[HN Gopher] Is the largest root of a random real polynomial more...
       ___________________________________________________________________
        
       Is the largest root of a random real polynomial more likely real or
       complex?
        
       Author : jjgreen
       Score  : 157 points
       Date   : 2024-05-10 09:01 UTC (13 hours ago)
        
 (HTM) web link (mathoverflow.net)
 (TXT) w3m dump (mathoverflow.net)
        
       | Sebb767 wrote:
       | For those not well versed in the Stack Exchange network, what's
       | the difference between Math Stack Exchange and Math Overflow?
        
         | SassyBird wrote:
         | MSE is for noobs, think HS and uni students. MO is for pros,
         | think PhDs, researchers etc.
        
           | lupire wrote:
           | Adults, who might or not have graduated or attended high
           | school or University, also use MSE.
        
             | OJFord wrote:
             | There are probably some very clever people who are not PhDs
             | nor professional researchers using MO too, GP was just
             | giving examples ('think [...]').
        
             | colechristensen wrote:
             | This is not a helpful comment.
        
         | jjgreen wrote:
         | MO is for research-level discussions, questions are often
         | bumped from one to the other (more often from MO to MSE).
        
         | admissionsguy wrote:
         | I believe one was for professionals and the other one for
         | students. Don't remember which one, I guess Overflow is for
         | professionals since we use it for coding.
        
           | Sharlin wrote:
           | Honestly it's Stack Overflow that's really the "noob" level
           | programming discussion, with 95% of questions incredibly
           | elementary, whether "professional" or not.
           | cstheory.stackexchange.com is more analogous to
           | mathoverflow.com in that it's about research-level questions.
           | 
           | I think Math Overflow was the first overflow instance after
           | SO, so it has a sort of a special status and its own domain.
           | *.stackexchange.com came later to meet the growing demand for
           | more instances.
        
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         | Math Overflow domain and name are owned by a nonprofit
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         | https://meta.mathoverflow.net/questions/969/who-owns-mathove...
        
           | melenaboija wrote:
           | > The MathOverflow corporation is completely independent from
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           | operation of the site in a manner that meets the needs and
           | expectations of the community.
           | 
           | Does it make it more or less hostile than Stack Exchange?
        
             | rvnx wrote:
             | They claim "The MathOverflow corporation is completely
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             | and then when you click Legal notice, you see that all
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             | " _completely_ independent " is perhaps exaggerated claim.
        
               | lupire wrote:
               | The corporation is independent, and has the right to
               | unilaterally leave the website at any time. The current
               | website is not independent.
        
             | The_suffocated wrote:
             | MSE is sometimes perceived as hostile to beginners because
             | it often closes off newbie questions that lack context or
             | are thought to be of low quality. MO is not suffering from
             | this because almost all such questions are migrated to MSE.
             | In this sense, yes, MO is less hostile than MSE because its
             | hostility is outsourced to MSE.
        
               | barrenko wrote:
               | > Thievery corporation
        
             | mamediz wrote:
             | My experience with MathOverflow was funny, the first time I
             | posted a question it was almost immediatly downvoted and
             | criticized by the moderators. Later the same day, I was
             | surprised when my question was answered by Terence Tao,
             | then the moderators quickly changed their opinion and
             | people started to upvote the question.
        
               | renewiltord wrote:
               | Mathematics even worse than Software Engineering because
               | lay practitioner can miss significance of question. If
               | you have link to question, would appreciate. Just out of
               | curiosity to see if I would realize why question is
               | interesting. That in itself is a skill.
        
               | mamediz wrote:
               | To be fair the question was kind of naive, about
               | language, but T. Tao bother to answer and made my day.
               | https://mathoverflow.net/questions/206544/why-do-people-
               | use-...
        
               | renewiltord wrote:
               | Thank you for responding. Assuaged my curiosity :)
        
       | selimthegrim wrote:
       | Looking through the links from here one of the answers by Boris
       | Hanin may have resolved a question I had for a while.
        
