My own experience with academia has lead me to believe that there problems that are truly difficult and complex, but there are also problems which are described as difficult because so many people were exposed to the topic through a resource (typically another person) who had little experience (or interest) in effectively teaching the subject.

As an adult having these personalities enthusiastically breaking down the fundamentals and using analogies to help cement the concepts intuitively has been a taste of what I imagine having a brilliant and engaging math professor would be like.

I used to homeschool and one of my sons had terrible baggage about math by the time I pulled him out of public school when he was 11. And somewhere along the way, I learned that a lot of elementary school teachers are women who weren't doing well in math and got encouraged to go become an elementary school teacher because, basically, sexism is alive and well and the world goes "Oh, I know what a woman who can't cut it in college is qualified for: Dealing with small kids all day!"

So a lot of elementary school teachers are women who suck at math and were also never told 'Get over it and get a degree you want.' Instead, they were told 'Oh, go teach kiddies. That's what you are qualified for.'

And I know this is a gender difference in terms of the world treating men and women differently -- a social difference, not a biological one -- because I'm a woman who is good at math (or was at one time) and was married to a man who was terrible at it. No one ever told him (a career soldier) "Oh, go become an elementary school teacher!"

Instead, he got his wife to tutor him in college math even though he found it so difficult he threw his pencil across the room one day when I showed him the right way to do it and then I told him "You can be nice or go find another tutor."

For a long time, being a drill instructor (the guy yelling at you in boot camp) was considered a low status position and therefore a career dead end. This in turn led to the DI spots being filled primarily by people who were in trouble, didn't care or were "bad apples".

At some point, the questions were raised: "Wait, why are we putting some of our worst Marines to train new Marines? Wouldn't it it make more sense to have our BEST Marines train the next generation?". The follow up was then: so how do we actually make that happen?

The answer was to make being a DI one of the stepping stones to higher level ranks. This dramatically increased the quality of DIs and in turn, raised the quality of the training and finally the Marines.

There is a similar story in some countries with socialized medicine. E.g. to get a license to practice medicine privately, you first have to spend N years working in a public hospital. This ensures that you can get top quality people working even in a public healthcare based system.

I hear your frustration, and I’m glad that there has been progress in this regard. However I think that what the GP was saying and what you’re saying are two different issues:

The GP is referring to particular teaching styles which might be overly formal (in the mathematical sense) or declarative (this is convolution, that’s how it is, remember it) as opposed to intuitive (which might not convoy the whole truth but helps some if not most people understand and remember the concept better). This issue can and often takes place in higher education where the teacher or lecturer is expected to be highly qualified.

I remember being frustrated with the way my linear algebra and multivariable calculus classes were taught. Both generalize to n dimensions but many operations and theorems can often be understood intuitively in two or three dimensions. The courses I took were extremely formal with not a minute spent on the graphical interpretation which would have helped so much. When I raised the issue with two separate professors, both were of the opinion that the graphical approach was frivolous because it wasn’t precise. But that argument misses the point of the lecture theatre space ...

People who think a thing is hard and must be dealt with in a serious manner will use formal methods to ward off their fear of the topic or their fears of social perception. It takes a great deal of confidence to say "We are just going to have fun today and play with this wonderful topic!"

They exist, but they aren't the norm.

And they teach it to really young kids who are very impressionable and don't have some means to avoid being brainwashed into believing it.

My one and only goal with homeschooling my son was to get him past the math phobia he had developed and teach him "Math is your friend." He's genuinely bad with numbers, but he no longer has a math phobia and has a better grasp of the concepts than a lot of people, even though he can't calculate anything in his head and routinely asks me or his brother to calculate it for him.

A math phobia is crippling and many people never get over it.

The only kind of complain that I have ever heard (and very rarely so) is because some of this material is supported by videos in a platform with shitty terms of service.

Wrong example, lol

On the other hand, it makes people believe that they have understood what's there to understand and move on. In a sense, they contribute to the Dunning-Kruger effect.

The article illustrates this quite well. The intuition is presented well, but is no substitute for actually learning math. The first two questions in the end:

> Why is one function reversed?

