Mastering Efficiency with Binary Search

In computer science, we don't measure time by seconds, we measure it by growth.

That's why we use big O notation. It's not about how fast your CPU is, it's about how much

w- extra work your algorithm does. Take the simplest example, linear search.

If you have 10 items, you check 10 times. If you have a million items, you check a million

times. That's a straight line of increasing work. Finding one specific

item in a disorganized eye means looking everywhere. In the worst case, you have looked

at every single thing. And if you have nested loops, that's...

It gets messy fast. We want to avoid that line climbing too high. Now let's talk about