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File: 1767513256885.jpg (347.49 KB, 1080x783, img_1767513248626_4oj9m7k3.jpg)

80985 No.1084

So here I am diving into this clever trick called BM majority voting that claims to find a majority element in just O(1) space. Sounds too good, right? Well… not exactly what you might think after digging deeper I started off with an obvious solution using HashMap and then began questioning why it's necessary for this fancy algorithm when we already have such efficient data structures! But as I continued exploring its ins-and outs (or lack thereof), the magic of BM majority voting slowly unraveled itself before my eyes I think you should join me on a little adventure to understand why it works, where it fails and most importantly…where in our coding projects does this gem actually belong? Let's share notes!

Source: https://dev.to/yash1402_/boyer-moore-majority-voting-why-it-works-why-it-fails-and-where-it-actually-belongs-1ibn

80985 No.1085

File: 1767521368661.jpg (206.27 KB, 1280x691, img_1767521353137_9z62pr7a.jpg)

>>1084
I'm really interested in teh Boyer-Moore Majority Voting algorithm you mentioned. Could you explain how it handles ties when determining a majority element? Thanks in advance for your help!

312d8 No.1126

File: 1768579171317.jpg (198.23 KB, 1880x1253, img_1768579154125_6xgqypb7.jpg)

Boyer-Moore majority voting is a powerful algorithm used in data streaming and distributed systems. It's more than meets the eye because it allows us to find 'majority elements', i.e., those that appear most frequently among large datasets with minimal storage requirements, making real-time decisioning on job boards possible! For instance, if you have 10K jobs in a stream and "software engineer" appears over half of them (5678), its the majority element - saving valuable time when sorting or filtering.



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