> Why is convolution commutative?

With having studied math "properly", it's immediately obvious that they are answers to each other. Defining it with the "reversal" on one function is what makes the operation commutative by introducing a somewhat hidden symmetry and that's a quite common trick in mathematics.

The other two questions are also quite clear with some actual math background. Not M.Sc. level math, just some proper calculus. You just need to sit down and do the math, which can take half a page of pen-on-paper. That's what math is, you need to work it.

Again, don't get me wrong, I appreciate the effort. But those types of posts don't make you more fluid in math than reading a popular science article about Alpha Centauri makes you an astronomer or an expose about Covid-19 makes you an epidemioloist. It's popular science and can't replace the actual study of the subject.

Innovation is the result of very particular institutions and resources being coordinated and people networking and clustering tightly, which is why, despite incredibly dispersion of knowledge over the internet, innovation and VC money actually still is so geographically concentrated.

I think these online learning resources are good but the expectations should be correct, they're not tools to kickstart innovation, which is much more tacit and reliant on close interaction between people rather than knowledge.

It inspired me enough that I started creating a DMOZ-style catalog of learning resources as a FOSS project: https://github.com/learn-awesome/learn

nobody alerted me to these

> I remember as a graduate student that Ingrid Daubechies frequently referred to convolution by a bump function as "blurring" - its effect on images is similar to what a short-sighted person experiences when taking off his or her glasses (and, indeed, if one works through the geometric optics, convolution is not a bad first approximation for this effect). I found this to be very helpful, not just for understanding convolution per se, but as a lesson that one should try to use physical intuition to model mathematical concepts whenever one can.

> More generally, if one thinks of functions as fuzzy versions of points, then convolution is the fuzzy version of addition (or sometimes multiplication, depending on the context). The probabilistic interpretation is one example of this (where the fuzz is a a probability distribution), but one can also have signed, complex-valued, or vector-valued fuzz, of course.

[0] https://mathoverflow.net/questions/5892/what-is-convolution-...

Understanding convolution different contexts: geometry, vector algebra, statistics, signal progressing, control theory, functional analysis .. etc. just deepens the understanding.

Instead of finding one definition and then seeing everything trough it, one should try to find how different viewpoints are the same and learn to switch.

To convolve : to roll together : writhe.

Convolution : a form or shape that is folded in curved or tortuous windings.

I first encountered it as a young gamedev hobbyist, doing image post-processing in OpenGL.

One kind of kernel makes images blurry, one (attempts to) make them sharp.

It doesn't bring folding to mind at all. And in FP, "fold" means "reduce".

How much of dyscalculia is just one person thinking an analogy is completely obvious and the other person thinking it makes no sense?

https://commons.wikimedia.org/wiki/File:Cross_correlation_an...

Any kernel with positive values will blur the image, and the effect will be quite similar to Gaussian blur (by the Central Limit Theorem).

It's kernels with positive and negative coefficients that may "enhance" or "sharpen" the image.

Can anyone elaborate on this?

Correlation is a generalized dot product: multiplying corresponding pairs of values from two signals, and then adding the factors together. The result is zero if the signals are orthogonal (like the dot product of two vectors in 2D or 3D that are at 90 degrees).

The intuition behind the reversed signal comes from processing in the time domain. There are application in which we convolve a new signal with a segment based on a captured historic signal. That function is reversed so that the newest sample from the new signal correlates with the least recently captured sample in the historic signal.

For instance, we can use an impulse signal (like a clap) to capture the echoes from an acoustic space. That signal can then be convolved with audio data in order to give it a reverberation similar to what would be produced by that acoustic space. The captured impulse response has to be reversed, because echoes come in reverse. In the impulse capture, the longest echo is found on the far right. When we apply the impulse to simulate the echo, it has to be on he far left, because it has to correlate with an old sample of he input.

E.g. if we are at t = 0, and want to hear the 500 ms echo, that echo corresponds to a past sound sample that is now at t = -500. When are capturing the impulse response, then we issue the gunshot or clap at t = 0, and the 500 ms echo comes at t = 500.

