Kolmogorov Complexity and the Primes

Bill's post on how to derive the non-finiteness of the primes from Van der Waerden's theorem reminds me of a nice proof using Kolmogorov complexity.

A quick primer: Fixed some universal programming language. Let C(x), the Kolmogorov complexity of x, be the length of the smallest program that outputs x. One can show by a simple counting argument for every n there is an x such that C(x) ≥ n. We call such x "random".

Suppose we had a finite list of primes p₁…p_k. Then any number m can be expressed as p₁^e₁···p_k^e_k. Pick n large, a random x of length n and let m be the number x expresses in binary. We can compute m from e₁,…,e_k and a constant amount of other information, remembering that k is a constant. Each e_i is at most log m and so we can describe all of them in O(log log m) bits and thus C(m) = O(log log m). But roughly C(m) = C(x)  ≥  n = log m, a contradiction.

But we can do better. Again pick n large, a random x of length n and let m be the number x expresses in binary. Let p_i be the largest prime that divides m where p_i is the ith prime. We can describe m by p_i and m/p_i, or by i and m/p_i. So we have C(m) ≤ C(i,m/p_i) ≤ C(i) + C(m/p_i) + 2 log C(p_i) ≤ log i + log m/p_i + 2 log log i + c. The 2 log C(p_i) term is needed to specify the separation between the program for i and the program for m/p_i.

Since C(m) ≥ log m, we have
log m ≤ log i + log (m/p_i)+ 2 log log i + c
log m ≤ log i + log m - log p_i + 2 log log i + c
log p_i ≤ log i + 2 log log i + c
p_i ≤ O(i (log i)²)

The prime number theorem has p_i approximately i log i, so we get just a log factor off from optimal with simple Kolmogorov complexity.

I wrote a short introduction to Kolmogorov complexity with this proof. I originally got the proof from the great text on Kolmogorov complexity from Li and Vitányi and they give credit to Piotr Berman and John Tromp.

Van der Waerden's theorem implies the infinitude of the primes

(Sam Buss and Denis Hirschfeld helped me on this post.)

I was reading the table of contents of the American Math Monthly and saw an article by Levent Alpoge entitled

Van der Waerden and the primes

in which he showed from VDW's theorem that the set of primes is infinite. The article is  here and here. My writeup of it is here.  Prof K saw me reading the paper.

 K: I see you are interested in proving the set of primes is infinite from VDW's theorem.

BILL: Yes, who wouldn't be!!!!

 K: Well, lots of people. Including me. Can't you just state VDW's theorem and then give the normal proof? Would that count? Besides, we already have an easy proof that the set of primes is  infinite without using VDW's theorem.

I turn K's comments  into a valid question:  What does it mean to prove A from B if A is already known?

 There are two issues here, informal and formal.

Informally:  If you look at the proof of VDW-->primes infinite the steps in that proof look easier than than the usual proof that the set of primes is infinite. And the proof is certainly different. If you read the paper you will see that I am certainly not smuggling in the usual proof. Also, the proof truly does use VDW's theorem.

Formally one could (and people working in Reverse Mathematics do similar things- see the books Subsystems of Second order Arithmetic by Simpson,, and  Slicing the Truth, reviewed here) devise a weak axiom system that itself cannot prove the set of Primes is Infinite, but can prove the implication VDW-->Primes infinite.  Note that Reverse Mathematics does this sort of thing, but for proofs involving infinite objects, nothing like what I am proposing here.

Open Problem 1: Find a proof system where the implication VDW-->Primes infinte can be proven, but primes infinite cannot. Sam Buss pointed out to me that for the weak system IΔ₀ it is not known if it can prove the primes are infinite.

Open Problem 2: Find a proof system where you can do both proofs, but the prove of the implication is much shorter. Perhaps look at (VDW--> there are at least n primes) and (there are at least n primes)
and look at the length of proof as a function of n.

Open Problem 3: The statement there are no primes with  n bits, the with leading bit 1 can be expressed as a propositional statement. Get lower bounds on its refuation in (say) resolution. (A commenter pointed out an error in a prior version of this one so be wary- there may be an error here as well.)

I am suggesting work on the reverse mathematics of systems much weaker than RCA₀. I do not know if this is a paper, a PhD thesis, a career, a dead end, or already pretty much done but I am not aware of it.

The Grad Student Tax

By now as you've read from Luca or Scott or PhD Comics or a variety of other sources on the dangerous changes to the tax code that passed the US House of Representatives last week. Among a number of university unfriendly policies, the tax code will eliminate the tax exemption for graduate student tuition for students supported with teaching or research duties, nearly every PhD student in STEM fields. The CRA, ACM, IEEE, AAAI, SIAM and Usenix put out a joint statement opposing this tax increase on graduate students. This is real.

Without other changes, a tax on tuition will make grad school unaffordable to most doctoral students. In computer science where potential PhD students can typically get lucrative jobs in industry, we'll certainly see a precipitous drop in those who choose to continue their studies. Universities will have to adjust by lower tuition, if finances and state law allows, and raising stipends. US government science funding will at best remain flat so in almost any scenario we'll see far fewer students pursue PhD degrees particularly in CS and STEM fields. Keep in mind we already don't come close to producing enough CS PhD students entering academia to meet the dramatically growing demand and these moves could frustrate faculty who also might head off to industry.

