# Visualizing an Assassin Puzzle

Over at Math3ma, Tai-Danae Bradley shared the following puzzle, which she also featured in a fantastic (spoiler-free) YouTube video. If you’re seeing this for the first time, watch the video first.

Consider a square in the xy-plane, and let A (an “assassin”) and T (a “target”) be two arbitrary-but-fixed points within the square. Suppose that the square behaves like a billiard table, so that any ray (a.k.a “shot”) from the assassin will bounce off the sides of the square, with the angle of incidence equaling the angle of reflection.

Puzzle: Is it possible to block any possible shot from A to T by placing a finite number of points in the square?

This puzzle found its way to me through Tai-Danae’s video, via category theorist Emily Riehl, via a talk by the recently deceased Fields Medalist Maryam Mirzakhani, who studied the problem in more generality. I’m not familiar with her work, but knowing mathematicians it’s probably set in an arbitrary complex $n$-manifold.

See Tai-Danae’s post for a proof, which left such an impression on me I had to dig deeper. In this post I’ll discuss a visualization I made—now posted at the end of Tai-Danae’s article—as well as here and below (to avoid spoilers). In the visualization, mouse movement chooses the firing direction for the assassin, and the target is in green. Dragging the target with the mouse updates the position of the guards. The source code is on Github.

## Outline

The visualization uses d3 library, which was made for visualizations that dynamically update with data. I use it because it can draw SVGs real nice.

The meat of the visualization is in two geometric functions.

1. Decompose a ray into a series of line segments—its path as it bounces off the walls—stopping if it intersects any of the points in the plane.
2. Compute the optimal position of the guards, given the boundary square and the positions of the assassin and target.

Both of these functions, along with all the geometry that supports them, is in geometry.js. The rest of the demo is defined in main.js, in which I oafishly trample over d3 best practices to arrive miraculously at a working product. Critiques welcome 🙂

As with most programming and software problems, the key to implementing these functions while maintaining your sanity is breaking it down into manageable pieces. Incrementalism is your friend.

## Vectors, rays, rectangles, and ray splitting

We start at the bottom with a Vector class with helpful methods for adding, scaling, and computing norms and inner products.

function innerProduct(a, b) {
return a.x * b.x + a.y * b.y;
}

class Vector {
constructor(x, y) {
this.x = x;
this.y = y;
}

normalized() { ... }
norm() { ... }
subtract(vector) { ... }
scale(length) { ... }
distance(vector) { ... }
midpoint(b) { ... }
}


This allows one to compute the distance between two points, e.g., with vector.subtract(otherVector).norm().

Next we define a class for a ray, which is represented by its center (a vector) and a direction (a vector).

class Ray {
constructor(center, direction, length=100000) {
this.center = center;
this.length = length;

if (direction.x == 0 && direction.y == 0) {
throw "Can't have zero direction";
}
this.direction = direction.normalized();
}

endpoint() {
}

intersects(point) {
let shiftedPoint = point.subtract(this.center);
let signedLength = innerProduct(shiftedPoint, this.direction);
let projectedVector = this.direction.scale(signedLength);
let differenceVector = shiftedPoint.subtract(projectedVector);

if (signedLength > 0
&& this.length > signedLength
} else {
return null;
}
}
}


The ray must be finite for us to draw it, but the length we've chosen is so large that, as you can see in the visualization, it's effectively infinite. Feel free to scale it up even longer.

The interesting bit is the intersection function. We want to compute whether a ray intersects a point. To do this, we use the inner product as a decision rule to compute the distance of a point from a line. If that distance is very small, we say they intersect.

In our demo points are not infinitesimal, but rather have a small radius described by intersectionRadius. For the sake of being able to see anything we set this to 3 pixels. If it’s too small the demo will look bad. The ray won’t stop when it should appear to stop, and it can appear to hit the target when it doesn’t.

