Error Correcting Curves - Numberphile
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- เผยแพร่เมื่อ 31 ส.ค. 2023
- Isabel Vogt on error correction, curves, and some big polynomials. Extra footage at: • Eating Curves for Brea...
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Isabel Vogt at Brown University - www.math.brown.edu/ivogt/
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Took a class with Professor Vogt my freshman year of college and absolutely loved it, she’s a fantastic lecturer. So happy to see her on numberphile!
Funny seeing you here
I'm a fan.
@@aidanhennessey5586haha i was wondering if this would happen
What class was it?
One of the first uses of Reed-Solomon error correcting codes was to transmit data back from the Voyager spacecraft. Once they got beyond Jupiter, the signal to noise ratio became untenable without error correction.
I thought they used binary and not decimal encoding. would it not be untwoable?
@@TaohRihze Reed-Solomon in binary is simpler to understand and implement
@@TaohRihzeBa-dum tish!
@@TaohRihze You have to account for inflationary language. Without that things would be twoderful.
@@allanolley4874 That's a deep cut from the 1940s.
It was a bit glossed over but properly a Reed-Solomon code can correct up to half the redundant bits. So +1 would only detect the error and +2 would correct up to 1 bit. If you have a million bits then the chances of more than one error is really high. So in practice it's somewhere around 1.5x the information rather than 3x the information.
Also, the extra numbers need to be float instead of integers, no? I can't see how it can be done with only integer y's.
@@tinglin6121 Unfortunately the end of the explanation is rushed. I think you're right, you construct a polynomial using the sent data as coefficients - these can be integers, because it's the input, you're not trying to achieve any particular curve on a graph, whatever you get is fine. Then you pick a point or two (or more) on the graph, and at that part it could be challenging to pick a point which both x and y are integers. This brings the question of precision and I imagine as many things in life, the actual error correction is full of complex nuances like finding the most optimal precision and number of points. Also there was no example on how it actually looks when you detect a mismatch: you got your 16 values + 2 additional points, but those 2 additional points don't lie on the curve drawn by the polynomial defined by the 16 points - so how do you find which is the offending point, and then how do you use the 2nd value to correct the error? There might be some smart math behind that, but it wasn't presented.
I always love the interaction with Brady. He's questions and thoughts are always on point and helpfull.
*His questions
Yeah. In this case he asked a very on-point question -- he asked whether the maximum number of random points that a curve interpolates is also the minimum number of points that defines the curve.
The answer is yes, as explained later in the video, but unfortunately it seems like the prof misunderstood his question :(
Reed-Solomon Codes are very prominently used for CDs. That way a tiny scratch can be accounted for and doesnt make your data unrecoverable.
The real magic is the interleaving. Reed Solomon can't help you if the whole code is unreadable
Note that when you do these manipulations in binary, they get a whole lot easier and faster to process. Reed-Solomon coding is actually one of the earliest and easiest error correction codes to calculate. It was so easy that even in the limited processing of pagers in the 1980s, they could work quite well. But there are more complex error correction codes that can correct longer strings of errors. There are Bose-Chaudhuri-Hocquenghem (BCH) codes, of which Reed-Solomon is a subset, there are "turbo-codes" and there are Low Density Parity Check codes (LPDC). Each of these methods will enable one to get perfect copy closer and closer to the noise floor.
Do any of those correct for *missing* bits? As in, let's say I send 50 bits of real message encoded in 100 total bits, and a chunk of 10 bits in the middle just never arrives at the destination, can one of these codes allow the receiver to reconstruct the 50 bit real message from the 90 that did arrive?
@@maxbaugh9372 Replace the missing bits with random ones at the receiver and you are back to the original situation. This is not the most efficient way, because by using the information the bits have been "erased" you can optimize the error correcting code and the receiver, but it would still work. You can research about Information Theory to find out all the mathematical limits and awesome stuff you can do.
