In this essay, we are going to look at recursive algorithms, and how sometimes, we can organize an algorithm so that it resembles the data structure it manipulates, and organize a data structure so that it resembles the algorithms that manipulate it.
When algorithms and the data structures they manipulate are isomorphic,^{1} the code we write is easier to understand for exactly the same reason that code like template strings and regular expressions are easy to understand: The code resembles the data it consumes or produces.
We’ll finish up by observing that we also can employ optimizations that are only possible when algorithms and the data structures they manipulate are isomorphic.
Here we go.
GEB Recursive, © 2006 Alexandre DuretLutz, some rights reserved
an exercise: rotating a square
Here is a square^{2} composed of elements, perhaps pixels or cells that are on or off. We could write them out like this:
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚫️⚪️⚪️⚪️
⚪️⚪️⚫️⚫️⚫️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
Consider the problem of rotating our square. There is an uncommon, but particularly delightful way to do this. First, we cut the square into four smaller squares:
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚫️⚪️⚪️⚪️
⚪️⚪️⚫️⚫️ ⚫️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
Now, we rotate each of the four smaller squares 90 degrees clockwise:
⚪️⚪️⚪️⚪️ ⚫️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚫️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚫️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚪️⚪️⚪️⚪️
Finally, we move the squares as a whole, 90 degrees clockwise:
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚪️⚫️⚪️⚪️
⚪️⚪️⚪️⚫️ ⚫️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️ ⚪️⚪️⚪️⚪️
Then reassemble:
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️⚪️⚫️⚪️⚪️
⚪️⚪️⚪️⚫️⚫️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️⚪️⚪️⚪️⚪️
How do we rotate each of the four smaller squares? Exactly the same way. For example,
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️
⚪️⚪️⚪️⚫️
Becomes:
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚫️
⚪️⚪️ ⚪️⚫️
By rotating each smaller square, it becomes:
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚫️⚫️
And we rotate all four squares to finish with:
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚪️
⚪️⚪️ ⚪️⚪️
⚫️⚫️ ⚪️⚪️
Reassembled, it becomes this:
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️
⚫️⚫️⚪️⚪️
How would we rotate the next size down?
⚪️⚪️
⚫️⚫️
Becomes:
⚪️ ⚪️
⚫️ ⚫️
Rotating an individual dot is a NOOP, so all we have to do is rotate the four dots around, just like we do above:
⚫️ ⚪️
⚫️ ⚪️
Reassembled, it becomes this:
⚫️⚪️
⚫️⚪️
Voila! Rotating a square consists of dividing it into four “region” squares, rotating each one clockwise, then moving the regions one position clockwise. It brings whirling dervishes to mind.^{3}
Here’s the algorithm in action:^{4}
recursion, see recursion
In From HigherOrder Functions to Libraries And Frameworks, we had a look at multirec
, a recursive combinator.
function mapWith (fn) {
return function * (iterable) {
for (const element of iterable) {
yield fn(element);
}
};
}
function multirec({ indivisible, value, divide, combine }) {
return function myself (input) {
if (indivisible(input)) {
return value(input);
} else {
const parts = divide(input);
const solutions = mapWith(myself)(parts);
return combine(solutions);
}
}
}
With multirec
, we can write functions that perform computation using divideandconquer algorithms. multirec
handles the structure of divideandconquer, we just have to write four smaller functions that implement the parts specific to the problem we are solving.
In computer science, divide and conquer (D&C) is an algorithm design paradigm based on multibranched recursion. A divide and conquer algorithm works by recursively breaking down a problem into two or more subproblems of the same or related type, until these become simple enough to be solved directly. The solutions to the subproblems are then combined to give a solution to the original problem.—Wikipedia
We’ll implement rotating a square using multirec
. Let’s begin with a naïve representation for squares, a twodimensional array. For example, we would represent the square:
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚪️
⚪️⚪️⚪️⚫️
⚪️⚪️⚪️⚫️
With this array:
[['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚫️'],
['⚪️', '⚪️', '⚪️', '⚫️']]
To use multirec
, we need four pieces:
 An
indivisible
predicate function. It should report whether an array is too small to be divided up. It’s simplicity itself:(square) => square.length === 1
.  A
value
function that determines what to do with a value that is indivisible. For rotation, we simply return what we are given:(something) => something
 A
divide
function that breaks a divisible problem into smaller pieces. Our function will break a square into four regions. We’ll see how that works below.  A
combine
function that puts the result of rotating the smaller pieces back together. Our function will take four region squares and put them back together into a big square.