The current senate proposal leaves the exemption in place though no one can predict what will happen the the two bills get reconciled. In the best case scenario this bill goes the same way as the failed health care reform but republicans seem desperate to pass something major this fall. So reach out to your representatives, especially your senators, and express the need to leave in the exemption.

A Tale of Three Rankings

In the Spring of 2018 the US News and World Report should release their latest rankings of US graduate science programs including computer science. These are the most cited of the deluge of computer science rankings we see out there. The US News rankings have a long history and since they are reputation based they roughly correspond to how we see CS departments though some argue that reputation changes slowly with the quality of a department.

US News and World Report also has a new global ranking of CS departments. The US doesn't fare that well on the list and the ranking of the US programs on the global list are wildly inconsistent with the US list. What's going on?

75% of the global ranking is based on statistics from Web of Science. Web of Science captures mainly journal articles where conferences in computer science typically have a higher reputation and more selectivity. In many European and Asian universities hiring and promotion often depend heavily on publications and citations in Web of Science encouraging their professor to publish in journals thus leading to higher ranked international departments.

The CRA rightly put out a statement urging the CS community to ignore the global rankings, though I wished they made a distinction between the two different US News rankings.

I've never been a fan of using metrics to rank CS departments but there is a relatively new site, Emery Berger's Computer Science Rankings, based on the number of publications in major venues. CS Rankings passes the smell test for both their US and global lists and is relatively consistent with the US News reputation-based CS graduate rankings.

Nevertheless I hope CS Rankings will not become the main ranking system for CS departments. Departments who wish to raise their ranking would hire faculty based mainly on their ability to publish large number of papers in major conferences. Professors and students would then focus on quantity of papers and this would in the long run discourage risk-taking long-range research, as well as innovations in improving diversity or educating graduate students.

As Goodhart's Law states, "when a measure becomes a target, it ceases to be a good measure". Paradoxically CS Rankings can lead to good rankings of CS departments as long as we don't treat it as such.

Can you measure which pangrams are natural

A Pangram is a sentence that contains every letter of the alphabet

The classic is:

                                      The quick brown fox jumps over the lazy dog.

(NOTE- I had `jumped' but a reader pointed out that there was no s, and that `jumps' is the correct word)

which is only 31 letters.

I could give a pointer to lists of such, but you can do that yourself.

My concern is:

a) are there any pangrams that have actually been uttered NOT in the context of `here is a pangram'

b) are there any that really could.

That is- which pangrams are natural?  I know this is an ill defined question.

Here are some candidates for natural pangrams

1) Pack my box with five dozen liquor jugs

2) Amazingly few discotheques provide jukeboxes

3) Watch Jeopardy! Alex Trebek's fun TV quiz game

4) Cwm fjord bank glyphs vext quiz
(Okay, maybe that one is not natural as it uses archaic words. It means
``Carved symbols in a mountain hollow on the bank of an inlet irritated an
eccentric person'  Could come up in real life. NOT. It uses every letter
exactly once.)

How can you measure how natural they are?

For the Jeopardy one I've shown it to people and asked them

``What is unusual about this new slogan for the show Jeopardy?''

and nobody gets it. more important- they believe it is the new slogan.

So I leave to the reader:

I) Are the other NATURAL pangrams?

II) How would you test naturalness of such?

Pinning down `natural' is hard. I did a guest post in 2004 before I was an official co-blogger, about when a problem (a set for us) is natural, for example the set all regular expressions with squaring (see here).

Advice for the Advisor

A soon-to-be professor asked me recently if I could share some ideas on on how to advise students. I started to write some notes only to realize that I had already posted on the topic in 2006.

Have students work on problems that interest them not just you. I like to hand them a proceedings of a recent conference and have them skim abstracts to find papers they enjoy. However if they stray too far from your research interests, you will have a hard time pushing them in the right directions. And don't work on their problems unless they want you to.
Keep your students motivated. Meet with them on a regular basis. Encourage students to discuss their problems and other research questions with other students and faculty. Do your best to keep their spirits high if they have trouble proving theorems or are not getting their papers into conferences. Once they lose interest in theory they won't succeed.
Feel free to have them read papers, do some refereeing and reviewing, give talks on recent great papers. These are good skills for them to learn. But don't abuse them too much.
Make sure they learn that selling their research is as important as proving the theorems. Have them write the papers and make them rewrite until the paper properly motivates the work. Make them give practice talks before conferences and do not hold back on the criticism.
Some students will want to talk about some personal issues they have. Listen as a friend and give some suggestions without being condescending. But if they have a serious emotional crisis, you are not trained for that; point them to your university counseling services.
Once it becomes clear a student won't succeed working with you, or won't succeed as a theorist or won't succeed in graduate work, cut them loose. The hardest thing to do as an advisor is to tell a student, particular one that tries hard, that they should go do something else. It's much easier to just keep them on until they get frustrated and quit, but you do no one any favors that way.

