Line Drawing Algorithm
2.6.3 Heap - Heap Sort - Heapify - Priority Queues
4.9 Longest Common Subsequence (LCS) - Recursion and Dynamic Programming
7.3 Traveling Salesman Problem - Branch and Bound
DDA Line Drawing Algorithm - Computer Graphics
4.8 Reliability Design - Dynamic Programming
8.1 NP-Hard Graph Problem - Clique Decision Problem
6.4 Hamiltonian Cycle - Backtracking
1.12 Disjoint Sets Data Structure - Weighted Union and Collapsing Find
4.7 [New] Traveling Salesman Problem - Dynamic Programming using Formula
6. Everything about Variables in JAVA
9.2 Rabin-Karp String Matching Algorithm
10.2 B Trees and B+ Trees. How they are useful in Databases
5. Everything about JAVA Data Types