I have given the following answers for years; however, I post this and will use your comments as a sanity check. In fact, I suspect I am wrong on some points.

1) IF SAT was in P then something very clever was done. Even if its n^{100} it was very clever. That cleverness will almost surely result in other better algorithms. They may only be better in practice by not in theory (it works in practice- if only we can get it to work in theory :-) ), but it will still work and be a great advance.

2) IF SAT was in P then perhaps we could USE SAT in P to get a better algorithm for SAT in P. This was the theme of a chapter in Lance Fortnow's book The Golden Ticket: P, NP. and the search for the impossible and is also likely behind something I once heard (I think it was from Scott but I can't find it on his blog)

*If P=NP then math would be easy and we would have already found a proof that P=NP. Hence P ≠ NP*

The two above really can't be WRONG or even RIGHT or even NOT EVEN WRONG since they are speculations. The next few is where I need my sanity checked

3) Whenever a real world natural problem is in P it has a low degree poly algorithm OR there are ideas to make it work in practice, if not in theory. When Lance read a prior version of this post he pointed out that `real world natural problem' is not a well defined term. True. Not sure what to do about that. Even so, is this true? roughly true? Something theorists say to be able to sleep at night?

4) In the real world people say ``OH- the problem is NP-complete. Better look for approx solutions instead'' Or similar things. While this sounds true since I have never worked in the real world I really don't know. Is that what people say? do?

5) If Lance proves P=NP next week then the consequences are enormous for real people working on real computing problems. But what if Lance proves P NE NP? Would that affect people working on real computing problems? A long time Carl Smith told me

*If P NE NP was proven then the proof would have great insight into computing which would have a real affect on real people working on real computing problems*. My Darling (who is a Software Engineer) is skeptical of that. What do you think?

In the real world people are faced with NP-complete problems. I think they tend to solve them using heuristics rather than approximation algorithms with rigorous guarantees. SAT solvers are a good example of this: they seem to work well on "most" real-world instances.

ReplyDeleteOn 1, overwhelming focus on worst case time complexity on all problems took us in the wrong path.

ReplyDeletePrudent one would have been to study worst case time complexity for P and best case time complexity for NP problems and that would have brought us more fruitfulness.

On 2, everything is hard/impossible when we don't know about it. Once we know about it, then it is a question of possible or not.

One example coming to mind is the approximation algorithm for non-negative permanents (Jerrum, Sinclair, and Vigoda). Despite some effort to optimise its running time, the current best is still roughly n^7. It doesn't seem to be an easy problem to solve in practise either.

ReplyDeleteI do work in "the real world", and I do say things like 4).

ReplyDeleteAlthough often, the people I discuss with when something like that comes up have no idea what NP-complete problems are, so that typically gets translated to "We don't have the resources to give an exact solution", or something similar.

As for 5), I'm on Your Darlings side. A proof may give great insight, and it may have a real effect on real computing problems. But that remains to be seen.

Even if SAT is not in P there may be some few efficient SAT-solvers that handle all real world problems. See Knuth's fascicle "Satisfiability" for an overview of the huge progress that has already been achieved. Though I am not subscribing to his thesis that P=NP (but non-constructively!) I think it possible that SAT can be solved, though not necessarily in general, but for most, if not all, practical purposes. Also, even if NP is not contained in P/poly there may be sufficiently efficient circuits for all practical purposes.

ReplyDeleteOn (4): I do applied operations research at Google. We solve mixed integer linear programs w/ ~millions of constraints and variables every hour or so, often achieving optimality because practical solvers are very good. When we set it up wrong (bad conditioning, scaling, etc.) it takes 8-12 hours to finish. When we set it up right, 15 minutes to 2 hours.

ReplyDeleteRather than what you said in (4), I'd say "If your problem is NP hard, then there are great tools but you have to work a lot harder (and know a lot more theory) to make sure you don't spiral out of control in terms of time/space." If we mess up the set up for the problem (we as in our group collectively, not all of whom are experts in OR), we have to go ask the OR experts for help to figure out what, in theory, is causing our problem to solve slowly. Also, my impression is that a constant factor approximation would not fly for most applications, and it's FPTAS or use a solver (for serious problems), or give up on theory and use randomized greedy until it becomes a problem.

That being said, a colleague of mine came to me with what turned out to be the dynamic transitive closure problem for DAGs, and I was delighted to learn (via some notes of Virginia Williams) that there's a polynomial lower bound depending on the matrix multiplication exponent. This informed me and my colleague that she couldn't expect to have a data structure for this problem with a log-time update. That episode makes me think fine-grained complexity makes P more relevant, if not in its relation to NP, then among the relation between problems in P.

SAT cannot still be used to mine bitcoin efficiently (and it is a very practical problem!).

ReplyDeleteDetermining whether a graph has a clique of size 100 takes O(n^100) time using exhaustive search. Does that constitute a "natural problem"?

ReplyDeleteP=NP

ReplyDeletehttp://vixra.org/abs/1805.0399

" If Lance proves P=NP next week then the consequences are enormous for real people working on real computing problems."

ReplyDeleteI am not sure that is true either. A proof of that might not have any impact for at least quite some time. It is not as if people just close their shop once they know that the problem is NP-hard. People have developed efficient solvers for hard problems, and it is very hard to believe any of these algorithms will be displaced anytime soon.

I think the relative lack of polytime algorithms with a large exponent is simply due to the fact that coming with a say O(n^17) algorithm is very hard: The algorithm has to be complicated for its complexity to be O(n^17), at least if the value 17 does not appear explicitly in the input ("finding cliques of size 17"). On the other hand, coming with an algorithm in O(2^n) is often quite simpler. Our limited brains are still able to understand four nested loops, or maybe 5 or 6 for the brightest amongst us, but are short of understanding the power of tens of nested loops. Of course, this statement has no proof nor evidence, but I am nevertheless convinced that there is some truth in it.

ReplyDeleteHold on your holy crowd. What if the proof that P=NP

ReplyDeleteis non-constructive after all ? It would shed little, to none light on things.

(Incidentally, what is so wrong about saying NP=P ? Come on guys it is an equality after all and not an assignment.)

I think that your point 2) somewhat corresponds to what Scott Aaronson described as "8. The Self-Referential Argument" in his "Reasons to believe" post:

ReplyDeletehttps://www.scottaaronson.com/blog/?p=122

(Presumably that's also similar to what Lance wrote in _The Golden Ticket_, but I haven't read that).

This only stuck in my memory because a while ago, I commented on a "Godel's Lost Letter" blog entry describing "A Panel on P vs. NP":

https://rjlipton.wordpress.com/2017/02/05/a-panel-on-p-vs-np/#comment-80215

Basically, I was saying that even if P!=NP, you could still possibly figure out what the smallest circuit for SAT is (for _tiny_ problems), using a SAT solver. Then, incorporate that circuit into the solver; if it's actually an improvement, then you can find the best circuit for a _slightly_ larger problem...

Since my guess is that P!=NP, I don't think this would get very far (and would be a lot of work, and use a ton of CPUs). And if the circuit size for SAT increases quickly for small problems, that would only be circumstantial evidence that P!=NP. (However, it would find an optimal SAT-solving circuit for small problems, as a side effect. Admittedly, probably only very small problems.../)

Great News.

ReplyDeleteIF, P = NP

In the factorization of every odd number.

published in: International Journal of Engineering and Future Technology Vol 16 (1) 2019.

Is online now.