As noted, indivisible
and value
are trivial. We’ll call our functions hasLengthOne
, and, itself
:^{5}
const hasLengthOne = (square) => square.length === 1;
const itself = (something) => something;
divide
involves no more than breaking arrays into halves, and then those halves again. We’ll write a divideSquareIntoRegions
function for this:
const firstHalf = (array) => array.slice(0, array.length / 2);
const secondHalf = (array) => array.slice(array.length / 2);
const divideSquareIntoRegions = (square) => {
const upperHalf = firstHalf(square);
const lowerHalf = secondHalf(square);
const upperLeft = upperHalf.map(firstHalf);
const upperRight = upperHalf.map(secondHalf);
const lowerRight = lowerHalf.map(secondHalf);
const lowerLeft= lowerHalf.map(firstHalf);
return [upperLeft, upperRight, lowerRight, lowerLeft];
};
Our combine
function, rotateAndCombineArrays
, makes use of a little help from some functions we saw in an essay about generators:
function split (iterable) {
const iterator = iterable[Symbol.iterator]();
const { done, value: first } = iterator.next();
if (done) {
return { rest: [] };
} else {
return { first, rest: iterator };
}
};
function * join (first, rest) {
yield first;
yield * rest;
};
function * zipWith (fn, ...iterables) {
const asSplits = iterables.map(split);
if (asSplits.every((asSplit) => asSplit.hasOwnProperty('first'))) {
const firsts = asSplits.map((asSplit) => asSplit.first);
const rests = asSplits.map((asSplit) => asSplit.rest);
yield * join(fn(...firsts), zipWith(fn, ...rests));
}
}
const concat = (...arrays) => arrays.reduce((acc, a) => acc.concat(a));
const rotateAndCombineArrays = ([upperLeft, upperRight, lowerRight, lowerLeft]) => {
// rotate
[upperLeft, upperRight, lowerRight, lowerLeft] =
[lowerLeft, upperLeft, upperRight, lowerRight];
// recombine
const upperHalf = [...zipWith(concat, upperLeft, upperRight)];
const lowerHalf = [...zipWith(concat, lowerLeft, lowerRight)];
return concat(upperHalf, lowerHalf);
};
Armed with hasLengthOne
, itself
, divideSquareIntoRegions
, and rotateAndCombineArrays
, we can use multirec
to write rotate
:
const rotate = multirec({
indivisible : hasLengthOne,
value : itself,
divide: divideSquareIntoRegions,
combine: rotateAndCombineArrays
});
rotate(
[['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚫️'],
['⚪️', '⚪️', '⚪️', '⚫️']]
)
//=>
([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚪️', '⚪️']
])
Voila!
accidental complexity
Rotating a square in this recursive manner is intellectually stimulating, but our code is encumbered with some accidental complexity. Here’s a flashing strobeandneon hint of what it is:
const firstHalf = (array) => array.slice(0, array.length / 2);
const secondHalf = (array) => array.slice(array.length / 2);
const divideSquareIntoRegions = (square) => {
const upperHalf = firstHalf(square);
const lowerHalf = secondHalf(square);
const upperLeft = upperHalf.map(firstHalf);
const upperRight = upperHalf.map(secondHalf);
const lowerRight = lowerHalf.map(secondHalf);
const lowerLeft= lowerHalf.map(firstHalf);
return [upperLeft, upperRight, lowerRight, lowerLeft];
};
divideSquareIntoRegions
is all about extracting region squares from a bigger square, and while we’ve done our best to make it readable, it is rather busy. Likewise, here’s the same thing in rotateAndCombineArrays
:
const rotateAndCombineArrays = ([upperLeft, upperRight, lowerRight, lowerLeft]) => {
// rotate
[upperLeft, upperRight, lowerRight, lowerLeft] =
[lowerLeft, upperLeft, upperRight, lowerRight];
// recombine
const upperHalf = [...zipWith(concat, upperLeft, upperRight)];
const lowerHalf = [...zipWith(concat, lowerLeft, lowerRight)];
return concat(upperHalf, lowerHalf);
};
rotateAndCombineArrays
is a very busy function. The core thing we want to talk about is actually the rotation: Having divided things up into four regions, we want to rotate the regions. The zipping and concatenating is all about the implementation of regions as arrays.
