This tutorial will help you go from beginner to advanced with “Time and Space Complexity Analysis”.

We cover indepth explanations of BigO, BigOmega, Theta and other notations
Types of recurrence relations (Linear, DivideandConquer)
How to solve any relation easily
Time and space complexity of recursive programs
The maths along with explaining it in a simple language
Comparisons of various complexities along with graphs
NPCompleteness introduction
and much more!

NOTE: If you get any root in the recurrence relation equation, just replace N as 2^k. This will now be converted to divide and conquer relation. Then solve normally using AkraBazzi formula and in the end substitute for N.

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Resources
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Timestamps:
00:00:00 Introduction
00:03:45 Example
00:06:27 Time Complexity
00:17:18 Comparing Complexities
00:23:18 Procedure for Analysing Complexity
00:37:50 BigOh Notation
00:44:38 BigOmega Notation
00:46:28 BigTheta Notation
00:49:20 LittleOh Notation
00:53:03 LittleOmega Notation
00:55:29 Space Complexity
00:58:22 Question
01:04:24 Complexity Analysis : Sorting Algorithms
01:05:57 Complexity Analysis : Recursive Programs
01:13:55 Types of Recurrence Relations
01:16:49 DivideandConquer Recurrence Relation
01:25:55 AkraBazzi Theorem
01:44:11 Linear Recurrence Relation
01:46:46 Solving Homogenous Linear Recurrence Relation
01:57:43 Q : Find nth Fibonacci Number using Golden ratio
02:03:15 Q : Solve Recurrence Relation with Repeated Roots
02:08:00 NonHomogeneous Linear Recurrence Relation
02:09:10 Solving NonHomogenous Linear Recurrence Relation
02:16:28 How to guess a Particular Solution?
02:20:34 Example
02:24:54 NPComplete Problems
02:27:20 Outro

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