The earliest reflections have to apply to more recent signal, but they are based on the least recently captured data.

This. Convolution is a function which results from applying to convolution operator to two functions. A dot product is nothing of the sort.

Its nearly the same thing, isn't it? If you denote by Tx the left-shift operator defined by (Tx f)(y) = f(y+x), then the correlation of f and g evaluated at x is precisely the dot product of f and Tx g. If you evaluate your function at a certain point, you obtain a scalar product.

It really isn't the same, and oddly enough you unknowingly show that off, by mentioning that convolution is the function that maps input functions to the output function, but the dot product is at best a single point evaluated with the output function.

I don't think it's true, cannot even imagine how it's possible, especially for long signals, like the whole song.

Any links to read about.

Here is a bit more rambling in the direction of linear algebra and probability: you can define a vector space on the set of all random variables, wherein each random variable is a vector. Once you do so, you can further impose an inner product space with the traditional covariance function as the inner product. If the two random variables are orthogonal with respect to this inner product and this space, by definition their covariance is equal to 0. It follows by the definition of correlation from covariance that their correlation will also be 0, i.e. orthogonality implies two variables are uncorrelated in this space :)

Strictly speaking the correlation function is not the inner product in this instance, but practically the result is the same.

Interesting - when I learnt it, convolution came first.

I like the visual 'sliding' explanation best. Perhaps partly because that's what I was shown, but also because, I think, how else would you explain any integral? I mean, you could, but would you?

Even to a blind person (so I couldn't draw a diagram) I'd explain integration as 'picture a graph ... it's that bit ... it's adding this and this and this ... just like when you multiply two sides of a rectangle ...'

i.e. if the signal is a (+)slope /, convolving it with a reverse of that signal \ = 0

In this sense you are leaving the difference between 2 signals.

Fast convolution algorithms use FFT and run in O(n log n) time, which is why fast multiplication algorithms are based on FFT and why everyone was looking for O(n log n) one, which was found like a year ago.

(Though from what i can tell in "Faster Convolutions" section the article claims you can easily build O(n log n) multiplication using FFT which doesn't work as it ignores carries)

The trick is to only put as many bits into each number as are guaranteed not to overflow. So if the convolution is n long and you put B bits in to each "digit" then the output needs to be able to hold log(n)+2*B bits.

So if your convolution is done with double precision with 56 bits of precision and n = 1 million, log(n) = 20 so you put (56 -20)/2 = 18 bits into each "digit". You do your convolution, and ripple the carries at the end.

This is the way Prime95 does its maths, though it uses a IBDWT which through a bit of extra maths magic halves the length of the FFT needed. While it is rippling the carries at the end it checks to see if each number is pretty nearly an integer - if it isn't them something has gone wrong and it blows up with "FATAL ERROR: Rounding was 0.5, expected less than 0.4".

https://hal.archives-ouvertes.fr/hal-02070778/document

Basically, you can't. It's the overflows that make it so hard. It's complex, even though we all had the intuition that it should be done (and it should be simple to do)

If this is allowed by your model of computation, then you could use this magical O(1) addition to to implement multiplication in O(n).

How's that?

    let X = to_magic_integer("abcde")
    let y = "fghij".reverse()
    let S = to_magic_integer("0")
    for d = 0 to 4
        for i = 1 to y[d]
            S += X
        end
        X = X+X+X+X+X + X+X+X+X+X
    end

    return S

> addition operations of numbers with log(n) bits deemed to take O(1) time.

not

> addition operations of numbers with n bits deemed to take O(1) time.

> addition operations of numbers with an arbitrary number of bits deemed to take O(1) time

?

I was picturing a more restrictive model of computation in which your claim is along the lines of

> There's an O(n)-time algorithm, which, given two numbers with n digits and a magic black box that can add log(n)-digit numbers together in O(1) time, will output the product of those two numbers.

So if your model of computation lets you look things up in a table like that in O(1) time, you can kind of get the situation I'm describing above. Maybe your model of computation shouldn't let you do this, but some do. Often if an algorithm takes up O(foo) space, we don't worry about the fact that lookups into that space should take Theta(log(foo)) time (to even read the address) rather than O(1) time, because things are assumed to "fit into memory".

