Automatic differentiation in Ruby

Automatic differentiation in Ruby

Hi. Im going to briefly talk about what
automatic differentiation is and how to do it in Ruby. Automatic differentiation is a clever trick
that makes it easy to do differentiation on a computer. Its a beautiful fusion of maths
and computer programming, and I want to tell you about it so you can enjoy how satisfying
it is.

So, what is differentiation? Its a mathematical
word for something we do to functions, so first we need to talk about what a function
is. A function really just means a relationship
between two quantities  for example, the relationship between the current time and
the distance travelled by a car. One relationship between time and distance
might be cruising along the road at a constant speed, like the red car. A different relationship
is pulling away from stationary, like the green car  it starts slowly and speeds

Each car has its own function that relates the distance its travelled to the current
time. If we plot time horizontally and distance
vertically, the function of the red cars distance over time looks like this  a straight
line. And the green cars function looks like this  a curve. If we periodically measure the red cars
distance we can get a sense of how it relates to the current time, and in this case we can
see that the distance is actually equal to how many seconds its been travelling for.
After three seconds its travelled three metres, for example.

If we measure the green cars distance,
it turns out its the time multiplied by itself. After three seconds its travelled nine
metres. Differentiation is the process of finding
out how fast a functions result is changing, and to keep this video short, youll just
have to believe me that this is a useful thing to be able to do on a computer. So if we take a distance function and differentiate
it, we get another function that tells us how fast the distance is changing, which in
physics we call speed.

So we know how far the green car has travelled
at each point in time, but differentiation can tell us how fast its going at each
point in time, which is otherwise not obvious. We can see visually how fast a function is
changing by graphing it and looking at the slope of the line. The red cars distance
function makes a straight line. Lets say the slope is one metre per second: for every
one second you go across, it goes one metre up.

If the car travels twice as fast, the line
is steeper. We can call that slope two metres per second. And if it travels half as fast, we get a less
steep line, say half a metre per second. So steeper means faster, shallower means slower.

The speed of the green car changes over time.
Initially its speed is zero, but then the curve gets steeper, and then even steeper
as its speed increases. So if we know these distance functions, how
do we get a computer to calculate the speeds? There are two traditional ways of doing it.
The first is called symbolic differentiation. The idea is to start with the expression of
the function you want to differentiate  for example, time multiplied by time  and work
out an expression for the differentiated function. If you did any calculus at school, youll
know that mathematicians have a load of rules for how to do these manipulations.

a whole Wikipedia page full of them! Theres the constant factor rule, the sum
rule, the subtraction rule, the product rule, the chain rule, the inverse function rule,
the elementary power rule, and so on. To do symbolic differentiation, we represent
the expression as a data structure inside the computer  as a tree of symbols  and
get the computer to manipulate that structure according to all those differentiation rules
and the rules of basic algebra. Firstly, we teach the computer that time
multiplied by time can be written as time to the power of two, or time squared.
And then one of the differentiation rules, the elementary power rule, says: something
to the power of n differentiates to n multiplied by that something to the power of n minus
one. So time to the power of two differentiates
to two multiplied by time to the power of one, which is just two multiplied by time.

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Now we know that speed is two multiplied by
time, we can compute the speeds. So the big picture is: from the expression
for distance, the computer uses some rules to work out the expression for speed, and
then evaluates it for different inputs. That gives accurate answers but its difficult
to do. Also, in general, the size of the differentiated expression can be much larger than the size
of the original expression, so it takes longer to evaluate.

The other traditional strategy is called numerical
differentiation. To do that, instead of representing the functions
expression as a data structure, we implement it directly as a program. Heres the green cars distance function
in Ruby. The idea of numerical differentiation is that
we can guess the rate of change at a particular value by picking a nearby value and looking
at the slope of the line that connects them.

The slope of that line doesnt quite match
the curve, so its only an approximation. On the other hand, if we choose an even closer
value the approximation gets more accurate. Itll never be perfect, but if we pick a
small enough distance between the two values, the line between them matches the slope of
the curve quite closely. So heres how we implement that.

To get
the rough speed at a particular time, we pick a really short amount of time  say, 0.01
Seconds  then calculate how much further the car travels in that time, and divide that
distance by the amount of time elapsed. We dont need to reimplement this for every
function we want to differentiate; we can make a higher order function to do it, or
do it with metaprogramming or whatever. Heres the results we get. Theyre extremely
close to the exact answers we got from doing it symbolically.

With this technique we dont have a simple
expression that tells us the speed directly, but we do have an algorithm for approximating
it. This is easier and faster than the symbolic
method, but its only approximate, and dividing by tiny quantities makes it susceptible to
rounding errors. So weve seen the traditional strategies:
symbolic and numerical differentiation. Automatic differentiation is a less well-known technique.
Its as accurate as symbolic differentiation but as fast and easy as numerical.

The big idea is to calculate a functions
rate of change and its value all at once. So rather than just putting a time into our
distance function and getting a distance out, we should put in a time and a rate of change
for example, we specify that time is changing at one second per second  and get out a
distance and a rate of change  for example, we get the result that the distance is changing
at six metres per second. One way of doing this is to invent a new kind
of number that can represent a value and its rate of change all at once, and then make
our implementations of functions work with these new numbers. So we need to design and
then implement a sort of two-dimensional number that can store both a value and its rate of
change and operate on them together.