We can argue that this is necessary complexity, because squares are arrays, and that’s just what we programmers do for a living, write code that manipulates basic data structures to do our bidding.
But what if our implementation wasn’t an array of arrays? Maybe divide
and combine
could be simpler? Maybe that complexity would turn out to be unnecessary after all?
Recursive Chessboard, © 2007 fdecomite, some rights reserved
isomorphic data structures
When we have what ought to be an elegant algorithm, but the interface between the algorithm and the data structure ends up being as complicated as the rest of the algorithm put together, we can always ask ourselves, “What data structure would make this algorithm stupidly simple?”
The answer can often be found by imagining a data structure that looks like the algorithm’s basic form. If we follow that heuristic, our data structure would be recursive, rather than ‘flat.’ Since we do all kinds of work sorting out which squares form the four regions of a bigger square, our data structure would describe a square as being composed of four region squares.
Such a data structure already exists, it’s called a quadtree.^{6} Squares are represented as four regions, each of which is a smaller square or a cell. A simple implementation is a “Plain Old JavaScript Object” (or “POJO”) with properties for each of the regions. If the property contains a string, it’s cell. If it contains another POJO, it’s a quadtree.
A square that looks like this:
⚪️⚫️⚪️⚪️
⚪️⚪️⚫️⚪️
⚫️⚫️⚫️⚪️
⚪️⚪️⚪️⚪️
Is composed of four regions, the ul
(“upper left”), ur
(“upper right”), lr
(“lower right”), and ll
(“lower left”), something like this:
ul  ur
+
ll  lr
Thus, for example, the ul
is:
⚪️⚫️
⚪️⚪️
And the ur
is:
⚪️⚪️
⚫️⚪️
And so forth. Each of those regions is itself composed of four regions. Thus, the ul
of the ul
is ⚪️
, and the ur
of the ul
is ⚫️
.
The quadtree could be expressed in JavaScript like this:
const quadTree = {
ul: { ul: '⚪️', ur: '⚫️', lr: '⚪️', ll: '⚪️' },
ur: { ul: '⚪️', ur: '⚪️', lr: '⚪️', ll: '⚫️' },
lr: { ul: '⚫️', ur: '⚪️', lr: '⚪️', ll: '⚪️' },
ll: { ul: '⚫️', ur: '⚫️', lr: '⚪️', ll: '⚪️' }
};
Now to our algorithm. Rotating a quadtree is simpler than rotating an array of arrays. First, our test for indivisibility is now whether something is a string
or not:
const isString = (something) => typeof something === 'string';
The value of an indivisible cell remain the same, itself
.