Another fun way to be fussy along these lines is that the usual algorithms for sorting a list of n distinct items only give bounds of O(n (log n)^2) time. Each item takes around log(n) bits to even store, so comparing two of those can't be done in time O(1) in the worst case (in a model that forbids the kind of "cheating" I'm describing above). The best uniform bound you can hope for is time O(log n) per comparison. (Though in some sense the extra log(n) factor might "drop back out", because it takes O(n log(n)) space to even write down an input to this algorithm.)

It really isn't. Just because you see different digits being multiplied that doesn't mean you're convolving functions.

That serves as a less magical introduction to the operator. You can then point out that the polynomials whose coefficients one convolves can be considered power series, which has a nice interlude into the Z transform and its usefulness as an analytical tool when working with convolutions (and then on to the Fourier transform, etc).

100101 * 23 = 2302323. If you defer the carry over to the end, the digit-based steps we do is convolution. That said, giving lots of examples like those in the article is useful.

Next up looking at multiplying two polynomials in a single variable.

Edit: changed digit-wise to digit-based to make it clear that I'm not asking to multiply corresponding digits.

  f(0) = 1      g(0) = 3      f*g(0) = 3 
  f(1) = 0      g(1) = 2      f*g(1) = 2 
  f(2) = 1                    f*g(2) = 3 
  f(3) = 0                    f*g(3) = 2
  f(4) = 0                    f*g(4) = 0
  f(5) = 1                    f*g(5) = 3 
                              f*g(6) = 2

This is cool. It literally is the same, if you use base-infinity digits (not binary, or decimal, but infinity-ary?).

How do you multiply two polynomials with a single variable?

Roughly, in base 10, the number represented by the sequence of digits d_0, d_1, ..., d_n is d_0 + d_1 * 10 + d_2 * 10^2 + ... + d_n * 10^n.

In "base infinity", the "number" represented by the sequence of digits d_0, d_1, ..., d_n is the polynomial d_0 + d_1 * x + d_2 * x^2 + ... + d_n * x^n.

Multiplying these "numbers" (polynomials) is like decimal multiplication, except the digits can be arbitrarily high and you do no carrying at all.

I don't agree the example has anything to do with convolution. The example is explained with plain old primary school multiplication.

100101 * 23 = (100000+100+1)*23

         = 2300000+2300+23

Convolution is not a number. Convolution is an operator that outputs a function.

Set x=10 and you're dealing with decimal representation of numbers.

...and you no longer have a function, only the image of a function at a specific point of its domain.

Convolutions are functions, not functions evaluated at a specific, and cherry-picked point.

It isn't. It's a cherry picked example. A broken clock which coincides with the current time.

No. It really is. Look at the example.

Nobody can convince you if you are already set up against that idea, but look at what happens when you multiply two polyomials P(z)Q(z). You obtain a polynomial whose coefficients are the convolution of those of P and Q. Now, a decimal number is "just" a polynomial evaluated on z=10. And a vibrating string is "just" a polynomial evaluated at z=exp(it). The algebra of convolutions and products is exactly the same in all three cases.

A more 'convolved' example:

121 * 23 = 2783

If you use hex instead it also works for larger digits like 0x42 * 0x31 = 0xCA2 = [12, 10, 2]

So, if you convert to a digit of a large enough base you can do any multiplication with just a convolution and no carry.

Aren't you restating my point?

> So, if you convert to a digit of a large enough base you can do any multiplication with just a convolution and no carry.

There is no convolution at all. There's only a cherry-picked example of how plain old multiplication feels similar to a sliding dot product, which for some reason some people confuse with convolution. This is not helpful, and only adds to the confusion already expressed.

You’re wrong to suggest that elementary-school-multiplication and (discrete) convolution are so dissimilar that the analogy is so unhelpful that it only adds to everyone’s confusion. Maybe to yours, but not to everyone’s.