@@maxbaugh9372 if I am not wrong, they can, since bits comes in packages, also, missing bits are registered as bits with 0 value.
but I am not sure
@@maxbaugh9372usually you know how many bits the message is, so you would add the missing bits as zeroes.
But that probably works only if you know where the bits are missing.
This is generally not a problem because signals are clocked, if a link is functioning at all the both sides will agree completely about how many bits were sent.
The “sending it twice” has been the default method of error correction since the invention of the telephone, but it can break down if factors like someone’s accent or a poor connection make the repeat sound similar. For example, “nine” and “one” can sound similar and letters like “B” and “D” can also sound similar, and still sound the same to the hearer upon repeat.
Fun fact: Credit card numbers already have error detection so that the scenario described on the start doesn't happen. It's not error *correction* though
thank you.
I can't imagine why you'd use forward error correction for a credit card number when you could just detect an error and ask the sender to resend that packet. It's typically only used for very low latency requirements like live video, or for situations like storage or digital TV broadcasts where it's impossible to request a resend. But I guess it's just an example.
@@gdclemo Yes it is just an example, however asking the sender to send again isn't always an option even ignoring latency demands. For example, imagine archival. There, your primary struggle is against temporal corruption: if your archival solution survives longer than the original work's medium, you don't get to ask for the original work again when corruption occurs.
@@mark.panghyUzcard does, it is from Uzbekistan. Also, some credit cards from the communications industry or used in healthcare may start with 8.
@@gdclemo Well, the website asking for the number might not explicitly do it, but all of the communication between both ends is error-corrected at various levels of he networking stack.
Is this Isabel's Numberphile debut? She's awesome!
The mathematics of how this is accomplished in real-world applications is very interesting as well. I can't recall if Numberphile has done a lot of videos on finite fields, but they're nicely suited to software and hardware implementations. Nowadays they've largely been replaced by turbocodes although they're still important. The Voyager probes use Reed-Solomon codes, as does the Compact Disc audio standard.
And ata disks and fpgas have a niche for finite fields calculations
QR codes also use Reed-Solomon.
this seems like a very easy crossover with computerphile to look at the actual implementation of these codes and what kind of algorithms they're using
Isabel Vogt is an amazing teacher. Great video!
Yeah it's okay
Nah
Great explanation of Error-Correcting Codes! I never looked at Reed-Solomon codes in this way.
Extra footage from this interview over on Numberphile2: th-cam.com/video/eLPfRY4NATw/w-d-xo.html
Tq
MORE MATH.
you should pin this
Glad I checked the comments. Is there a reason to post this here and not in the description?
this seems like a comment that traveled through time as the comment is from 1 day ago, but the video got published 13 hours ago
I was always surprised that Jules Verne, in the "In Search of the Castaways (French: Les Enfants du capitaine Grant, lit. 'The Children of Captain Grant')", did not have a letter in a bottle streaked with small print all over the field with the coordinates of the island where the shipwrecked were saved, instead of a long-winded artistic description of the severity of their situation.
Great questioning from Brady!
Very cool! Great video :)
if I'm not mistaken, that's how compact discs that contain data (not audio) function.
audio discs don't contain such a mechanism (so, they technically speaking hold more useful data); but instead errors get like "filtered" out. because music is unlike data not just entirely random. so, one single harsh spike won't pass the subsequent filter.
Audio CDs also use Reed-Solomon for error detection and correction. The 'filtering' happens when there are errors that the Reed-Solomon code can't correct.
@@edwardfanboy oh, okay.
but there's definitely a difference in data they contain.
maybe a CD for computers contains even more correction methods. but you definitely lose some storage capabilities. (and no, I'm not talking about discs that store more then 650 megabytes which just use more if the available area.)
I mean, that's all aside this other encoding method where way more physical "pits" get used to represent digital bits. I think you need fourteen to represent eight. that's because you need to constantly focus the laser (it's about a square millimeter when it hits the surface to be insensitive towards scratches) and can't just have eight consecutive zeros. it *must* change after two spots or the laser will lose focus and alignment. this kinda like provides the same function as the punched sides of analog film, so to speak.