Our divide
function is simple: quadtrees are already divided in the manner we require, we just have to turn them into an array of regions:
const quadTreeToRegions = (qt) =>
[qt.ul, qt.ur, qt.lr, qt.ll];
And finally, our combine function reassembles the rotated regions into a POJO, rotating them in the process:
const regionsToRotatedQuadTree = ([ur, lr, ll, ul]) =>
({ ul, ur, lr, ll });
And here’s our function for rotating a quadtree:
const rotateQuadTree = multirec({
indivisible : isString,
value : itself,
divide: quadTreeToRegions,
combine: regionsToRotatedQuadTree
});
Let’s put it to the test:
rotateQuadTree(quadTree)
//=>
({
ul: { ll: "⚪️", lr: "⚫️", ul: "⚪️", ur: "⚫️" },
ur: { ll: "⚪️", lr: "⚫️", ul: "⚪️", ur: "⚪️" },
lr: { ll: "⚪️", lr: "⚪️", ul: "⚫️", ur: "⚪️" },
ll: { ll: "⚪️", lr: "⚪️", ul: "⚪️", ur: "⚫️" }
})
If we reassemble the square by hand, it’s what we expect:
⚪️⚫️⚪️⚪️
⚪️⚫️⚪️⚫️
⚪️⚫️⚫️⚪️
⚪️⚪️⚪️⚪️
Now we can be serious about the word “Isomorphic.” Isomorphic means, fundamentally, “having the same shape.” Obviously, a quadtree doesn’t look anything like the code in
rotateQuadTree
ormultirec
. So how can a quadtree “look like” an algorithm? The answer is that the quadtree’s data structure looks very much like the wayrotateQuadTree
behaves at run time.
More precisely, the elements of the quadtree and the relationships between them can be put into a onetoone correspondance with the call graph of
rotateQuadTree
when acting on that quadtree.
separation of concerns
But back to our code. All we’ve done so far is moved the “faffing about” out of our code and we’re doing it by hand. That’s bad: we don’t want to retrain our eyes to read quadtrees instead of flat arrays, and we don’t want to sit at a computer all day manually translating quadtrees to flat arrays and back.
If only we could write some code to do it for us… Some recursive code…
Here’s a function that recursively turns a twodimensional array into a quadtree:
const isOneByOneArray = (something) =>
Array.isArray(something) && something.length === 1 &&
Array.isArray(something[0]) && something[0].length === 1;
const contentsOfOneByOneArray = (array) => array[0][0];
const regionsToQuadTree = ([ul, ur, lr, ll]) =>
({ ul, ur, lr, ll });
const arrayToQuadTree = multirec({
indivisible: isOneByOneArray,
value: contentsOfOneByOneArray,
divide: divideSquareIntoRegions,
combine: regionsToQuadTree
});
arrayToQuadTree([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚫️', '⚪️']
])
//=>
({
ul: { ul: "⚪️", ur: "⚪️", lr: "⚫️", ll: "⚪️" },
ur: { ul: "⚪️", ur: "⚪️", lr: "⚪️", ll: "⚪️" },
lr: { ul: "⚪️", ur: "⚪️", lr: "⚪️", ll: "⚫️" },
ll: { ul: "⚫️", ur: "⚪️", lr: "⚫️", ll: "⚫️" }
})
Naturally, we can also write a function to convert quadtrees back into twodimensional arrays again:
const isSmallestActualSquare = (square) => isString(square.ul);
const asTwoDimensionalArray = ({ ul, ur, lr, ll }) =>
[[ul, ur], [ll, lr]];
const regions = ({ ul, ur, lr, ll }) =>
[ul, ur, lr, ll];
const combineFlatArrays = ([upperLeft, upperRight, lowerRight, lowerLeft]) => {
const upperHalf = [...zipWith(concat, upperLeft, upperRight)];
const lowerHalf = [...zipWith(concat, lowerLeft, lowerRight)];
return concat(upperHalf, lowerHalf);
}
const quadTreeToArray = multirec({
indivisible: isSmallestActualSquare,
value: asTwoDimensionalArray,
divide: regions,
combine: combineFlatArrays
});
quadTreeToArray(
arrayToQuadTree([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚫️', '⚪️']
])
)
//=>
([
["⚪️", "⚪️", "⚪️", "⚪️"],
["⚪️", "⚫️", "⚪️", "⚪️"],
["⚫️", "⚪️", "⚪️", "⚪️"],
["⚫️", "⚫️", "⚫️", "⚪️"]
])
And thus, we can take a twodimensional array, turn it into a quadtree, rotate the quadtree, and convert it back to a twodimensional array again:
quadTreeToArray(
rotateQuadTree(
arrayToQuadTree([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚫️', '⚪️']
])
)
)
//=>
([
["⚫️", "⚫️", "⚪️", "⚪️"],
["⚫️", "⚪️", "⚫️", "⚪️"],
["⚫️", "⚪️", "⚪️", "⚪️"],
["⚪️", "⚪️", "⚪️", "⚪️"]
])
but why?