Next up we have a class for a Rectangle, which is where the magic happens. The boilerplate and helper methods:

class Rectangle {
constructor(bottomLeft, topRight) {
this.bottomLeft = bottomLeft;
this.topRight = topRight;
}

topLeft() { ... }
center() { ... }
width() { .. }
height() { ... }
contains(vector) { ... }


The function rayToPoints that splits a ray into line segments from bouncing depends on three helper functions:

1. rayIntersection: Compute the intersection point of a ray with the rectangle.
2. isOnVerticalWall: Determine if a point is on a vertical or horizontal wall of the rectangle, raising an error if neither.
3. splitRay: Split a ray into a line segment and a shorter ray that’s “bounced” off the wall of the rectangle.

(2) is trivial, computing some x- and y-coordinate distances up to some error tolerance. (1) involves parameterizing the ray and checking one of four inequalities. If the bottom left of the rectangle is $(x_1, y_1)$ and the top right is $(x_2, y_2)$ and the ray is written as $\{ (c_1 + t v_1, c_2 + t v_2) \mid t > 0 \}$, then—with some elbow grease—the following four equations provide all possibilities, with some special cases for vertical or horizontal rays:

\displaystyle \begin{aligned} c_2 + t v_2 &= y_2 & \textup{ and } \hspace{2mm} & x_1 \leq c_1 + t v_1 \leq x_2 & \textup{ (intersects top)} \\ c_2 + t v_2 &= y_1 & \textup{ and } \hspace{2mm} & x_1 \leq c_1 + t v_1 \leq x_2 & \textup{ (intersects bottom)} \\ c_1 + t v_1 &= x_1 & \textup{ and } \hspace{2mm} & y_1 \leq c_2 + t v_2 \leq y_2 & \textup{ (intersects left)} \\ c_1 + t v_1 &= x_2 & \textup{ and } \hspace{2mm} & y_1 \leq c_2 + t v_2 \leq y_2 & \textup{ (intersects right)} \\ \end{aligned}

In code:

  rayIntersection(ray) {
let c1 = ray.center.x;
let c2 = ray.center.y;
let v1 = ray.direction.x;
let v2 = ray.direction.y;
let x1 = this.bottomLeft.x;
let y1 = this.bottomLeft.y;
let x2 = this.topRight.x;
let y2 = this.topRight.y;

// ray is vertically up or down
if (epsilon > Math.abs(v1)) {
return new Vector(c1, (v2 > 0 ? y2 : y1));
}

// ray is horizontally left or right
if (epsilon > Math.abs(v2)) {
return new Vector((v1 > 0 ? x2 : x1), c2);
}

let tTop = (y2 - c2) / v2;
let tBottom = (y1 - c2) / v2;
let tLeft = (x1 - c1) / v1;
let tRight = (x2 - c1) / v1;

// Exactly one t value should be both positive and result in a point
// within the rectangle

let tValues = [tTop, tBottom, tLeft, tRight];
for (let i = 0; i  epsilon && this.contains(intersection)) {
return intersection;
}
}

throw "Unexpected error: ray never intersects rectangle!";
}


Next, splitRay splits a ray into a single line segment and the “remaining” ray, by computing the ray’s intersection with the rectangle, and having the “remaining” ray mirror the direction of approach with a new center that lies on the wall of the rectangle. The new ray length is appropriately shorter. If we run out of ray length, we simply return a segment with a null ray.

  splitRay(ray) {
let segment = [ray.center, this.rayIntersection(ray)];
let segmentLength = segment[0].subtract(segment[1]).norm();
let remainingLength = ray.length - segmentLength;

if (remainingLength < 10) {
return {
segment: [ray.center, ray.endpoint()],
ray: null
};
}

let vertical = this.isOnVerticalWall(segment[1]);
let newRayDirection = null;

if (vertical) {
newRayDirection = new Vector(-ray.direction.x, ray.direction.y);
} else {
newRayDirection = new Vector(ray.direction.x, -ray.direction.y);
}

let newRay = new Ray(segment[1], newRayDirection, length=remainingLength);
return {
segment: segment,
ray: newRay
};
}