Computer science evolves dramatically but the basic principles of advising don't. This advise pretty much works now as well as it did in 2006, in the 80's when I was in the student or even the 18th century. Good advising never goes out of style.

Of course I don't and can't hand out a physical proceedings to a student to skim. Instead I point to on-line proceedings but browsing just doesn't have the same feel.

Looking back I would add some additional advice. Push your students and encourage them to take risks with their research. If they aren't failing to solve their problems, they need to try harder problems. We too often define success by having your paper accepted into a conference. Better to have an impact on what others do.

Finally remember that advising doesn't stop at the defense. It is very much a parent-child relationship that continues long after graduation. Your legacy as a researcher will eventually come to an end. Your legacy as an advisor will live on through those you advise and their students and so on to eternity.

The two fears about technology- one correct, one incorrect

When the luddites smashed loom machines their supporters (including Lord Byron, Ada Lovelaces father) made two arguments in favor of the luddites (I am sure I am simplifying what they said):

1. These machines are tossing people out of work NOW and this is BAD for THOSE people. In this assertion they were clearly correct. (`lets just retrain them' only goes so far).
2. This is bad for mankind! Machines displacing people will lead to the collapse of civilization! Mankind will be far worse off because of technology. In this assertion I think they were incorrect. That is, I think civilization is better off now because of technology. (If you disagree leave an intelligent polite comment. Realize that just be leaving a comment you are using technology. That is NOT a counterargument. I don't think its even IRONY. Not sure what it is.) 
3. (This third one is mine and its more of a question) If you take the human element out of things then bad things will happen. There was a TV show where a drone was going to be dropped on something but a HUMAN noticed there were red flowers on the car and deduced it was a wedding so it wasn't dropped. Yeah! But I can equally well see the opposite: a computer program notices things that indicate its not the target that a person would have missed. But of course that would make as interesting a story. More to the point- if we allow on computers to make decisions without the human elemnet, is that good or bad? For grad admissions does it get rid of bias or does it reinforce bias? (See the book Weapons of Math Destruction for an intelligent argument against using programs for, say, grad admissions and other far more important things.)

I suspect that the attitude above greeted every technology innovation. For AI there is a similar theme but with one more twist: The machines will eventually destroy us! Bill Gates and Steven Hawkings have expressed views along these lines.

When Deep Blue beat Kasparov in chess there were some articles about how this could be the end of mankind. That's just stupid. For a more modern article on some of the dangers of AI (some reasonable some not) see this article on watson.

It seems to me that AI can do some WELL DEFINED (e.g., chess) very well, and even some not-quite-so-well-defined things (Nat Lang translation) very well, but the notion that they will evolve to be `really intelligent' (not sure that is well defined) and think they are better than us and destroy us seems like bad science fiction (or good science fiction).

Watson can answer questions very very well, Medical diagnosis machines may well be much better than doctors. While this may be bad news for Ken Jennings and for doctors, I don't see it being bad for humanity in the long term. Will we one day look at the fears of AI and see that they were silly--- the machines did not, terminator-style, turn against us? I think so. And of course I hope so.
  

Matching and Complexity

Given a group of people, can you pair them up so that each pair are Facebook friends with each other? This is the famous perfect matching problem. The complexity of matching has a rich history which got a little richer in the past few months.

For bipartite graphs (consider only friendships between men and women), we have had fast matching algorithms since the 1950's via augmenting paths. In the 1965 classic paper, Path, Trees and Flowers, Jack Edmonds gives a polynomial-time algorithm for matching on general graphs. This paper also laid out an argument for polynomial-time as efficient computation that would lead to the complexity class P (of P v NP fame).

After Razborov showed that the clique problem didn't have polynomial-size monotone circuits, his proof techniques also showed that matching didn't have polynomial-size monotone circuits and Raz and Wigderson show that matching requires exponential size and linear depth. Because of Edmond's algorithm matching does have polynomial-size circuits in general. NOTs are very powerful.

Can one solve matching in parallel, say the class NC (Nick's class after Pippenger) of problems computable by a polynomial number of processors in polylogarithmic time? Karp, Upfal and Wigderson give a randomized NC algorithm for matching. Mulmuley, Vazirani and Vazirani prove an isolation lemma that allows a randomized reduction of matching to the determinant. Howard Karloff exhibited a Las Vegas parallel algorithm, i.e., never makes a mistake and runs in expected polylogarithmic time.

Can one remove the randomness? An NC algorithm for matching remains elusive but this year brought two nice results in that direction. Ola Svensson and Jakub Tarnawski give a quasi-NC algorithm for general graph matching. Quasi-NC means a quasipolynomial (2^polylog) number of processors. Nima Anari and Vijay Vazirani give an NC algorithm for matching on planar graphs.

Matching is up there with primality, factoring, connectivity, graph isomorphism, satsfiability and the permanent as fixed algorithm problems that have played such a large role in helping us understand complexity. Thanks matching problem and may you find NC nirvana in the near future.