Now, we argued above that we’ve neatly separated the concerns by making three separate functions, instead of interleaving dividing twodimensional squares into regions, rotating regions, and then reassembling twodimensional squares.
But the converse side of this is that what we’re doing is now a lot less efficient: We’re recursing through our data structures three separate times, instead of once. And frankly, multirec
was designed such that the divide
function breaks things up, and the combine
function puts them back together, so these concerns are already mostly separate once we use multirec
instead of a bespoke^{7} recursive function.
One reason to break the logic up into three separate functions would be if we want to do lots of different kinds of things with quadtrees. Besides rotating quadtrees, what else might we do?
Well, we might want to superimpose one image on top of another. This could be part of an image editing application, where we have layers of images and want to superimpose all the layers to derive the finished image for the screen. Or we might be implementing Conway’s Game of Life, and might want to ‘paste’ a pattern like a glider onto a larger universe.
Let’s go with a very simple implementation: We’re only editing blackandwhite images, and each ‘pixel’ is either a ⚪️
or ⚫️
. If we use twodimensional arrays to represent our images, we need to iterate over every ‘pixel’ to perform the superimposition:
const superimposeCell = (left, right) =>
(left === '⚫️'  right === '⚫️') ? '⚫️' : '⚪️';
const superimposeRow = (left, right) =>
[...zipWith(superimposeCell, left, right)];
const superimposeArray = (left, right) =>
[...zipWith(superimposeRow, left, right)];
const canvas =
[ ['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚪️', '⚪️', '⚫️'],
['⚪️', '⚪️', '⚪️', '⚫️']];
const glider =
[ ['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚫️', '⚪️']];
superimposeArray(canvas, glider)
//=>
([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚫️'],
['⚫️', '⚫️', '⚫️', '⚫️']
])
Seems simple enough. How about superimposing a quadtree on a quadtree?
Two trees, © 2013 Jon Bunting, some rights reserved
recursive operations on pairs of quadtrees
We can use multirec
to superimpose one quadtree on top of another: Our function will take a pair of quadtrees, using destructuring to extract one called left
and the other called right
:
const superimposeQuadTrees = multirec({
indivisible: ({ left, right }) => isString(left),
value: ({ left, right }) => right ==='⚫️'
? right
: left,
divide: ({ left, right }) => [
{ left: left.ul, right: right.ul },
{ left: left.ur, right: right.ur },
{ left: left.lr, right: right.lr },
{ left: left.ll, right: right.ll }
],
combine: ([ul, ur, lr, ll]) => ({ ul, ur, lr, ll })
});
quadTreeToArray(
superimposeQuadTrees({
left: arrayToQuadTree(canvas),
right: arrayToQuadTree(glider)
})
)
//=>
([
['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚫️'],
['⚫️', '⚫️', '⚫️', '⚫️']
])
Again, this feels like faffing about just so we can be recursive. But we are in position to do something interesting!
optimizing recursive algorithms with isomorphic data structures
Many images have large regions that are entirely white or black. When superimposing one region on another, if either region is entirely white, we know the result must be the same as the other region. When superimposing one region on another, if either region is entirely black, the result must be entirely black.