@@HxTurtle CD's, Barcodes, QR codes, even ASDL all have encoding schemes that prevent the continuous zero issue, But we usually don't call that data, it's something extra from the data payload that's being sent and it's below the EC.
The PCM audio stream coming out of the CD is identical to the master copy used to make the CD, If the data can be repaired with EC, the issue is most shitty CD rippers put the CD drive in audio playback mode which just ignores errors and keeps playing to maintain tempo, and not data read mode which will try reading the data sector again. Remember Audio CD's were designed in the 80s to be played on affordable hardware with kB of RAM, you don't have 10s of buffering to go back and attempt to reread the data like we do with TH-cam, or can wait a few extra seconds for the game to load like on the PS1.
If I remember right, audio CDs have just enough error detection to detect a bad sample and recreate it from its neighbours, and only protect the most significant bits of the sample. Data CDs need much more error correction to recreate every bit perfectly.
The more you learn about data mechanisms, the more wonderful it is that all of it can be stored so flawlessly.
Extremely well explained. Great video.
3:04 - There's another problem with the tripling the digits solution in the manner executed here. Errors are usually 'bursty'. There'll be a bit of noise and a whole bunch of digits in a row will have a problem. Tripling each individual digit protects very poorly against this. It would be better to send the whole number three times.
It took me a long time to wrap my head around Reed-Solomon codes because in their implementation they use Galois fields. Specifically Galois fields of characteristic 2, which are basically strings of binary digits where addition is replaced with xor.
But, because I'm me, I had to have a deep understanding of _why_ you could replace addition with xor and still have algebra work exactly the same because otherwise I would never remember how the whole thing worked because it wouldn't fit into a system.
I think you use xor to make (and check) whether an array is odd or even (one of the easiest to understand [and therefore to implement] error checking [and potentially self correcting, if you make it two dimensional] methods.)
You can mitigate against this problem by the sender doing 1) encode 2) permute the coded bits 3) send. The receiver must thus do 1) receive 2) unpermute the received bits 3) decode. One sort of permutation is called interleaving. But permuting bits is independent of encoding...
Do you know of any books that explain the theory in your last paragraph? I learned this from a book about computer networking, and if I recall I was annoyed that this leap was made. In fact, I had this type of annoyance several times during my engineering degree.
@@DF-ss5ep Discrete mathematics, usually a required course for computer science, "Discrete Mathematics with Applications", is the book I would recommend, Will give you an intro to things like fields, especially in the section about encryption, it should give you enough info to actually gain some intuition. The more rigorous Abstract Algebra stuff is a bit above my knowledge.
Love the cute paper change intermissions!
This method is also used for a multi-password encryption (not real term), for example if you want to encrypt a message, and provide 10 people with their unique decryption keys, and require that in order to encode it at least 4 people need to get together and uae their unique decryption keys at the same time.
That was an absolutely brilliant explanation!
Of what? Is numberphile aimed at cognoscenti or ordinary curious people? If the second we need more explanation
@@jeremykeens3905in my case it's admittedly closer to the former. I'm a software developer for the airline industry, the hardware I work with uses error correction codes for all sorts of things like passport barcode scans and so on. I've known for a long time that polynomials are used for error detection and recovery but I've never really intuitively understood why.
You should be the guest
I wish I could say I followed this, but I'm getting an error between my neurons.
Knowing Lagrange's Interpolation and working out a few programming examples may help in understanding the original problem.
@@abhijitborah If someone is completely lost it might be a bit much to expect them to be able to jump straight to understanding and applying the maths here, no?
If you also look at the second video, you have two new pieces of data, and can correct the error between your neurons. :)
All you need to do is add another connection of neurons
@@abhijitborahI think that was a joke, fyi
Lagrange Interpolation is a huge part of the research I’m doing for my masters, such a cool tool
So cool!!!! Thanks for the explainer!