If I understand your objection correctly (other than the already conceded fact that the analogy does not work in case of multiplication with carrying), you already have internalized that convolution is an operator on 2 functions, resulting in a function. And since you have already internalized that, the analogy with any operator on two natural numbers resulting in a natural number confuses you — although I assume you can see the similarity in the sliding operation.

For people who have not internalized what a convolution is, the visualisationg of the sliding operation, and the analogy with elementary-school-multiplication is the helpful bit.

To clarify why the operations are really similar: consider a natural number as a function that takes a natural number as argument, and returns the digit (between 0 and 9 inclusive) for that decimal position.

So f = 2 031 is considered to be: n → f(n) = if n = 3 then return 2 else if n = 1 then return 3 else if n = 0 then return 1 else return 0 (end if).

And g = 320 is considered to be: n → g(n) = if n = 2 then return 3 else if n = 1 then return 2 else return 0 (end if).

What is then the discrete convolution f ∗ g?

It’s f ∗ g = n → (f ∗ g)(n) = f(3) × g(n – 3) + f(1) × g(n – 1) + f(0) × g(n)

= n → if n = 5 then return 6 else if n = 4 then return 4 else if n = 3 or n = 2 then return 9 else n = 1 then return 2 else return 0 (end if).

This is how we consider 649 920 = 2 031 × 320.

They are way more than dissimilar, they are radically different concepts altogether.

Convolution is a function, or an operator that outputs a function given two input functions that share the same domain.

Plain old algebra over real numbers is nothing of the sort.

You're trying to compare a function, with its domain and codomain, with an operation between scalars that results in a scalar. It's way more than apples to oranges. It's apples to orange tree plantations.

This is not a large conceptual chasm. It is boilerplate for actually talking about decimal (or binary or hex or whatever) representations of numbers. Here is one version of that boilerplate, spelled out:

Think of a base-10 representation of a natural number as a function with domain N (the set of natural numbers) and codomain {0, ..., 9}, where f(0) is the ones digit, f(1) is the tens digit, f(2) is the hundreds digit and so on. (This function will be finitely supported, i.e. all but finitely many inputs to this function will give output 0.)

If f and g are the representations of two numbers n and m, then one can say the following about the representation of their product n * m:

  (1) Extend the codomain of f and g, to N, i.e. think of them as functions N -> N instead of functions N -> {0, ..., 9}.
  (2) Compute the convolution of those two functions, giving you another (still finitely supported) function h: N -> N. Usually at this point h will have values that are larger than 10.
  (3) Do all the carrying (e.g. repeatedly take the first n for which h(n) > 10, and subtract 10 from h(n) and add 1 to h(n+1)). Now all the values of h are in {0, ..., 9}, so you can think of h as a function N -> {0, ..., 9}.

Elsewhere in the thread you seem to be suggesting that convolution is a way to convert between a frequency domain and a time domain, which suggests that you are confusing convolution with Fourier transforms. There's some nice relationships between these things, so they are often discussed together, but even then a single convolution happens either entirely in the time domain or entirely in the frequency domain, not as a way of going from one of those domains to the other. E.g. the pointwise product (in the frequency domain) of the Fourier transforms of two functions is the Fourier transform of the convolution (in the time domain) of those two functions.

There's also a chance you are focused on the difference between functions with a discrete domain and functions with a continuous domain. The term "convolution" is often applied to both.

Btw that isn't true. That operation is called a "transform" as in "Fourier transform" and "z-transform". The property of those transforms is that element-wise multiplication in one domain becomes convolution in the transformed domain. Element-wise multiplication in "time" domain is convolution in "frequency" domain and element-wise multiplication in "frequency" domain is convolution in "time" domain.

Further to that, if you replace x with complex variable z^-1, you get what is usually called the z-transform. Set z to a specific complex root of unity exp(i2pi/N) and you have your discrete FT.

One of the algorithms for multiplying large arbitrary precision numbers uses multiplication of the discrete transform of the digit sequences (in a different base) iirc.

Same thing.