We can use the quadtree’s hierarchal representation to exploit this. We’ll store some extra information in each quadtree, its colour: If it is entirely white, its colour will be white. If it is entirely black, its colour will be black. And if it contains a mix of white and black cells, its colour will be a question mark.
const isOneByOneArray = (something) =>
Array.isArray(something) && something.length === 1 &&
Array.isArray(something[0]) && something[0].length === 1;
const contentsOfOneByOneArray = (array) => array[0][0];
const divideSquareIntoRegions = (square) => {
const upperHalf = firstHalf(square);
const lowerHalf = secondHalf(square);
const upperLeft = upperHalf.map(firstHalf);
const upperRight = upperHalf.map(secondHalf);
const lowerRight = lowerHalf.map(secondHalf);
const lowerLeft= lowerHalf.map(firstHalf);
return [upperLeft, upperRight, lowerRight, lowerLeft];
};
const colour = (something) => {
if (something.colour != null) {
return something.colour;
} else if (something === '⚪️') {
return '⚪️';
} else if (something === '⚫️') {
return '⚫️';
} else {
throw "Can't get the colour of this thing";
}
};
const combinedColour = (...elements) =>
elements.reduce((acc, element => acc === element ? element : '❓'))
const regionsToQuadTree = ([ul, ur, lr, ll]) => ({
ul, ur, lr, ll, colour: combinedColour(ul, ur, lr, ll)
});
const arrayToQuadTree = multirec({
indivisible: isOneByOneArray,
value: contentsOfOneByOneArray,
divide: divideSquareIntoRegions,
combine: regionsToQuadTree
});
arrayToQuadTree(
[ ['⚪️', '⚪️'],
['⚪️', '⚪️'] ]
).colour
//=> "⚪️"
arrayToQuadTree(
[ ['⚪️', '⚪️'],
['⚪️', '⚫️'] ]
).colour
//=> "❓"
arrayToQuadTree(
[ ['⚫️', '⚫️'],
['⚫️', '⚫️'] ]
).colour
//=> "⚫️"
arrayToQuadTree(
[ ['⚪️', '⚪️', '⚪️', '⚪️'],
['⚪️', '⚫️', '⚪️', '⚪️'],
['⚫️', '⚪️', '⚪️', '⚪️'],
['⚫️', '⚫️', '⚫️', '⚪️']]
).colour
//=> "❓"
Now, we can take advantage of every region’s computed colour when we superimpose “coloured” quadtrees:
const eitherAreEntirelyColoured = ({ left, right }) =>
colour(left) !== '❓'  colour(right) !== '❓' ;
const superimposeColoured = ({ left, right }) => {
if (colour(left) === '⚪️'  colour(right) === '⚫️') {
return right;
} else if (colour(left) === '⚫️'  colour(right) === '⚪️') {
return left;
} else {
throw "Can't superimpose these things";
}
};
const divideTwoQuadTrees = ({ left, right }) => [
{ left: left.ul, right: right.ul },
{ left: left.ur, right: right.ur },
{ left: left.lr, right: right.lr },
{ left: left.ll, right: right.ll }
];
const combineColouredRegions = ([ul, ur, lr, ll]) => ({
ul, ur, lr, ll, colour: combinedColour(ul, ur, lr, ll)
});
const superimposeColouredQuadTrees = multirec({
indivisible: eitherAreEntirelyColoured,
value: superimposeColoured,
divide: divideTwoQuadTrees,
combine: combineColouredRegions
});
We get the same output, but now instead of comparing every cell whenever we superimpose quadtrees, we compare entire regions at a time. If either is “entirely coloured,” we can return the other one without recursively drilling down to the level of individual pixels.
There is no savings if both quadtrees are composed of a fairly evenly spread mix of black and white pixels (e.g. a checkerboard pattern), but in cases where there are large expanses of white or black, the difference is substantial.
In the case of comparing the 4x4 canvas
and glider
images above, the superimposeArray
function requires sixteen comparisons. The superimposeQuadTrees
function requires twenty comparisons. But the superimposeColouredQuadTrees
function requires just seven comparisons.
If we were writing an image manipulation application, we’d provide much snappier behaviour using coloured quadtrees to represent images on screen.
The interesting thing about this optimization is that it is tuned to the characteristics of both the data structure and the algorithm: It is not something that is easy to perform in the algorithm without the data structure, or in the data structure without the algorithm.
