Two most common reasons for believing that P is different from NP are the following:

- There are (literally) tons of problems which are NP-complete (i.e. if one is in P, and all will be in P), and nobody has been able to devise any polynomial-time algorithm for any of these problems. However, from our experience, we know that we are "good" at devising polynomial-time algorithms, but not very good at proving lower bounds. [Just look at all the great

algorithms we have devised. Also witness that we can't even prove that satisfiability for propositional logic cannot be solved in O(n^{2}).]

- We can intuitively think of P as the set of problems for which we can produce proofs in polynomial time, and of NP as the set of problems for which, given a proof, we can check that it is
*indeed*a proof in polynomial time. Well, from our experience with math, we know that producing proofs is usually much harder than verifying proofs.

Well, both of these reasons sound very reasonable. However, I also think that there are holes in these arguments (because of which, it is equally reasonable to believe that P = NP). Most people associate the complexity class P with the set of problems that we can solve "efficiently", and classify non-polynomial algorithms as bad (or inefficient) algorithms. [If you have done some computer science courses, you should see this immediately.] But this is a wrong assumption. For example, the Simplex algorithm for solving linear programming runs in exponential time in the worst case (I believe the same is true in average case), but the algorithm runs very efficiently in practice. There are also polynomial-time algorithms for linear programming, but it seems that none of those have outperformed the simplex algorithm in practice. Furthermore, the exponentiallity of the simplex algorithm's running time only occurs on some pathological examples that never appear in practice. On the other hand, there are many polynomial-time algorithms that do not actually run efficiently in practice. A counterexample for this (quoting Cook's paper) is due to a result by Robertson-Seymour that says that every minor closed family of graphs can be recognized in O(n

^{3}), but the hidden constant is so huge that the algorithm is inefficient in practice.

So, maybe there exist polynomial-time algorithms for satisfiability for propositional logic, it's just that the smallest exponents are so big (maybe bigger than the number of atoms in the visible universe, 10

^{90}). [Of course, the time hierarchy theorem tells us that there is an problem solvable in n

^{k}, but not solvable in n

^{k-1}.] Perhaps, the smallest size of the polynomial-time algorithm that solve satisfiability for propositional logic already outnumbers 10

^{90}. Who knows! Perhaps, we are good at devising efficient algorithms, but not so good at devising polynomial-time algorithms in general. Maybe the following question is more relevant: assuming that P = NP, give an upper bound on the size of the algorithm (by this I mean, multi-tape Turing Machine) that solves satisfiability in polynomial-time; does it necessarily consist 10, 100, or 10

^{90}states?

In a sense, if you're a practical-minded person, P vs. NP doesn't really matter at all, and there is technical reason why. Recall that the linear speedup theorem (look at Papadimitriou's Computational Complexity) says that if a problem is solvable in `f(n)`, then for any `epsilon > 0`, there exists an algorithm that solves the same problem which runs in `epsilon f(n) + n + 2`. So, if your algorithm runs in 2

^{n}, then just use this theorem to speed up your algorithm for all n that is relevant in practice. [Of course, if you check the proof, the size of the resulting algorithm grows quite so quickly in the size of `n`, that there is no way you can write your algorithm on earth. So, the practically-minded people will probably settle with heuristics instead.]

Anyway, I think that it is very possible [correction: 'very' -> 'equally'] that P equals NP, and the proof of which is non-constructive (or we may never actually find the proof, because the length of the shortest possible proofs exceeds the number of atoms in the visible universe).