       | meindnoch wrote:
       | As there's no formula for degree-5 polynomials and above, how do
       | you distinguish between a real root vs. a real number +
       | epsilon*i?
        
         | jcla1 wrote:
         | Just because of the impossibility of an exact formula for the
         | roots of a high-degree polynomial that does not rule out the
         | possibility of figuring out, say, the distribution of roots of
         | such polynomials. The question is not about any polynomial in
         | particular (hence Abel's theorem is no barrier).
         | 
         | edit: Think of the following example: take a polynomial a_n x^n
         | + ... + a_0, where the coefficients a_i are i.i.d. Bernoulli
         | random variables. Even though the degree n might be large (> 4)
         | I can say with confidence that such a polynomial has a real
         | root (x = 0) with probability 1/2. Similar though more
         | sophisticated arguments are at work in the linked question.
        
         | vouaobrasil wrote:
         | The estimations are not based on numerics, but rather
         | reasoning. For example, since real-valued polynomials are
         | stable under complex conjugation, each complex root must have a
         | complex conjugate. For polynomials with three distinct roots
         | then, one must be real. This is just an EXAMPLE of the kind of
         | reasoning that can be used to infer whether a root is purely
         | real or complex.
         | 
         | As for formulas: no, there is no GENERAL formula for the
         | quintic polynomial or higher. General formulas only exist for
         | polynomials of degree four or lower.
         | 
         | Of course, there are SPECIFIC formulas for specific KINDS of
         | higher-degree polyomials. But for the general quintic and
         | higher, nothing. That's already been proved classically.
        
           | orlp wrote:
           | > As for formulas: no, there is no GENERAL formula for the
           | quintic polynomial or higher.
           | 
           | Note that there is no general formula for quintics (and
           | higher) _in radicals_.
           | 
           | But it is a bit arbitrary to draw the line there. There is
           | also no 'general formula' using basic arithmetic operations
           | (addition, subtraction, multiplication, division) that gives
           | roots for x^2 = 2.
           | 
           | However it is quite useful to be able to talk about those
           | roots, so we added a dedicated symbol for them (sqrt), and
           | more generally extended the definition of exponentiation to
           | fractional powers.
           | 
           | If it were generally useful to talk about the roots of
           | quintics we also would've added common notation for their
           | roots.
        
             | lanstin wrote:
             | People sort of have:
             | https://en.wikipedia.org/wiki/Bring_radical
        
         | yorwba wrote:
         | You use Budan's theorem
         | https://en.wikipedia.org/wiki/Budan%27s_theorem to certify that
         | there's exactly one (or exactly zero) real root in the interval
         | ( _r_ - e, _r_ + e]. As long as your guess _r_ is within e of a
         | root, this allows distinguishing between real and complex roots
         | even if you don 't have the exact value. (There are instances
         | where Budan's theorem cannot give an answer, e.g. most
         | obviously if there's more than one root in the interval.)
        
         | red_trumpet wrote:
         | I could imagine that one uses the derivative here. If x+iy and
         | x-iy are complex roots, and y is really small, then the
         | derivative at x should be small as well (if y = 0, you have a
         | double zero, so p'(x)=0). So if p'(x) is far away from zero,
         | you have a single real zero. And I guess double zeroes don't
         | really happen with random coefficients.
        
         | HappyPanacea wrote:
         | If we have polynomial with integer coefficients, we can bound
         | the largest and smallest root by an expression that is maximum
         | of abs. value of coefficients to some power. There should be
         | similar bounds for the smallest imaginary value a root can
         | have. If your polynomial have real numbers for coefficients
         | having a formula doesn't help - we have the same problem of
         | being not able to tell if number is equal zero. For example in
         | the quadratic formula, we can have discriminant equal minus
         | epsilon. If epsilon was zero there is no imaginary root but if
         | it isn't we have an imaginary root.
        
         | bmacho wrote:
         | I think the others answered your question.
         | 
         | In addition, I suggest you to forget this "degree-5
         | polynomials" thing. They are not different from degree 2
         | polynomials in any meaningful way, if you talk about computing
         | their roots or reason about their roots.
        