And it’s not the only optimization. Remember our ‘whirling regions’ implementation of rotateQuadTree
? Here’s rotateColouredQuadTree
:
const isEntirelyColoured = (something) =>
colour(something) !== '❓' ;
const rotateColouredQuadTree = multirec({
indivisible : isEntirelyColoured,
value : itself,
divide: quadTreeToRegions,
combine: regionsToRotatedQuadTree
});
Any region that is entirely white or entirely black is its own rotation, so no further dividing and conquering need be done. For images that have large areas of blank space, the “whirling regions” algorithm is not just aesthetically delightful, it’s faster than a bruteforce transposition of array elements.
Optimizations like this can only be implemented when the algorithm and the data structure are isomorphic to each other.
Detail from “Game of Life 6,” © Windell Oskay, some rights reserved
why!
So back to, “Why convert data into a structure that is isomorphic to our algorithm.”
The first reason to do so, is that the code is clearer and easier to read if we convert, then perform operations on the data structure, and then convert it back (if need be).
The second reason to do so, is that if we want to do lots of different operations on the data structure, it is much more efficient to keep it in the form that is isomorphic to the operations we are going to perform on it.
The example we saw was that if we were building a hypothetical image processing application, we could convert an image into quad trees, then rotate or superimpose images at will. We would only need to convert our quadtrees when we need to save or display the image in a rasterized (i.e. arraylike) format.
And third, we saw that once we embraced a data structure that was isomorphic to the form of the algorithm, we could employ elegant optimizations that are impossible (or ridiculously inconvenient) when the algorithm and data structure do not match.
Separating conversion from operation allows us to benefit from all three reasons for ensuring that our algorithms and data structures are isomorphic to each other.
afterward
There is more to read about multirec
in the previous essay, From HigherOrder Functions to Libraries And Frameworks, and in the followup, Time, Space, and Life As We Know It.
Have an observation? Spot an error? You can open an issue, discuss this on hacker news or reddit, or even edit this post yourself.
p.s. Thank you for reading this far. Here is your reward, An Algorithm for Compressing Space and Time. And hey! If you like this kind of thing, JavaScript Allongé is exactly the kind of thing you’ll like.
notes

In biology, two things are isomorphic if they resemble each other. In mathematics, two things are isomorphic if there is a structurepreserving map between them in both directions. In computer science, two things are isomorphic if the person explaining a concept wishes to seem educated. ↩

To maintain a laserfocus on the principles being discussed, we will make a huge number of simplifying assumptions in this essay, starting with the constraint that all squares will have sides that are a “power of two” in length, e.g. 2x2, 4x4, 8x8, 16x16, an so forth. Every single function discussed can be adjusted to deal with other cases, but we will omit those adjustments as our goal is understanding principles, not writing production code. ↩

There are other interesting, and elegant ways to rotate a square 90 degrees clockwise, the simplest being
zip(square)
. They each have their own set of tradeoffs to consider. For example, the ‘whirling regions’ approach can also be generalized to handle rotating squares in 180 and 270 degree increments, not to mention reflections on either axis. But for the purpose of this essay, ‘whirling regions’ is the one we will consider most interesting. ↩ 
itself
is known formally is the I Combinator, and also fondly nicknamed “The Idiot Bird” using Raymond Smullyan’s ornithological taxonomy. ↩ 
More specifically, the data structure we are going to use is called a region quadtree. But we’ll just call it a quadtree. ↩

In American English, bespoke typically refers to a garment that is handcrafted for its wearer. “Bespoke” has, in the last decade, been associated with various hipster endeavours, to the point where its use has become ironic. The turning point was likely when a poppedcollar founder of a prerevenue startup boasted of having two iPhones running a bespoke time management application.
Today, “bespoke” often refers to an item where the owner obtains more value from the status conferred by having a bespoke item, than from the item’s fitness for their personalized purpose. Calling a function “bespoke” implies that it was written to display the author’s trendy use of functional programming, rather than to efficiently rotate a square. ↩