Скачать или смотреть 1.22 Recurrence Relation(Dividing Function)-part 2 | T(n)=9T(n/3)+n^2 | T(n)=2T(n/2)+n log n

In this video, I walk through three important recurrence relations commonly seen in divide-and-conquer algorithms. Each example is solved step-by-step using either the back substitution method or the recursion tree method, making it easier for you to understand the logic behind time complexity analysis.

✅ What’s Covered:
T(n) = 9T(n/3) + n²
🔹 Solved using Back Substitution
🔹 Demonstrates how the recursive term dominates growth.

T(n) = 2T(n/2) + n log n
🔹 Solved using the Recursion Tree Method
🔹 Highlights how log-linear additive terms influence total cost.

T(n) = 2T(n/2) + n / log n
🔹 Solved using Back Substitution
🔹 A unique case with a sublinear additive term.

This video is ideal for students, programmers, or anyone preparing for technical interviews or computer science exams. By the end, you'll have a stronger understanding of how to approach and solve recurrence relations.
Data Structure And Algorithm Course playlist link-   • DATA STRUCTURE AND ALGORITHM COURSE IN HINDI  

📌 Subscribe for more algorithm tutorials, and comment below if you want me to cover other examples!

#RecurrenceRelations #AlgorithmAnalysis #BackSubstitution #RecursionTree #DivideAndConquer #TimeComplexity #MasterTheorem
#DSATutorial
#ComputerScience
#TechnicalInterviewPrep
#CodingInterview
#BigONotation
#RecurrenceExamples
#SolveRecurrence
#AlgorithmsForBeginners
#ProgrammingTutorial
#DataStructuresAndAlgorithms