M(57885161) verified as 48th Mersenne Prime
mersenne.org
mersenne.org
It seems to me like he had a private method for calculating primes (that was imperfect, sure, but also astounding for being developed in the 17th century).
To me it feels like when this is brought up, modern day mathematicians are too quick to dismiss him based on the incorrect numbers/the missed primes, and ignore the many correct ones and the question of _how_ he managed to find them. That's what I would like to know, is his method.
It's a small sample but it's worse than you make it sound, I think. He came up with four new numbers, given what was known at the time. Half were wrong and he missed three. Whatever method he'd come up with, it was pretty badly mistaken. It's curious, historically, but almost certainly mathematically uninteresting.
He did not just stumble onto two correct primes in the 17th century by chance. He had used a seemingly now-lost process that was semi-fruitful. This should be worthy of investigation, not dismissed as "mathematically uninteresting".
Mersenne somehow figured out M(127)...in the early 1600s, and no one to this day seems to know how. How is this not mathematically interesting?
M31 you can verify by hand, which really leaves us with one out of four. It's entirely possible he got one right by chance. It probably wasn't strictly by chance, very likely convinced himself of some pattern that doesn't really hold. There are plenty of neat-looking lines in, say, an Ulam spiral but those don't generate primes either.
This is exactly the attitude I alluded to in my earlier post. You think M(127) was found arbitrarily through chance or plucked from a likewise arbitrary pattern, which then allows you to completely write him off...as opposed to the possibility I'm asking about: that he actually did have an interesting method of analyzing primes which, while faulty, could nonetheless prove fruitful or even just interesting. It's this dismissiveness that I don't understand.
https://primes.utm.edu/glossary/page.php?sort=MersennesConje...
Which seems both plausible, or at least in the ballpark and not terribly mysterious. He got a big number right. It happens.
That's not to say there aren't mathematical mysteries and curiosities of that nature. You have to look no further than Mersenne's contemporary and correspondent Pierre de Fermat for a solid one. He cranked out a few dozen theorems with next to nothing in the way of surviving proof. From the famous one on down, he got just about all of them right. People have been wondering aloud how exactly he pulled that off since at least Gauss's time. Nearly as famously, he got one wrong:
https://en.wikipedia.org/wiki/Fermat_number
These look, in form, reminiscent of Mersenne's numbers and the first few of them are also prime. F5 is not. In a fun connection, Euler, the same dude who showed M31 is prime showed F5 is not. You can almost imagine Mersenne and Fermat shaking their fists and yelling 'Euleeeeeeer!' Seinfeld-like, from their graves.
2, 3, 5, 7, 13, 17, 19, 31, 61, 89, 107, 127, 521
And Mersenne's was:
2, 3, 5, 7, 13, 17, 19, 31, 67, 127 and 257
2-19 were proven naively prior to Mersenne with just 128 trial divisions leaving his portion of the list as:
31 (right), (missed), 67 (wrong), (missed), 127 (right) and 257 (wrong)
Knowing it's just 31 and 127 that were really new AND correct does it still seem like Mersenne truly "somehow figured out M(127)"? To me it seem like he either applied an erroneous method or, more likely in my opinion, just had erroneous naive checking of these particular values up until he felt confident enough to list them rather than rigorously prove them and it sticks out to us as M(127) is the highest of these that we retrospectively know to be valid.
Of course the method was erroneous, that's not the point I am (apparently rather poorly) trying to get across. My point is that the method seems to have been lost to time, and that it is a shame because while faulty for finding primes it may prove fruitful elsewhere, or may simply be interesting. And if he had a method of checking for primes in the 17th century, this would also be noteworthy and interesting, though it would beg the question of why he stopped presumably before reaching a thousand.
My base observation is relatively straightforward and I honestly don't understand the dismissiveness it is met with:
Sometime in the early 1600s, Mersenne found M(127). The 'how' is interesting to me because I don't see it likely that this was discovered by random luck, but predicated on a novel method he had developed which, while clearly very flawed, would nonetheless still curious to see. Our math and science textbooks are filled with various flawed methods, why then is this one of seeming no concern?
The exact method doesn't matter because poorly performed naive division checking with the first couple thousand primes gets you a more accurate list of primes so whatever he was doing isn't particularly interesting (and could have been as simple as something like that that, we just can't say how he messed up for certain without a time machine).
Maybe it would be possible to create a block-chain technology in which their participants calculate something scientifically meaningful.
Wikipedia lists a few https://en.wikipedia.org/wiki/List_of_distributed_computing_...
The impact may be small, but I see no reason why the impact should be there in the first place just to satisfy some mathematicians' curiosity about special numbers.
A more likely scenario for such independent instructions would operate on an entire bit string, like boolean operators and vector instructions. I think you'd have a tough time producing any useful output from such an algorithm, though, because you wouldn't be able to do much with conditionals to keep the branch predictor score fair.