Credit card numbers already contain a check digit that tests for errors. Does that mean you only need one extra digit for the polynomial, since the 16th digit is already non-random?
I think technically no, you still need two extra, but I’m not sure it’s terribly important. See, the 16th digit is only non-random if you enter your credit card number correctly. If you make a mistake, it potentially becomes 16 random numbers. Those 16 numbers when transmitted electronically could have further errors, so you still need two extra digits on top to catch and fix. However, if you entered an invalid credit card number in the first place, I’m not sure how important it is to catch any errors that happen afterwards, but from a coding regulation perspective you probably want to just to catch the edge cases.
Depends on implementation. The credit-card code catches any single-digit error, but might fail to catch that there are two errors. So if you get 17 digits that you detect as wrong, there will likely be 2 combinations of 16 digits subsets that produce legal credit card number, so it is likely not enough to be self-repairing, but using it might still be useful with some other tricks.
15:04 of course, if there was a degree-15 polynomial through those 17 points then it must be the same as the original one (since it still passes through the 16 correct points, and there's only one degree-15 polynomial that does this)
Also, as stated, you could _probably_ correct the error sending just 17 points since the degree-15 polynomials interpolating the wrong sets of 16 points would, in all likelihood, not have integer coefficients. But I assume in practice we'd be doing this over finite fields, in order to reduce the information being sent.
So for the 4 special cases, is there a different expression for the minimum number of random points needed for the interpolation, or is there NO possible error correction/interpolation?
When I took the error correction code class, the first page of the slides professor showed is to teach us the field, group .... and it's so difficult for a beginner and hard to find the relation btw channel coding.
It's so nice to watch this video !
It's kind of amusing to me that credit card numbers were chosen, because they already have error detection built-in to the cc# itself (mod10 code) to detect people transposing digits or having a wrong digit when writing them down or saying them over the phone.
And also because sending 48 bytes instead of 16 bytes would make no noticeable difference to the transmission time. It was a pretty bad choice of example.
@@RFC3514Perhaps, but this isn't computerphile.
The polynomial used in Lagrange interpolation taken to its limit describes a generating function. Maybe the redundancy of Reed-Solomon codes using Lagrange interpolation explains why generating functions have functional equations such as recurrent relations on their coefficients? Also, using a generating function that is periodic with each period containing the finite set of numbers n (in the same order) would be like the naïve approach on its side taken to its limit. I think finding those periodic generating functions is doable.
Small correction that I’m sure someone else has pointed out already but I can’t see: At 7:00 3 random points do not ALWAYS lie on a circle, as there is always a chance that the three points end up on a straight line
They pointed it out themselves. Their use of the word "random" is hiding some mathematical details that are too much of a tangent to get into. The tldr is that that those cases are a measure 0 set and so come up with 0 probability so we are mathematically justified in ignoring them (that doesn't say that they are impossible however). If you really want you could say it is a circle with a radius of infinity
s/random/generic/
A straight line is also often considered as a circle with basically infinite radius.
Then even if they are on a line, we can fit such a circle
Even though the 2 extra data points that we need to send for error correction is independent of the number of data we are sending, this however would only work if we assume that we can have at most 1 mistake in the message we're trying to send. Unfortunately the bigger the data we are sending the higher the likelihood of multiple errors at the same time so we'll probably have to send more than 2 extra data points for error correction to work with larger data messages, which of course wouldn't cause any problem in practical terms since the ratio of redundant to the essential data would be close to 0 for large messages.
Well, this video provides the thought process, implementation of course need to solve more problems. But still, imagine you need to send milion digits and expect 10% to get lost on the way. With naive solution, you might need grahams number of digits to send, while with this solution, you actually need to send little over 1,2 milion digits to be reasonably sure the code will still fix itself on the other side.
Now I understand how RAID6 works a little better :) Great video
She's great at explaining
fantastic video
Thanks :) how does it work regarding the precision of the extra number we send? How many digits doest it require ? I guess you can't always find an integer no ?
