00:08 Understanding priori vs posterior analysis of time and space complexity 03:16 Understanding Priori Analysis for Algorithm Time Complexity 06:26 Time complexity is the estimation of total CPU computations required to execute an algorithm. 09:34 Analyzing the time complexity of a simple algorithm with frequency count method 12:32 Understanding time complexity of algorithm instructions 15:38 The frequency count of the instruction is n + 1. 18:24 The time complexity of the algorithm depends on the number of times certain instructions are executed. 21:10 Understanding time complexity of an algorithm 23:47 Time complexity of algorithm is Big O
1.Priori vs. Posterior Analysis Recap: Priori analysis estimates time and memory space before executing an algorithm. Posterior analysis calculates them after execution. 2.CPU Computations and Main Memory Space: CPU computations refer to tasks executed by the CPU (instructions). Main memory space stores data and instructions for quick access during execution. 3.Time Complexity Estimation: Use the frequency count method. Calculate the sum of frequency counts for each instruction. Example: sum = 0 (1 unit), for (i = 1; i
Although in the end, the main answer we're looking for is O(n)... just wondering about the sum = sum + a[i] part. Isn't it supposed to be 1? Because it's a one liner... I'm thinking in terms if we assign values to variables, say .... int a = 0, j = 9;
Sir can you pls bring a course on networking? Like the many topics covered in ccna networking exam pls. It will add a depth of knowledge tobus and prepare us for that exam 🙏
Big O notation represents the sets of functions, which have to be above the original function, by a given constant c and a given value n0. In mathematical way: O(g(n)) = { f(n) | Exist two costant c > 0 and n0 > 0 : 0 = 4n + 1 by a given n0. For example if n0 = 20, the inequality will be: c * 20 >= 4 * 20 + 1; so, to make this inequality true, we can use c = 5. You can use any number that you want for the demonstration. If you were questioning if you could just write O(4n + 4) the answer is yes! But it's not as formal as writing O(n). I hope that is helpful :)
Algorithms Playlist: ruclips.net/p/PLBlnK6fEyqRhoF3cPp0mgOZPuXeu84nAd
00:08 Understanding priori vs posterior analysis of time and space complexity
03:16 Understanding Priori Analysis for Algorithm Time Complexity
06:26 Time complexity is the estimation of total CPU computations required to execute an algorithm.
09:34 Analyzing the time complexity of a simple algorithm with frequency count method
12:32 Understanding time complexity of algorithm instructions
15:38 The frequency count of the instruction is n + 1.
18:24 The time complexity of the algorithm depends on the number of times certain instructions are executed.
21:10 Understanding time complexity of an algorithm
23:47 Time complexity of algorithm is Big O
The algorithm brought me here!
Thank you Neso Academy, for making our life easier.
Thanks you..this video has helped me...need more videos on DSA topic
Thank you for sharing in depth knowledge 🙏
Clear pronunciation.Good video.
so much AURA i'm feeling right now cant lie LOL. NESO are the best
nice video well done amazing job
Plz bring series of gate pyq year wise subject wise
better than previous
1.Priori vs. Posterior Analysis Recap:
Priori analysis estimates time and memory space before executing an algorithm.
Posterior analysis calculates them after execution.
2.CPU Computations and Main Memory Space:
CPU computations refer to tasks executed by the CPU (instructions).
Main memory space stores data and instructions for quick access during execution.
3.Time Complexity Estimation:
Use the frequency count method.
Calculate the sum of frequency counts for each instruction.
Example: sum = 0 (1 unit), for (i = 1; i
very helpful
Although in the end, the main answer we're looking for is O(n)... just wondering about the sum = sum + a[i] part. Isn't it supposed to be 1? Because it's a one liner... I'm thinking in terms if we assign values to variables, say .... int a = 0, j = 9;
NOt really because it involves two operations, addition(1 task) and assigning value(1 task)...this continues till n times
one thing i could say is just thanks
i think that is very small one
any slides or notes available?
For increment what I'd the time complexity
2 minute video took my 24 minutes
Fr. Dogshit video. Absolutely zero info on how to calculate more complex algorithms. Not worth 24 mins
Sir can you pls bring a course on networking? Like the many topics covered in ccna networking exam pls. It will add a depth of knowledge tobus and prepare us for that exam 🙏
Well that's my problem.
I don't understand why not O(4n+4)
I also asking same question.
Big O notation represents the sets of functions, which have to be above the original function, by a given constant c and a given value n0.
In mathematical way: O(g(n)) = { f(n) | Exist two costant c > 0 and n0 > 0 : 0 = 4n + 1 by a given n0. For example if n0 = 20, the inequality will be: c * 20 >= 4 * 20 + 1; so, to make this inequality true, we can use c = 5. You can use any number that you want for the demonstration.
If you were questioning if you could just write O(4n + 4) the answer is yes! But it's not as formal as writing O(n).
I hope that is helpful :)
@@nicolasbarone2176 thanks
how 4n+4 became O(n)?
constants are ignored
wow