I don't think that there are any algorithms that could operate within a generic benchmark that could have random elements in them _and_ product a useful result. Either the calculations are different and fair but meaningless, or they're exactly the same with the same result.
One that springs to mind is Monte-Carlo sampled raytracing. An individual ray might take more or less time to compute, but the time to compute 10 million rays will be statistically be roughly constant. You could even imagine averaging a bunch of renders of the same scene from different machines to get a lower-noise result, thereby demonstrating a benchmark combined with useful work.
Statistical predictability is the key.
(Confession - this isn't exactly theoretical. I sometimes have occasion to render light fields, and I shard the work over as many random workstations as I can get my hands on. It's always obvious which workstations are faster than others, even without making any special attempt to balance the workload. I think this is actually a workable concept.)
Which now that I think about it, of course it would be like that. But still, what a headache for someone who just wants The Best One.
Tape measures are useful even if they're not the same length.
Higher-end engineering tapes acknowledge that length isn't constant, and provide temperature coefficients, ranges of accuracy, and tensioning guidelines.
You can spend a lot of money on a tape measure which is more exact under more conditions. Most people don't need this.
It proves it because with the selected algorithm, there's no known shortcut to just doing a load of hashes, so if you found one it means you did a lot of hashing.
What problem would you insert instead that had the same properties?
We should also not forget that it gets harder and harder for the same benefit. Any amount of energy can be poured into a PoW system. Invent fusion, use it all in crypto.
The problem then is more economics, who has an incentive to discover more Mersenne primes?
With a PoW that has "external rewards", the cost of attacking the chain falls in proportion to the external reward. You are correct that it does not fall to zero.
No individual would pay for it, but it’s still more valuable than random noise.
Now one can argue that mathematical patents provide more costs than benefits, which is a line of debate I am certainly open to. End that, and math research would be a public good.
But in the system we have now, a huge amount of mathematical research has generated a significant amount of money for very many people, from the RSA algorithm to Penrose patenting his tilings (and getting money from toilet paper makers that used his aperiodic tilings to make their TP more fluffy [1])
What I think you mean to say is that the market consistently undervalues mathematical research. Unfortunately it's not so easy to actually demonstrate, because given the huge amount of math research produced, only a very small amount of that ends up being useful in the future and often the original discoverer is long dead. This is similar to the situation with other arts and sciences. A small number of people drive the field forward and the vast majority of scientific publications are basically filler. Certainly the world would be better with more effective funding mechanisms that we have on offer, which is basically a game of capture-the-grant.
[1]https://apnews.com/article/59354892d1875c277c91afd3fb5ca516
You can use these distributed computing projects for burn-in testing though. When you just want to run your computer hard to make sure it can manage N hours at 100%, and don't necessarily need to know how it compares to others, distributed computing can work (although, there's some risk of wrong results and therefore more work for the organizers if you're testing your system in extreme conditions, sometimes those lead to miscalculations)
rank p digits discovered
48 257,885,161-1 17,425,170 2013 Jan 25
49* 274,207,281-1 22,338,618 2016 Jan 07
50* 277,232,917-1 23,249,425 2017 Dec 26
51* 282,589,933-1 24,862,048 2018 Dec 07
So we've already found three larger ones but have still yet to be verified as the 49th, 50th, 51st consecutive ones. Verification seem of more interest to those following closely and discovery of another one being bigger.I guess my question is - is this applied in any way?
EDIT: I see from responses my comment created the impression that I conflate "purely intellectual" and "not valuable." That was not my intention, simply asking whether this has applications or not.
No, no, we ask that all the time. :P
Because it was hard
> is this applied in any way
Not sure, but this is a vacuous way to define what is important
Carbon footprint anyone? ;)
Mₙ = 2ⁿ − 1 gives an easy to generate candidate set and testing primality of a single number isn't that expensive. 57885161th candidate being the 48th hit is getting pretty sparse I guess, but it isn't that crazy. What am I missing? Seems the compute would be rather small compared to much of the "big data" / NN training done every day
You do not check whether that is a prime by trial division (https://en.wikipedia.org/wiki/Trial_division). If you could do a division every nanosecond and had a billion devices, that would still take way, way more time than till the heat death of the universe to do.
We have better tests (https://en.wikipedia.org/wiki/Primality_test#Fast_determinis...), but the best generic one still is Õ((log n)⁶). Here, log(n) is about 58 million, so that log(n)⁶ has over 46 digits.
We can do better, as we don’t need a generic test. for Mersenne numbers, there’s https://en.wikipedia.org/wiki/Lucas–Lehmer_primality_test.
See here for more background:
If you scroll to the very bottom there's some stuff on recent use of probable prime proofs, as well as a fairly detailed explanation of how the whole thing operates.
If that property continued after 2^20,996,011 - 1 the next one would be around 2^86,133,241-1 which is bigger than the (provisional) 51st Mersenne prime, so we'd be stuck at "40th verified Mersenne prime found in 2003".