Hey, Brady, been watching Numberphipe for more than a decade. Can you please add introductory information about the people you interview and ask them to name the field they talk about so those who are interested can leave more?
It's linked in the description.
I really like the B-roll of the landscape around there.
I realise this is beyond the scope of the video, but the main question this raised for me was the difference between single digits (4 bits) and polynomial coefficients (floating point numbers, which require much more storage, which would defeat the object of the exercise). I'm sure the real algorithm has some optimisations for binary, but that did bother me a bit!
Great video! Always amazed by Brady's ability to ask insightful questions. Professor Vogt was so engaging!
Did I get it right? The x coordinate is the position of the digit in the number, and the y is the value of the digit?
Another video about error correction code in a short while, this is the third one now. Coincidence??
That way if there was an error in any of the videos we can use the other two videos to correct it
I guess there is still a nonzero albeit small chance that the random errors manage to evade detection by satisfying some other polynomial? Can you quantify this probability?
The precise answer might sound strange to you. If the noise is a continuous random variable, the answer is 0 (think gaussian distribution or bell curve). If the distribution has discrete components, or delta functions (think flipping a coin), and they are perfectly placed, it can be non-zero, but this would never happen in practice. Is it POSSIBLE for a random error to produce a new polynomial and evade detection? Yes, it is an event that exists in the event space (in math jargon this is called the sigma field). But it is probability 0. It’s a quirk of probability theory but these don’t mean exactly the same thing. Essentially, it CAN happen, but it WON’T ever happen even if you repeat the random trial an arbitrary number of times.
As a fun addendum, this method can also be used to share a secret between parties.
Suppose you want to share the missile launch code among five generals, in such a way that if two go mad, the missiles can't be launched. At least three need to go mad.
Choose a polynomial f(x) = a2 x^2 + a1 x + a0, where a0 is the launch code, and a1 and a2 are random numbers.
Give one of these pairs to each general: (1,f(1)), (2,f(2)), (3,f(3)), (4,f(4)), and (5,f(5)).
Then if any three generals go mad, they can use Lagrange interpolation to calculate f(0), which is the launch code. If only two generals go mad, they can't do it.
(Note: As with error correcting curves, you need to work in a finite field for exactly no information to be leaked.)
Curious analogy. 😐
@@warlockpaladin2261 If you'd prefer to substitute a secret chicken or soft drink recipe, that's totally fine.
Isabel reminds me of Tom Crawford so much! Same look, same smile, and same enthusiasm. Does anyone else see that?
A variant maybe? Who knows?😂
In the same league of a Parker Square, this may be a Vogt Circle :D
Regarding the comment at 12:40, on knowing 16 points on a degree 16 polynomial determines it uniquely via Reed-Solomon - this seems oddly analogous to the Fundamental Theorem of Elimination Theory. I’m really curious whether this can be made precise.
Can this be used to detect if a seemingly random set of numbers (say, cases of an infectious disease on a map) are not random?
yay new video
Reminds me of Reed-Solomon error correction in link 16
Whoa…that is WILD!
Are the x values that get plugged into the curve just the indices of the numbers in the sequence being sent?
Replacing 16 base-10 digits with 16 floating-point coefficients is basically the same problem that we originally sought to avoid, though. Base-10 can be expressed fairly compactly in BCD, or even more compactly in pure binary.
_One_ single-precision IEEE float is 32 bits (4 bytes). 4 bytes can encode 8 base-10 digits in BCD, or about 9.63 base-10 digits in binary. Even if each base-10 digit is repeated 3 times for redundancy and error correction, using floating point numbers instead still results in about 2-3x as much data.
In real applications the computation is usually done over a finite field rather than the real numbers, which does not have this problem
Very smart indeed. But what if there are more than one errors? Trying out all possible combinations and fitting them to a curve seems to be quite an expensive process in itself.
i get that 3 random points will virtually never fall on a line, but isn’t that only true for analog data, where the number of possible points are infinite? in an extreme case of a grid that was, say, 4x4, 3 random points would create a line relatively often. what’s the resolution threshold for this method to actually be reliable?
