P versus NP Clarification

Question

Quoted from wikipedia, the P vs NP problem, regarding the time complexity of algorithms "... asks whether every problem whose solution can be quickly verified by a computer can also be quickly solved by a computer."

I am hoping that somebody can clarify what the difference between "verifying the problem" and "solving the problem" is here.

Answer 1

I am hoping that somebody can clarify what the difference between "verifying the problem" and "solving the problem" is here.

It's not "verifying the problem", but "verifying the solution". For example you can check in polynomial time whether a given set is valid for SAT. The actual generation of such set is NP hard. The section Verifier-based definition in the Wikipedia article NP (complexity) could help you a little bit:

Verifier-based definition

In order to explain the verifier-based definition of NP, let us consider the subset sum problem: Assume that we are given some integers, such as {−7, −3, −2, 5, 8}, and we wish to know whether some of these integers sum up to zero. In this example, the answer is "yes", since the subset of integers {−3, −2, 5} corresponds to the sum (−3) + (−2) + 5 = 0. The task of deciding whether such a subset with sum zero exists is called the subset sum problem.

As the number of integers that we feed into the algorithm becomes larger, the number of subsets grows exponentially, and in fact the subset sum problem is NP-complete. However, notice that, if we are given a particular subset (often called a certificate), we can easily check or verify whether the subset sum is zero, by just summing up the integers of the subset. So if the sum is indeed zero, that particular subset is the proof or witness for the fact that the answer is "yes". An algorithm that verifies whether a given subset has sum zero is called verifier. A problem is said to be in NP if and only if there exists a verifier for the problem that executes in polynomial time. In case of the subset sum problem, the verifier needs only polynomial time, for which reason the subset sum problem is in NP.

If you're more into graph theory the Hamilton cycle is a NP-complete problem. It's trivial to check whether a given solution is a Hamilton cycle (linear complexity, traverse the solution path), but if P != NP than there exists no algorithm with polynomial runtime which solves the problem.

Maybe the term "quick" is misleading in this context. An algorithm is quick in this regard if and only if it's worst-case runtime is bounded by a polynomial function, such as O(n) or O(n log n). The creation of all permutations for a given range with the length n is not bounded, as you have n! different permutations. This means that the problem could be solved in n ¹⁰⁰ log n, which will take a very long time, but this is still considered fast. On the other hand, one of the first algorithms for TSP was O(n!) and another one was O(n² 2ⁿ). And compared to polynomial functions these things grow really, really fast.