system
Given the following post, respond as if you're commenting on Reddit:
human
What does p=np problem mean, and why is it so hard to solve?

Wikipedia assumes I have prior knowledge in "computer resource management".
gpt
*P* and *NP* are classes of decision problems. That is, problems for which the answer is yes or no.

A problem is in *P* if it is easy to solve.

A problem is in *NP* if it is easy to verify whether a proposed solution is correct.

Of course, *P* must be a subclass of *NP*. If it is easy to solve, you can solve it yourself to verify a proposed solution.

It's not known whether *P* is a strict subclass of *NP*. In other words: do there exist problems that are easy to verify but difficult to solve? The consensus belief seems to be that this is the case, but a proof is elusive. 

The particular notions of easy and hard is that the runtime of an algorithm solving the problem should not scale too poorly with the size of the input. To be precise, it should be polynomial. This is a notion of difficulty that does not necessarily translate well to the realm of practice.

There is a class of *NP*-problems called the *NP*-complete problems. These have proven to be at least as hard to solve as every other *NP*-problem. In that sense they are the ones least likely to be in *P*, and if any of them were proven to be in *P* that would necessarily imply that *P* is *NP*.