Problem is points consist of two numbers. So unless our points are all roots, we have to send 2*(n+2) instead of 3n for error correction.
Thanks!
I'd prefer Parker Correcting Code which first confirms the nonexistence of errors, then introduces errors in a way which skirts most error correcting algorithms.
I think it would have been useful to do this example with only two points first as it’s much easier to see for someone not so versed in maths first before 16. Ie take 2 numbers make a line out of them by giving them some corresponding x values pick two more points on the line. Then if any of the points is wrong three points will still line on the line whilst only one won’t so you can see where an error is. Then add the heavier maths.
What is the (practical) runtime complexity of this algorithm? Trivially implemented I think it's going to be completely impractical, running in something like factorial (O(n!)) time, because of how fast nCr grows (for the example in the video - C(17, 15) = 136 curves to fit). I assume that Reed-Solomon probably suggested something a lot more effective (probably linear time, maybe even constant?), could you maybe do a follow-up video on that (unless it's covered in the extended video - going to check that one out later).
Awesome!
What if the impostor is one of the two extra numbers?
How can one figure out which point that is wrong given 2 extra points to help correct it?
Do you have to do N checks, each check omitting one point that you're checking for, in order to figure out which one that was wrong?
Because then with 1 million points, you'd have to do 1 million checks in the RS case, but if you were given 3 million points (in the naive case) by sending the 1 million data points 3 times, you'd only have to look at the wrong data point and do a majority vote of 3 numbers.
Fascinating.
Wouldn't it likely also be enough to send only the original 16 points because in case of an error occurs the probability of the coefficients being integers is incredibly small?
In practice, this is usually done over finite fields. So in other words, your polynomial can only take on a very limitied set of values to begin with. For example only the values 0, 1, 2, 3, 4, 5 and 6. No real numbers, no rational numbers, nothing in between, just these seven values. So if there is an error, it may change a 4 to a 6, or something. This is being done because calculating with these limited set of numbers is quicker than with the entire rational numbers.
When I was teaching math I loved the idea of smoothly connecting curves, I always taught my students the equation that passes through 3 points in order to make any multiple point graphs look smooth.
When they learned calculus I further taught them to make them "connect" smoothly by slopes and peaks.
Funtime.
I guess it wasn’t Brady’s day as he sounded a bit annoyed in contrast to the pure enthusiasm of miss Vogt.
Something does appear to be a bit off in comparison to other Numberphile videos. I am curious to the reason.
Maybe he had explosive diarrhea
Love the geometrical earrings
How does adding the 18th number help us correct the error? i understood fully up to the point where they start adding the additional points. 17th point i still understand, but how does the 18th point help correct the error?
If you don't have a degree 15 polynomial when you look at the 18 numbers, you can look at the eighteen different sets of 17 numbers. One of them will exclude the error, so will be 17 numbers that all lie on the degree 15 polynomial, while the other seventeen will each be the 1 error and 16 correct values. Since the 16 correct values define the correct degree 15 polynomial, which we know the error is away from, none of them will give a degree 15 polynomial.
So if there's at most one error, there will only be one degree 15 polynomial that fits 17 of the points given, and that will be the correct curve.
Adding the 18th point means you now effectively have 17 different sets of 17 different points, each excluding a single point. If there's one error, all the sets except the one that doesn't have the bad value will detect the error. You can then use the set that doesn't have the error to determine the correct value for the corrupted piece of data.
Quick question - I can see why adding two extra numbers for the RS code will enable you to identify which number is erroneous, but how is this then used for error correction? Couldn't the true value lie anywhere on the curve - how do we know what to correct it to?
I suppose as you know the polynomial, you just correct this erroneus number to the output of the polynomial at the exact same input
The coordinates are spaced out equally on the x axis. The x value is just the position of the number in the stream of data. There is one unique value generated by the polynomial for each position on the x axis and it is the value of numbers at their chosen x coordinate. To reconstruct the missing number you just put its position in the stream into the polynomial function.