Which really makes you wonder about the density of these primes in exponent terms ...
GIMPS' ~1PetaFLOPS[1] uses no more than a megawatt [2].
That's about 10 Gigawatt-hours (GWh) over a year [3].
The average US house uses about 10 MWh in a year [4] so GIMPS = the electrical power use of ~1,000 households. Portland reduced their power consumption by 20 GWh by switching their street lights to LEDs[5].
The electrical consumption for Superbowl parties is an estimated 75 GWh [6]. (I think Superbowl parties are far more frivolous.)
[1] - https://www.mersenne.org/primenet/
[2] from 2012, "HP plans to deliver a full peak petaflop with just a single megawatt" - https://www.hpcwire.com/2012/09/05/hp_intel_score_petaflop_s...
[3] 365.25days * 24hours/day * 1MW / 1000 = 8.8 GWh
[4] "In 2020, the average annual electricity consumption for a U.S. residential utility customer was 10,715 kilowatthours (kWh)" https://www.eia.gov/tools/faqs/faq.php?id=97&t=3
[5] https://www.portlandoregon.gov/transportation/article/678781
[6] https://www.americasgenerators.com/blog/post/2017/02/15/What...
Mathematics in general is a bunch of stuff that often looks useless to laypeople or even professionals and may look so for 200 years until it very suddenly becomes relevant and crucial.
The computer that you used to post your comment uses a lot of mathematical stuff developed in previous centuries. It is unlikely that Gauss or Euler could have predicted that their work (ink on paper) will one day be used to facilitate completely different modes of communication.
And it is well possible that some priest criticized them for wasting their talents on abstract things that don't help humanity as of 1800.
Say these are really relevant 200 years down the line, maybe even 100 or 50, does it really make sense to calculate them now, given that computers 50, 100 and 200 years from now will outperform ours by orders of magnitude?
Looking at this as reference: https://en.wikipedia.org/wiki/Microprocessor_chronology
Say you grab a CPU from the end of the 70s and get it working until now, that'd be about 13*10e15 operations, or about a month of processing in a modern 5GHz CPU. That's 40 years to a month.
IDK about this particular scientific team, but if you have to run a long-running task, you will be a lot more careful about complexity and speed of your data and algorithms instead of relying on brute force. Maybe you will actually discover some real improvements of existing knowledge.
I can even see the effect throughout my IT life outside science. People who cut their teeth on processors with limited power and not enough RAM tend to produce much more efficient solutions even when programming modern computers. They just don't take the gigabytes and gigahertz for granted.
The Mersenne primes for example are a delightful little mathematical object. IMO they are immediately curious and interesting. I am very grateful that we continue to make progress in understanding them.
It’s not known if there is an infinite number of Mersenne primes (though it is strongly suspected) - so an interesting mathematical object to discover would be ‘the largest Mersenne prime’ - but this process isn’t going to find that (it might find a candidate but it won’t find a proof). If there are indeed infinite Mersennes then this search is just guaranteed to keep finding more forever.
It’s like looking for green pebbles on a beach. It’s nice when you find one to add to your collection, but it’s not that surprising, and if you keep looking you’ll probably find more. You’re not advancing geological knowledge if you keep looking and finding more.
It’s like saying, we have a project that keeps sorting larger and larger sets using quicksort because we believe it will help advance the state of sorting algorithms. ‘We just sorted the largest ever set!’
We often don't know what the applications are at the time, but that pure mathematics work very often forms the foundation of something important later on.
When people give such an argument, they usually mean "I am not interested in this topic" and use "why is this helping humanity" as an excuse for their ignorance.
"When people say that other people usually mean X when they ask something, they are just ascribing that motive to another person, even if it is not what the asker has meant."
A few years ago, I got a PhD in pure mathematics (low dimensional geometry and topology). As someone who loves pure math and spent many years devoted to it, I've had many conversations with mathematicians about whether we should be concerned with practical applications. One common argument, which several people have made here, is that there's been a lot of math that found practical application long after it was done. That really only applies to a very small proportion of math. The vast majority of math done has still never found application. So arguing that we should do that math because it may have applications later is kind of like arguing you should play the lottery since there's a greater than 0 chance that you'll win. Also, from the (admittedly biased) group of mathematicians I've spoken to about this, very few seemed to actually do math in the hope that it would be useful later.
Instead, people do math because it's interesting, beautiful, and challenging. I would argue this Mersenne Prime search is almost more like recreation. I could be wrong, since I never did much number theory, but I don't think this is advancing any research. It's just a fun hobby for people who enjoy math. I think doing math for the sake of its beauty is generally fine. I do have concerns about the climate aspect of this particular project though. This is a lot of power being consumed just for recreation. This project may not be big enough to have much of an environmental impact, but in general, I think we should be more mindful of not wasting large amounts of computing power just for fun.
How do _you_ help humanity by browsing and posting? Like, what a weird thing to ask. I assume you ask the same thing of everything, right?