Understanding the Time Complexity of an Algorithm

Algorithms: Understanding the Time Complexity of an Algorithm
Topics discussed:
1. A Recap of Priori vs. Posteriori Analysis.
2. CPU Computations and Main Memory Space.
3. The Time Complexity of an Algorithm.
Algorithm Playlist: • Design and Analysis of...
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Music:
Axol x Alex Skrindo - You [NCS Release]
#AlgorithmsByNeso #Algorithms #TimeComplexity

[@Dr_Larken]

The algorithm brought me here!

[@TanvirAhmedOriginal]

Thank you Neso Academy, for making our life easier.

[@diwanshsawlani7028]

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

[@Think_First12]

Thank you for sharing in depth knowledge 🙏

[@akashtiwari397]

Plz bring series of gate pyq year wise subject wise

[@salsalazar7646]

nice video well done amazing job

[@Ehvan1729]

one thing i could say is just thanks i think that is very small one

[@yazidbessadet]

so much AURA i'm feeling right now cant lie LOL. NESO are the best

[@sujitsingh5862]

better than previous

[@Gust52]

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 🙏

[@Delj18]

For increment what I'd the time complexity

[@eghanshith5346]

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

[@mohamad.d1258]

Well that's my problem.

[@TanvirAhmedOriginal]

I don't understand why not O(4n+4)

[@shahidiqbal512]

I also asking same question.

[@nicolasbarone2176]

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 :)

[@TanvirAhmedOriginal]

@@nicolasbarone2176 thanks

[@footworld2335]

wow

[@AshvinParmar-ov2dv]

2 minute video took my 24 minutes

[@AbdullahBRashid]

Fr. Dogshit video. Absolutely zero info on how to calculate more complex algorithms. Not worth 24 mins