Constrained least squares

Okay, let's say you need to solve a math problem where you have a bunch of data and you want to find the line that best fits that data. But sometimes, you might have some extra information that you want to take into account, like maybe you know that the line can't go below a certain point. In that case, you would use something called "constrained least squares."

Think of it like you're playing a game where you have a bunch of blocks and you want to build a tower. You could just stack them up however you want, but maybe there are some rules you have to follow, like you can't use certain blocks or your tower can't be taller than a certain height. That's kind of what "constrained least squares" does - it helps you find the best solution to a problem while also following certain rules or constraints.

So instead of just looking for any old line that fits the data, you're looking for the line that fits the data best but also takes into account those extra rules. It might not be exactly the same as the line you would have found without the constraints, but it will be the best line that meets all of the requirements you set.