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631,510 Views • Aug 14, 2024 • Click to toggle off description
#tech #webdevelopment #bigonotation
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484 Comments
Top Comments of this video!! :3
Manager in job Interview: optimize this linear function to be logarithmic.
Manager after hire: just tell ops to upgrade our EC2 instances.
2.1K |
Stalinsort is the best sorting algorithm. Any element not already in order is removed.
1.2K |
An important note of Big O, it doesn't indicate speed, but rather scaling. This means that better Big O assures better performance at a large scale, but at smaller amounts of data an algorithm with worse Big O may perform better.
610 |
Fireship feeling that AI pressure.
924 |
The Big O released back in 1999, or the EN dub in 2001. It's only 26 episodes so it's really easy to binge. Every job hunting programmer can easily watch it to ensure they have a very thorough understanding of Big O.
68 |
Candidate: "Let me explain my cloud architecture experience."
Interviewer: "Just stfu and do the fizzbuzz."
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Manager: Please explain the O notation.
Me: Do you have a deck of cards?
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Big O is useful if you are being academic or generic, but in many real world cases you can get significantly better performance if you understand your data.
For example, if you have a deck of cards that isn't sorted, you can easily sort it in linear time by doing the following:
1. Allocate your sorted array with 52 elements
2. Loop over the unsorted deck and calculate an index based on the suit and number (13*suit ID + number - 1) and put the card in that spot in the result sorted array
Always look at the data and problem you have and solve for that. If your problem is sorting decks of cards then solve that problem and don't use a generic method like quick sort or merge sort.
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Sorting something random is at the same time the fastest and slowest method.
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I've been a dev for almost a decade and never once needed to know this, if I was asked in a job interview (and I never have been) I would just shrug.
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Excuse me, but O(n!) is definitely not the worst possible time complexity 😀 although, realistically, if something is worse, it's of theoretical interest at mos
29 |
If the deck is sorted no need to binary search. Each card can be accessed directly by its index derived from color and value of the card
16 |
52 pickup is actually an average time O(2**n) algorithm with no upper bound TC because each time you are randomly choosing a permutation (meaning you could cycle through invalid permutations forever)
17 |
Big O.... hmmm sounds like a good name for a tire company.
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You could use the fact that a deck of cards only has a few ranks of them. You'd only need to loop the deck once. Give a number to each one. Then have a hashmap, translating card names to these indexes you have. And use an array of integers, where the index tells you, how many of that card are in the stack. As you loop through the stack, looking at the cards, for each one, you lookup it in the array (using the index it has), in O(1), and incrementing its count. After you're done, you simply walk the array of counts once, and output the rank of each card as many times as the number tells you. Boom, sorted stack, linear time
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I was a developer for over 10 years and couldn't understand big O - this made it super easy to understand!
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“Big O!
SHOW-TIME!”
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or you can save the most ammount of time by googling a sorting library and praying that the guy that made it knew what they were doing
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What’s crazy is no matter how many times I learn big O notation I always forget it a few weeks later. Never sticks. It’s been over a year and I have to keep relearning it just cuz it does not stick.
3 |
@dale3478
3 months ago
In 1 universe out of the countless possible universes in multiverse, bogosort can perfectly sort data in O(1) time
3.7K |