       | LeonardoTolstoy wrote:
       | I assume this is one of these things where it is just like ... Oh
       | I don't know this kind of math.
       | 
       | Because in my mind I take a "random" polynomial and only consider
       | the largest two roots. Then I consider doing two reflections:
       | across the bottom/top of the curve, and across the x axis. If
       | those top two roots aren't degenerate the combination of the four
       | curves using the reflections have two curves with a largest real
       | root, and two curves with a largest imaginary root (I think). If
       | it is degenerate then the largest root is real.
       | 
       | So I would then conclude that (1) there are more real largest
       | roots than imaginary and (2) the "advantage" is vanishingly small
       | since there are infinitely more curves with a largest non-
       | degenerate root than degenerate root.
       | 
       | As one can tell I barely know the proper nomenclature. I assume
       | I'm mainly missing some consideration about the uniformity of
       | random coefficients not resulting in a uniform distribution in
       | space? Or possibly my reflections can violate one of the
       | conditions (real coefficients?)? Or I'm just plain wrong.
        
         | red_trumpet wrote:
         | I don't follow your reflections. Reflecting the graph of a
         | polynomial p(x) along the x-axis means replacing p(x) by p(-x).
         | What do you mean by reflecting "across the bottom/top of the
         | curve"? Reflecting along the y-axis? This would mean p(x) is
         | replaced by -p(x).
        
         | r0uv3n wrote:
         | What do you mean with reflection across the bottom/top of the
         | curve?
         | 
         | With combination you presumably mean taking
         | (polynomial+reflected polynomial)/2?
        
           | LeonardoTolstoy wrote:
           | Ah there we go. At the very least I was only considering the
           | subspace where the polynomial's derivative has only real
           | roots?
           | 
           | Think of a simple quadratic. The reflection is across the
           | min(f(x)) axis. But for higher order polynomials that doesn't
           | always work so that is certainly an issue. Thanks
        
       | keepamovin wrote:
       | Wow that is crazy that it is between chance and 1/phi.
       | 
       |  _In the linked MSE post, it has now been proved that the
       | probability that the largest root is real is at least 6.2%_
       | 
       | And! at least 1/10th of 1/phi. Hahaha! :)
       | 
       | I think the link of phi with number theory is natural in the
       | sense that the primes are not _random_ (as is commonly mistaken)
       | but arise recursively from prior primes: they are exactly the
       | 'gaps' not hit by multiples of prior primes, so there's a kind of
       | 'natural growth' pattern that we would expect to reflect some
       | manner of _e_ and _phi_
       | 
       | It's a very fundamental pattern of magnitude. Truth beauty et all
        
         | dr_dshiv wrote:
         | Beautiful! Phi always seems to show up when there is crowding.
        
           | keepamovin wrote:
           | Thanks! Let's collab. We should start a "primes
           | investigation" github repo!!!! :)
        
             | dr_dshiv wrote:
             | I love number theory in the context of waves. Here's an
             | example:
             | 
             | https://arxiv.org/pdf/1910.10751
        
               | keepamovin wrote:
               | Thanks. When you mention waves I'm wondering how we can
               | leverage FFT to make a function of the next prime? But it
               | has to include all primes, I guess? Or is this Reimann,
               | already??
        
               | Analemma_ wrote:
               | Yes, high-level descriptions of the RH are often vague
               | about the connection between the zeta function and the
               | prime numbers, but the short version is that, for every
               | point on on the complex plane, there is an associated
               | real-valued "Riemann harmonic function"; if you sum up
               | the Riemann harmonic functions of the non-trivial zeros
               | of the zeta function, you get the exact prime-counting
               | step function, and this function is much more predictable
               | if the RH is true.
        
         | RockofStrength wrote:
         | You get e from the primes by averaging the set size of only
         | growing gaps (2,3,5)(7,11)(13,17)... or only non-shrinking gaps
         | (2,3,5,7,11)(13,17)(19,23,29)...
         | 
         | Not a property of primes per se, just a property of growth. It
         | works better with a set of randoms.
        