I think you can test each of the 18 points one by one.
When testing the i-th point, you take the set of the other 17 points and find their interpolation. If the erroneous point is in this set, then the interpolation will have degree 16. But if the erroneous number isn't included in the set, then the interpolation of the points will be the original polynomial of degree 15.
So, when you test the erroneous point, you find that it's in fact errouneous, as well as the original polynomial. From the point you can take its value of x an by evaluating the polynomial you can get the corrected value.
@@Unifrog_ aah thank you! This was the key piece of info I'd missed - that the x value is just the position in the stream!
At what level of the OSI are these extra numbers sent and handled?
So, you transmit the coefficients of the polynomial. How many bits are needed to specify all of these coefficients?
6:45 was a Parker circle. They gave it a go.
I want to see how to create the ECC values using the input data... and how to verify/correct the received signal.
Is it a similar principle how your CD player fills in the gaps from a scratched disc? Other information is encoded within the surrounding data?
CD's actually use this exact mathematics.
This was fascinating and passionate. But, there seemed to be a whole introduction for the generalist that was missing. What the heck were those curves with all the points? Did you just transmit the credit card numbers assuming they were on a curve, or did you transmit the polynomial? I had absolutely no idea what was going on. Was there an earlier video?
Yes!
Nice and clear, right up until 13:24 where "n is the max we cant specify any more points".....What? Then later the presenter specifies more points (17 and 18). I get that you need a minimum of 2 points to specify a line and 3 points for a quadratic etc - where does the max come in?
i still dont get how, given 17 digits and one is wrong, how to detect that there exists an underlying polynomial in the first place? How do you avoid it becoming a guess-and-check combinatorics problem, where you check if any combination of points lies on some 15th degree polynomial?
Why are there light switches behind the books in the bookcase?
likely the switches pre-date the bookcases. you would think just relocate the switches, but there might not be a more suitable location. the most likely answer is that doing so requires union labor and significant cost. it is less expensive and more efficient simply to cut holes in a bookcase. I've heard stories very similar to this.
While I follow the maths of finding a number of points that uniquely define a curve, I don’t understand how you get from these 2-dimensional points with real numbers x and y to a list of single integers.
In terms of computational error detection and correction, I’ve found it more understandable and to view checksums in terms of parity bits. Hamming ECC uses a set of parity bits each of which covers half of the bits of the data and parity itself, so when any of the parity bits are wrong the positions of the incorrect parity bits point to the bit that was in error. I’ve heard of Reed-Solomon ECC since it is used for disc storage, but its algorithm looks very complicated.
That's where this algorithm gets into the really obscure math. The answer is that instead of real (or rational) numbers, you use something called a finite field, which has addition, subtraction, multiplication, and division like rational numbers, but there are only 256 of them and the answers don't agree in any way with the real numbers. They were discovered as a theoretical exercise, but they have all the necessary properties for Lagrange's Interpolation Theorem to be true. Of course, the graphs really don't look like anything, because there isn't anywhere between two points to draw a line.
If you know the x-co-ordinates of the points you're looking at (and for a method like this, you'll generally pick sensible x values in advance, so most of the work only needs to be done once) you can work out the general form of the polynomial at those points (so at x=1, you have something like a+b+c+d+e+f+g+h+i+j+k+l+m+n+o+p) which, when you substitute in the y values you receive will give you a bunch of simultaneous equations.
Because of the added structure of these equations all coming from the same underlying polynomial, you can solve them more efficiently by constructing a "divided difference" table - an inverted triangle where the first row is just the original y values, then in each subsequent row, each entry is the difference between the two entries above (right minus left) divided by the difference between the x values corresponding to the base of that entry's triangle (again, right minus left). So, for example, in the second row, below and between y1 and y2, you'd get y1,2 = (y2 - y1)/(x2 - x1), while in the third row, below and between y1,2 and y2,3 you'd get y1,3 = (y2,3 - y1,2)/(x3 - x1).