One kind of two-dimensional number you might
already know about is the complex numbers, which were invented to solve a similar problem.
They have two components, a real part and an imaginary part, but they can be added and
multiplied and otherwise used like normal numbers. The imaginary part of a complex number is
a multiple of this invented number i  its just a symbol, it doesnt represent a real
quantity. Our two-dimensional numbers will use a similar
idea, but theyre not complex numbers, theyre called dual numbers. They have a real
part and a dual part, and instead of i we use an invented number called epsilon.
Its just a symbol again, but you can think of epsilon as representing a very very tiny
number, almost zero but not zero  its called an infinitesimal.

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Well use this dual part to represent the
rate at which a number is changing. Epsilon is so tiny that it doesnt contribute to
the real value of the number at all, but we can say a number with ten epsilons is changing
twice as fast as a number with five epsilons. So lets just make a boring class for dual
numbers, with accessors for the real and dual parts, and a string representation. And add a convenience method to Kernel for
converting normal numbers into dual numbers, like the Complex and Rational classes have.
If we dont specify a rate of change then it defaults to zero  that gives us a dual
number that represents a constant, not changing at all.

So for the red car, where the distance is
just equal to the time, we already have something that works. We make a value three with rate
of change one, and when we call the distance function with it, we get out three with rate
of change one  that means three metres, one metre per second. And thats the right answer for this very
simple function where distance is the same as time. But what about when the function does some
operations on the input value? If youve worked with complex numbers you
might know that we can define how to add and multiply them, and so on, just by doing normal
algebra with their parts.

But to do that we need to know how i behaves, and by design
the rule is that i times i equals minus one. That choice makes complex numbers behave
in a certain way; theyre good for representing rotations and oscillations and things like
that in physics and electronics. We can add together two complex numbers by
just adding all their parts separately and grouping the multiples of i together. And when we multiply two complex numbers we
multiply out the brackets and group the multiples of i; were multiplying by i squared
at the end here, and that just turns into multiplying by minus one because thats
what i squared is, so thats just a subtraction; and then we group the multiples again.

We can do exactly the same thing for dual
numbers too, but this time we need to know how epsilon behaves. The rule for epsilon
is that its so small that when you multiply it by itself you get zero. And then all the
algebra follows in the same way. Adding dual numbers is literally the same:
we add all the parts and group the multiples of epsilon.

Multiplying is almost the same: we multiply
out the brackets and group the multiples, but now were multiplying by epsilon squared
at the end, and epsilon squared is zero, so that last part just goes away. We can use those results to implement plus
and multiply on our DualNumber class. This is where the mathematics meets the programming. Now if I have two dual numbers, I can add
them and multiply them.

So for the green car, where the distance is
time multiplied by time, we can take that three seconds changing at one second per second,
ask for the distance, and get the result: nine metres changing at six metres per second. And thats the answer we wanted. It just
works, because we built the idea of the rate of change into our numbers, and defined operations
that preserved and respected the rate of change. Here I fed different time values into the
green cars distance function and visualised the dual numbers it generated.

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The real values
are plotted in white, and the slope of the red line is the dual part  the rate of
change for the value where the dot is. So you can see that matches the slope of the
curve nicely. We have to do a bit of work to make dual numbers
compatible with Rubys built-in numbers. Right now if we try to add or multiply by
a Fixnum we get a NoMethodError, because our add and multiply operations are trying to
get the real and dual parts of a Fixnum.

We can fix that by just converting the argument
to a dual number inside the add and multiply methods. This is where that DualNumber conversion
method comes in handy. And now we can add and multiply by normal
numbers. They get automatically converted into dual number constants.

Of course we have the opposite problem, too,
if we have a Fixnum on the left and try to add or multiply by a dual number on the right.
This time instead of a NoMethodError we get a TypeError, because Fixnums add and multiply
operations dont know what to do with a dual number. Fortunately Ruby has a built-in way of coping
with this. If we call a numeric operation of a built-in number with an argument whose
type it doesnt recognise, the number will send a coerce message to that unrecognised
argument, and expect to get back a pair of objects. Then it will retry the original operation
with those objects, and whatever result it gets back, it will return that.

This coercion
protocol means built-in numbers can interoperate with user-defined ones. So if we implement this coerce method on the
DualNumber class, we can make built-in numbers automatically upgrade themselves to a dual
number whenever we try to add or multiply by a dual number. And now addition and multiplication work both
ways around. Of course, you can do more with numbers than
just add and multiply them.

With a bit more algebra its possible to work out definitions
for all sorts of other operations on dual numbers too. For example, these are the formulas
for taking the sine, cosine, natural exponential, natural logarithm and square root of a dual
number. Those formulas are easy to monkey patch into
the Math module. Here Im overriding sin and cos in the Math module to use the appropriate
formulas if theyre called with a dual number, otherwise they call super to use the original

And now we can take the sine and cosine of
dual numbers, and combine them into larger expressions. So just to finish, if I define my distance
function to be the sine of the time, then heres the graph of the real and dual values
that my Ruby code produces. Hopefully youre convinced that the slope of the line is correct. And we can compose as many operations as we

Heres a distance function that does sine and cosine and multiplies and adds and
divides, and here are the values and rates of change produced by that code. Heres a final example: time multiplied by the
sine of time squared plus one looks like this. So thats it. A clever little trick that
works by inventing a new kind of number, defining operations on it that preserve its meaning,
and passing it into existing functions to see what they do to it.

The Ruby dual number
implementation I built for this video is up on GitHub, or you can install it as a gem
if you want to play around with it. Thanks very much!.

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