           | lupire wrote:
           | I thinking that works for primes because primes are
           | approximately randomly distributed.
           | 
           | But primes are more directly "e" if you look at p(N), the
           | count of primes less than N. That is ~n/ ln(n)
           | 
           | https://en.m.wikipedia.org/wiki/Prime-counting_function
        
       | kzrdude wrote:
       | This reminds me of another of those random polynomial questions
       | where a good answer showed why the question asker's chosen
       | distribution of coefficients lead to the result they were seeing.
       | Anyone recognize that and have a link? It seems like it's
       | relevant here too.
        
         | evertedsphere wrote:
         | https://math.stackexchange.com/questions/206890/the-egg-biza...
         | ?
        
           | kzrdude wrote:
           | There's many of these then!
           | 
           | I think I found the one I was thinking of:
           | 
           | https://mathoverflow.net/questions/182412/why-do-roots-of-
           | po...
           | 
           | See answer by Will Jagy
        
       | Aardwolf wrote:
       | Does random polynomial mean with real or complex coefficients,
       | though?
        
         | cool_dude85 wrote:
         | The question posed specifies real coefficients, looks like
         | experimentally tested on random coefficients selected from [-1,
         | 1] from uniform distribution and a sort of scaled normal
         | distribution.
        
           | VyseofArcadia wrote:
           | This stuck out to me, and might be a place where our
           | intuition breaks down. As a human if you tell me, "give me a
           | polynomial with degree n with coefficients drawn uniformly
           | from [-1, 1]," I will likely produce one with a lot of zero
           | coefficients, just because a lot of the polynomials you
           | encounter in the wild have a lot of zero coefficients.
           | 
           | In reality, if you grab a coefficient uniformly from [-1,1],
           | the probability that it is 0 is 0.
        
             | madcaptenor wrote:
             | And how random sparse polynomials behave would _also_ be an
             | interesting question!
        
           | Someone wrote:
           | Likely (can't find the test code) tested on coefficients
           | selected from the IEEE doubles in _[-1, +1]_ , so from a
           | subset of numbers of the form _m /2^n_ with _m_ and _n_
           | integers and m between 0 and 2^53 or thereabouts.
           | 
           | For this problem, I don't see how that would give different
           | results as picking any number in _[-1, +1]_ , but that's not
           | a priori guaranteed. Mathematicians are well-trained as
           | nitpickers.
        
             | eternauta3k wrote:
             | Aren't roots continuous functions of the coefficients?
        
         | Smaug123 wrote:
         | The first line of the question says "real".
        
           | Aardwolf wrote:
           | But that first line is a statement about something related
           | but different (the question is about largest root, this
           | statement is about any root), and the term "random
           | polynomial" without specifying anything is used throughout
           | the rest of the content
        
         | outop wrote:
         | If you allow complex coefficients then the distribution is
         | rotationally symmetric in the complex plane. So a root is no
         | more likely to be in the real line than in any other line
         | through the origin. There would be vanishingly few polynomials
         | with largest root real.
        
       | dailykoder wrote:
       | Sligthly OT, but I always have fun reading these maths posts
       | here: I enjoyed maths in University very much (I did CE) and the
       | joy from my professors always inspired me. I'd love to learn more
       | and dip into solving things with it (maybe with a hang towards
       | numerics?).
       | 
       | But been out of Uni for 2 years now and haven't done much, so
       | probably gotta relearn quite a bit. Does anyone have some good
       | ideas where to start and find fun ideas? Not numerics, but I did
       | quite a few projecteuler problems during my bachelors - maybe
       | there is more like this? Any ideas welcome.
       | 
       | Or should I redo all tasks in my textbooks first? ;-)
        
         | belter wrote:
         | A bit unrelated to your question, but if you love Maths you
         | can't miss The Math Sorcerer:
         | https://www.youtube.com/@TheMathSorcerer
        
         | jjgreen wrote:
         | I went to University to do maths 6-7 years after leaving school
         | (with decent maths results there) so had forgotten everything.
         | Nothing beats doing all of the exercises that you did before,
         | and actually you find that you've not quite forgotten
         | everything. Good luck!
        