After filling out the divided difference table, the leading diagonal pretty much lets you read off the coefficients for the polynomial with minimal effort.
It seems complicated to describe, but if you try actually doing it a couple of times, it should all make sense :)
The Lagrange Interpolation Theorem is pretty lit if I do say so myself.
really clever! :)
When you send a point aren't you sending its x and y values i.e. two values.
Or is there a specific set of x for which only y are sent?
In the (very artificial) example used here, the "x" values are the position of the digit. So, always 1, 2, 3, ... ,15, 16. That''s how you can recalculate the correct digit if one of them doesn't fall on the curve (otherwise you would only have error detection, but not correction).
Very clever.
I still don't understand how you would be able to correct the error. I mean after the point has been identified, it could still lie at any point on the curve.
Or are the points sent at a specific interval so that it's just the y intersection at that x coordinate?
As I understand it, the numbers you want at the end are the coefficients, not the points. That's the data you're trying to send. When the receiver looks at the points, and sees one is wrong, they can just discount that one because they will still have the coefficients derived from the other 17 points. There's no need to calculate what the false point should've been.
You'd have an agreed set of x co-ordinates, and just send the y values.
Wouldn't this error correcting method not work as well with large amounts of data?
Yes, if your symbol error rate is constant you get more errors as you send more data.
To counter this, the data is split into chunks and the codes are applied chunkwise to get reasonable error rates.
can someone explain to me how this works if multiple "points" were incorrect? or do you just assume the message has errors and try sending again?
and i guess in the real world what is actually being "sent over the wire"? Is it the coefficients of the polynomial plus the y-coordinates? is it the (x.y) coordinates of the points? Don't you have to know the polynomial (say, via the coefficients) so you can do the inversion to get/correct the original inputs if they errored?
how can I construct the polynomial if I only have the y-coordinates?
Is the fact that the maximum number of points you can interpolate using a quadratic is 3 related to the fact that the maximum number of points you can interpolate with a circle is also 3?
Would this still work if more than a single number is wrong?
Very reminiscent of Shamir's Secret Sharing Scheme, by which a secret bitstring (such as a private key) can be recovered from any M of N "shares," where no subset of M-1 shares tells you anything about the secret. It's the same deal: you can't find the original polynomial (and thus the secret) unless you know at least a number of points equal to the degree of the polynomial plus 1.
"If I send it three times..." that made me think of a movie, "Minority Report".
7:51 Will the paper be on eBay again?
Error detection is built in TCP protocol, part of Internet, used by the web.
Why Lagrange interpolation polynomial and not the Chebychev’s one?
That is very clever
it feels very generatingfunction-y. like the polynomial you use to encode your numbers is the same as the ordinary generating function for if your numbers were a sequence with the last number being the zeroth value of the sequence
Imma guess that 3B1B's Hemming Code videos are gonna be useful in my being able to follow along with this video lol
Some of the basic ideas are the same, but the details of Hamming Codes are different enough that they'll be strictly limited in their utility.
What happens if more than one point is erroneous?
What if all the points are shifted by 1 for example
Then your code can't compensate. For your specific example, you'd get the same coefficients except for the constant term, which would be one higher. Fortunately, credit card numbers have their own error-detection built in, so you'd still be able to tell something went wrong in this example.
In general, error detection and correction can only go so far - there's always a chance of random noise happening to produce a valid signal, and the best anyone can do is reduce the chances of that happening to an acceptable level.
If you have a perfectly noisy channel - one where what's received is totally random, with no relationship to what was sent - then no possible code could ever help.
1 thing I don't really get: what if a credit card number is random, but doesn't look random? Like mine. The first 4 numbers specify the type of card, so you basically have 12 random numbers. According to this video, you can put my "random" numbers on a 5 or 6 degree polynomial. If I would have been a computer and someone would have send me my own credit card number, I would specify it as "made up" because it doesn't look random to me. How does a computer determine that it is in fact random?