[11.4] Comparing Our Algorithms скачать в хорошем качестве

[11.4] Comparing Our Algorithms

In this video, we bring together everything covered so far, step counts, growth shapes, and best/worst/average cases, to compare the search and sorting algorithms you’ve implemented this year. We revisit linear vs binary search, then compare quadratic-time sorts (insertion, selection, bubble) with merge sort’s n log n behavior. The key theme is how drastically the “winner” changes when input size grows: simple algorithms can be fine for small n, but break down completely at scale. By the end, you’ll see how to judge algorithms not by raw speed, but by how their work grows as the data grows.