         | jsbg wrote:
         | Mathematics and Its History
         | (https://link.springer.com/book/10.1007/978-1-4419-6053-5) is a
         | really fun book for someone who already knows some university-
         | level math and is just curious.
         | 
         | You may also enjoy Proofs from THE BOOK
         | (https://link.springer.com/book/10.1007/978-3-662-57265-8).
        
       | ptero wrote:
       | Two questions that immediately jump at me are:
       | 
       | 1. What is random? They seem to have run numerical experiments,
       | which suggests they used (something equivalent to) uniform
       | integer coefficients from a bounded set. And that could affect
       | findings a lot!
       | 
       | 2. Did they look at odd degrees (which always guarantees a real
       | root), even or both?
       | 
       | Not trying to knock this fun post.
        
         | lupire wrote:
         | Right. The problem as originally stated is paradoxical. You
         | can't uniformly sample an unbounded set. I assume OP used a
         | random floating point, which are bounded.
         | 
         | But the important part of the question can be asked for any
         | possible distribution over the reals.
        
           | Sesse__ wrote:
           | > You can't uniformly sample an unbounded set.
           | 
           | You definitely can, as long as you are accepting the use of
           | limits (and without that, sampling uniform over any
           | subsegment over the reals would also be pretty much
           | impossible). "Sample a uniformly chosen real" just is
           | shorthand for "Sample over a real from [-N, +N], then we are
           | interested in the answer then N goes to infinity". In this
           | case, then just divide all the coefficients by N, which
           | obviously doesn't affect the answer. So uniformly over
           | [-1,+1] is as good as uniformly over R.
           | 
           | This is very common. See e.g. number theory, where you can
           | ask questions like "what is the probability that two randomly
           | chosen natural numbers are coprime"; the set of natural
           | numbers is also unbounded, but it doesn't prevent this
           | question from being well-defined, and the answer is 6/p2
           | (about 0.608).
        
             | mturmon wrote:
             | This comment opens perhaps a bigger can of worms than
             | intended.
             | 
             | The original statement:
             | 
             | >> You can't uniformly sample an unbounded set. [of real
             | numbers]
             | 
             | is true, provided that by "sample" we mean "sample in a way
             | that obeys Kolmogorov's axioms". (I'm adding the qualifier
             | "of real numbers" to keep this somewhat grounded, so that
             | we don't get distracted with abstract metric spaces, or
             | worse.)
             | 
             | In the above comment, by placing a limit on the set ([-N,
             | +N]), you have made the set bounded -- thereby
             | contradicting the premise of the original statement!
             | 
             | *
             | 
             | Your last paragraph is interesting. Suffice to say, the
             | situation is more complex than your summary would indicate,
             | precisely because of the above issue -- that is, "what does
             | _sample_ mean in this context ".
             | 
             | It led me to this paper by Lei and Kadane (the latter, the
             | well-known Bayesian theorist) --
             | 
             | https://arxiv.org/pdf/1806.00053
             | 
             | It is well worth a read, if you're into such things.
             | 
             | See especially section 1, and section 7. In essence, the
             | above statement ("You can't sample uniformly") is true, if
             | you interpret "sample" as "following a scheme that obeys
             | Kolmogorov's well-known axioms."
             | 
             | However, if you are willing to abandon countable additivity
             | of your measure, and drop down to finitely-additive
             | measures, there are several classes to choose from that
             | support a notion of uniformity.
             | 
             | (Again, just thinking about measures on integers, not real
             | numbers. But these are not measures in Kolmogorov's sense.)
             | 
             | These measures support a notion of uniformity, but they may
             | not have some properties that you might hope for, such as
             | that the measure of a set A of natural numbers is the same
             | as A + i, where adding "i" shifts the set by "i" units left
             | or right.
             | 
             | For some of these measures, the result about co-prime
             | numbers is as you say, and for others, the result is
             | indeterminate. That is, the limit exists, but it can be any
             | number between 0 and $6/\pi/\pi$.
        
