Explain the proof by Vinay Deolalikar that P != NP [closed]

Question

I've only scanned through the paper, but here's a rough summary of how it all hangs together.

From page 86 of the paper.

... polynomial time algorithms succeed by successively “breaking up” the problem into smaller subproblems that are joined to each other through conditional independence. Consequently, polynomial time algorithms cannot solve problems in regimes where blocks whose order is the same as the underlying problem instance require simultaneous resolution.

Other parts of the paper show that certain NP problems can not be broken up in this manner. Thus NP/= P

Much of the paper is spent defining conditional independence and proving these two points.

Dick Lipton has a nice blog entry about the paper and his first impressions of it. Unfortunately, it also is technical. From what I can understand, Deolalikar's main innovation seems to be to use some concepts from statistical physics and finite model theory and tie them to the problem.

I'm with Rex M with this one, some results, mostly mathematical ones cannot be expressed to people who lack the technical mastery.

I liked this ( http://www.newscientist.com/article/dn19287-p--np-its-bad-news-for-the-power-of-computing.html ):

His argument revolves around a particular task, the Boolean satisfiability problem, which asks whether a collection of logical statements can all be simultaneously true or whether they contradict each other. This is known to be an NP problem.

Deolalikar claims to have shown that there is no program which can complete it quickly from scratch, and that it is therefore not a P problem. His argument involves the ingenious use of statistical physics, as he uses a mathematical structure that follows many of the same rules as a random physical system.

The effects of the above can be quite significant:

If the result stands, it would prove that the two classes P and NP are not identical, and impose severe limits on what computers can accomplish – implying that many tasks may be fundamentally, irreducibly complex.

For some problems – including factorisation – the result does not clearly say whether they can be solved quickly. But a huge sub-class of problems called "NP-complete" would be doomed. A famous example is the travelling salesman problem – finding the shortest route between a set of cities. Such problems can be checked quickly, but if P ≠ NP then there is no computer program that can complete them quickly from scratch.

This is my understanding of the proof technique: he uses first order logic to characterize all polynomial time algorithms, and then shows that for large SAT problems with certain properties that no polynomial time algorithm can determine their satisfiability.