Most of the time the actual stuff happening is quite simple, but if you're not living and breathing mathematical notation the conventional explanations can be quite impenetrable. This book puts everything out in code, so you can follow the steps and truly understand what's happening.

http://www.dspguide.com/

But if we collapse along the lines x+y = v, we obtain the convolution f⋆g(v).

This picture is a little more advanced, but it makes clear two key properties of convolution: symmetry (commutativity) and the fact that the total integral of the convolution is the total integral of f(x)·g(y).

This has applications in quantum mechanics where non-commutativity plays a prominent role.

It's certainly a useful mental model.

In order to explain something simply, you usually have to lie a little bit — but I speculate that those little lies bother the contributors to mathematical Wikipedia articles a great deal. So they correct those little lies, making the articles more accurate but less useful for many of us.

I realize convolution is commutative ("Part 3"), but it tripped me up a little as I tried to track the narrative.

It’s not a “list of patients” either, which had me thinking they were patient ids. A better phrase would be a “list of patient counts”.

1. https://en.wikipedia.org/wiki/Convolution_theorem

But I still feel like this kinda obscures and confuses the origin of the reversal.*

If you don't understand the convolution formula instinctively, read this comment enough times till you do.

The point is this.

-We have a function originBangSound(t) that maps the effect at (t) of an impulse or "bang" coming from the origin (t=0).

-We have a function bangWeights(t), which measures the distribution of impulses or "bangs" over time.

-The question is: how do we get the total allBangSounds(t)?

Simple: We make every point in time the origin, and add all the results together. Let's call (tau) the current origin. The size of the bang at this origin is bangWeights(tau). The size of the sound at (t) which is coming from this origin is originBangSound(t- tau), since we care about the position of (t) relative to the current origin (tau). Adding them up leads to an integral in the continuous case.

  allBangSounds(t) = \integral bangWeights(tau)*originBangSound(t-tau) d(tau)

Here's a nice sanity check: if (tau) is larger than (t), (or equivalently, (t-tau)<0) then do you expect to hear its bang? Ofcourse not. The bang hasn't happened yet. So unless its bang travels backward in time (which definitely does happen in spatial convolutions!) you ain't hearing it.

_____________________________

*Come to think of it, this is a very useful pun. When you think to yourself "what's the origin of the reversal again?", just remember, the moving the origin is the origin of the reversal.

I have written something on adjoints, they are literally everywhere https://github.com/adamnemecek/adjoint

True, its just the lack of mirroring that differs, which is a linear operation and hence, the network can be considered too have learned the mirrored kernels, but it matters for initialization(bilinear init for conv^T sampling is still incorrectly mirrored in pytorch), and every time someone has put the images of the kernels in a paper its better than even odds they show the correlation, not convolution kernls.

Everyone should introduce convolution that way.

The general idea is there is a sense of taking mathematical operations as having not just numbers and variables and outputing another number or variable... but that operations can take any number of anything and output any number of anything. The Fourier Transform takes in a function and outputs another function, which you can put meaning on it or you can just "nod and continue" (which is often easier instead of trying to put some words on abstract concepts).

Some "ideas" you can use to analyze the situation are concepts learned in things like linear algebra like abstract vector spaces, basis, eigen-whatever... and so on.

In the case of the continuous time convolution, it just turns out there is a magical thing associated with something called a Dirac Delta function with certain properties that are important for various things in controls engineering.

Kinda feels like a piece of turd that refuses to evacuate but I just stopped caring about "intuitive" explanations.

Is it a standard high school topic for AP math types in high school nowadays? If not, why not? There are a few topics like that, for which I’d gladly give up high school teaching (or learning) L’Hôpital’s rule for.

Convolution is neither a valid example of an inner product or an outer product. No basic geometric operation on vectors has the correct type signature and axioms for convolution to be interpreted as a generalized "x". What we'd be looking for is a billinear mapping of vectors to vectors, and complex number style multiplication in R^2, or cross product multiplication in R^3 or R^7 are the only real candidates in that category unless we start interpreting functions as matrices.

Integrating over t can be visualized as sliding g over f over the range of t. Result is a function of x as t has been integrated out. It all made sense to me when I thought of it as sliding one function over another fixed function.