               | selimthegrim wrote:
               | >It led me to this paper by Lei and Kadane (the latter,
               | the well-known Bayesian theorist)
               | 
               | Not to be confused with J-P Kahane, who also studied this
               | area (of the posted article) via Fourier series
        
           | ptero wrote:
           | They are also discrete, coming from a finite set. I remember
           | at the undergrad differential equation class the teacher
           | asked for some arbitrary coefficients so we can classify the
           | result.
           | 
           | She was very surprised that 3 differential equations she got
           | all turned out to be parabolical, which form a set of
           | probability 0 in the full space. But of course the
           | probabilities for equations with small integer coefficients
           | (which is what people wrote) are totally different.
        
           | owyn wrote:
           | > You can't uniformly sample an unbounded set.
           | 
           | I was asked to do this as an interview question at google! It
           | was about log file processing not math (sample an infinite
           | log file), and I thought my answer was ok? But that's why I
           | don't work there so I am really enjoying this whole thread.
        
             | CamperBob2 wrote:
             | What's the correct answer (and what was yours?)
        
             | racional wrote:
             | It was definitely a dorky question, and a glaring sign of
             | the interviewer's lack of maturity to blithely burn through
             | your precious time in this manner.
             | 
             | But if you're going to play Google's game, this what you're
             | going to get apparently. So what was your answer? One
             | guesses that they weren't so much interested in a correct
             | answer (since it's a bullshit question anyway) but that you
             | were willing to give it a college try (and reference some
             | concepts about limits, and perhaps the geometric layout of
             | this "file system" in the Doctor Who universe they were
             | apparently hoping to roll out this system in, the speed of
             | light, etc).
             | 
             | In order to, you know, pass their dipshit filter.
        
               | pedrosorio wrote:
               | Given the description about "sampling from an infinite"
               | something, it's likely this was intended as a variation
               | on reservoir sampling.
               | 
               | https://en.wikipedia.org/wiki/Reservoir_sampling
        
               | hcks wrote:
               | "I refuse categorically to entertain fun maths problems."
        
         | ykonstant wrote:
         | As stated in the definition, the distribution is that of
         | independent uniform coefficients in (-1,1).
        
           | thaumasiotes wrote:
           | True, but they give a justification for that distribution,
           | and the justification is incorrect.
           | 
           | > The number of real roots of a random polynomial with real
           | coefficients is much smaller than the number of complex
           | roots. Assume that the coefficients are independently and
           | uniformly random in (-1,1) for if not then we can divide each
           | coefficient by the coefficient with the largest absolute[]
           | value to scale each coefficient to (-1,1).
           | 
           | It's true that, if you have a known polynomial, you can
           | normalize the coefficients in this way... assuming that (-1,
           | 1) is a typo for [-1, 1].
           | 
           | But it's definitely not true that you can produce a random
           | polynomial as the input to this idea. That can't be done. The
           | concept of "a random polynomial with real coefficients" is
           | known not to exist; you would have to specify some kind of
           | nonuniform distribution over the reals for the coefficients
           | to be drawn from. Once you have done this, the distribution
           | of coefficients in the normalized equivalents that have
           | coefficients in [-1, 1] will not be uniform.
           | 
           | In other words, the question says "assume that the
           | coefficients [of a random polynomial] are independently and
           | uniformly random in (-1, 1), for if not, we can exhibit
           | equivalent polynomials whose coefficients are dependently and
           | nonuniformly random in [-1, 1]". But this observation makes
           | no sense.
        
             | ykonstant wrote:
             | >The concept of "a random polynomial with real
             | coefficients" is known not to exist;
             | 
             | The concept of "a random polynomial with real coefficients"
             | means what we want it to mean in each particular context;
             | mathematicians don't care too much about semantics, but
             | interesting questions. And this is a very interesting
             | question.
        
               | eternauta3k wrote:
               | He's right that this question isn't fundamentally about
               | the space of polynomials but rather about this particular
               | arbitrary-looking distribution. But yeah, the question is
               | interesting nonetheless.
        
               | scotty79 wrote:
               | I don't get your objection. (-1,1) uniform distribution
               | is equivalent to uniform distribution over (-inf, +inf)
               | when it comes to roots. Because you can map one to one
               | between those intervals without affecting roots.
        
               | SassyBird wrote:
               | There is no such thing as a uniform distribution over
               | (-inf, +inf) for the simple reason that the Lebesgue
               | measure of (-inf, +inf) is +inf and you can't make the
               | integral of the whole set be equal to 1. For it to be a
               | valid probabilistic distribution, the integral of 1 over
               | the whole space relative to the distribution should be 1.
               | But it won't be. In U(a,b) a and b need to be bounded, so
               | that you can divide the Lebesgue measure by (b - a).
        
               | jprete wrote:
               | It's impossible to have a uniform distribution over
               | (-inf, +inf). Since the distribution is uniform, each
               | digit has an equal chance of being zero through nine, but
               | that's true of all digits or else it's no longer uniform.
               | At that point 100% of the values drawn in any sampling of
               | the distribution are infinite.
               | 
               | If scaling the coefficients down to [-1, +1] gives a
               | uniform distribution, then the original distribution must
               | have been uniform in [-x, +x] where x is the largest
               | coefficient in the original polynomial. That, too, is a
               | contradiction! It means that either -x or +x are
               | guaranteed to be one of the random coefficients, which is
               | very much not a uniform distribution.
               | 
               | (That said, I think the (-1, +1) version of the problem
               | still has interest!)
        
               | zeroonetwothree wrote:
               | Typically you would do a uniform (-x, x) and then take
               | the limit.
               | 
               | This isn't necessarily the same as the (-1, 1) version
               | though
        
               | eigenket wrote:
               | Its the same because the uniform distribution over (-x,x)
               | can be obtained by taking a random variable uniformly
               | distributed over (-1,1) and multiplying it by x.
               | 
               | This doesn't change anything for the question about roots
               | because the roots of a polynomial don't change when you
               | multiply it by x.
        
             | shiandow wrote:
             | There are ways to 'approximate' a uniform distribution by
             | just choosing greater and greater ranges and then
             | normalising the result.
             | 
             | One problem with this is that there's no one way to do it.
             | You could argue equally well that the result should be a
             | uniformly random point on the S100 sphere.
             | 
             | What it definitely cannot be is a random point on the
             | [-1,1]^100 cube because if you divide by the greatest
             | absolute value then one of the coefficients will be -1 or 1
        
       | andrewla wrote:
       | If you're looking to run some numerical experiments with this,
       | there is built-in support in R for this sort of thing:
       | plot(polyroot(runif(101,-1,1)))
       | 
       | will give you a visualization of the roots of a polynomial of
       | degree 100.
        
       | dahart wrote:
       | > Assume that the coefficients are independently and uniformly
       | random in (-1,1) for if not then we can divide each coefficient
       | by the coefficient with the largest absolutely value to scale
       | each coefficient to (-1,1).
       | 
       | Not sure about my intuition here, but won't scaling this way
       | create a non-uniform distribution for all but the largest coeffs,
       | since the scaling is a divide?
        
         | klyrs wrote:
         | Yeah, but you can't pick a random real number with uniform
         | distribution. Something's gotta give.
        
         | axblount wrote:
         | The important thing is that multplying a polynomial by a
         | constant doesn't change its roots.
        
           | dahart wrote:
           | Ah right, of course. The original coefficient space would
           | still be uniform. I guess this means that a bi-unit [-1,1]
           | interval for the random numbers is equivalent to any space of
           | coefficients [-n,n] when it comes to answering the
           | probability question?
        
       | charlieyu1 wrote:
       | Now it surprised me as a guy who studied a lot of math. I would
       | think the complex plane is much larger than the real line so the
       | probability would go towards 0.
        
         | guyomes wrote:
         | That would probably be true if all the coefficients were
         | complex numbers. The biais here comes from the fact that all
         | the coefficients are real numbers.
        
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