As you have probably guessed, rayToPoints simply calls  splitRay over and over again until the ray hits an input “stopping point”—a guard, the target, or the assassin—or else our finite ray length has been exhausted. The output is a list of points, starting from the original ray’s center, for which adjacent pairs are interpreted as line segments to draw.

  rayToPoints(ray, stoppingPoints) {
let points = [ray.center];
let remainingRay = ray;

while (remainingRay) {
// check if the ray would hit any guards or the target
if (stoppingPoints) {
let hardStops = stoppingPoints.map(p => remainingRay.intersects(p))
.filter(p => p != null);
if (hardStops.length > 0) {
// find first intersection and break
let closestStop = remainingRay.closestToCenter(hardStops);
points.push(closestStop);
break;
}
}

let rayPieces = this.splitRay(remainingRay);
points.push(rayPieces.segment[1]);
remainingRay = rayPieces.ray;
}

return points;
}


That’s sufficient to draw the shot emanating from the assassin. This method is called every time the mouse moves.

## Optimal guards

The function to compute the optimal position of the guards takes as input the containing rectangle, the assassin, and the target, and produces as output a list of 16 points.

/*
* Compute the 16 optimal guards to prevent the assassin from hitting the
* target.
*/
function computeOptimalGuards(square, assassin, target) {
...
}


If you read Tai-Danae’s proof, you’ll know that this construction is to

1. Compute mirrors of the target across the top, the right, and the top+right of the rectangle. Call this resulting thing the 4-mirrored-targets.
2. Replicate the 4-mirrored-targets four times, by translating three of the copies left by the entire width of the 4-mirrored-targets shape, down by the entire height, and both left-and-down.
3. Now you have 16 copies of the target, and one assassin. This gives 16 line segments from assassin-to-target-copy. Place a guard at the midpoint of each of these line segments.
4. Finally, apply the reverse translation and reverse mirroring to return the guards to the original square.

Due to WordPress being a crappy blogging platform I need to migrate off of, the code snippets below have been magically disappearing. I’ve included links to github lines as well.

Step 1 (after adding simple helper functions on Rectangle to do the mirroring):

  // First compute the target copies in the 4 mirrors
let target1 = target.copy();
let target2 = square.mirrorTop(target);
let target3 = square.mirrorRight(target);
let target4 = square.mirrorTop(square.mirrorRight(target));
target1.guardLabel = 1;
target2.guardLabel = 2;
target3.guardLabel = 3;
target4.guardLabel = 4;

  // for each mirrored target, compute the four two-square-length translates
let mirroredTargets = [target1, target2, target3, target4];
let horizontalShift = 2 * square.width();
let verticalShift = 2 * square.height();
let translateLeft = new Vector(-horizontalShift, 0);
let translateRight = new Vector(horizontalShift, 0);
let translateUp = new Vector(0, verticalShift);
let translateDown = new Vector(0, -verticalShift);

let translatedTargets = [];
for (let i = 0; i < mirroredTargets.length; i++) {
let target = mirroredTargets[i];
translatedTargets.push([
target,
]);
}


Step 3, computing the midpoints:

  // compute the midpoints between the assassin and each translate
let translatedMidpoints = [];
for (let i = 0; i  t.midpoint(assassin)));
}


Step 4, returning the guards back to the original square, is harder than it seems, because the midpoint of an assassin-to-target-copy segment might not be in the same copy of the square as the target-copy being fired at. This means you have to detect which square copy the midpoint lands in, and use that to determine which operations are required to invert. This results in the final block of this massive function.

  // determine which of the four possible translates the midpoint is in
// and reverse the translation. Since midpoints can end up in completely
// different copies of the square, we have to check each one for all cases.
function untranslate(point) {
if (point.x  square.bottomLeft.y) {
} else if (point.x >= square.bottomLeft.x && point.y <= square.bottomLeft.y) {
} else if (point.x < square.bottomLeft.x && point.y <= square.bottomLeft.y) {
} else {
return point;
}
}

// undo the translations to get the midpoints back to the original 4-mirrored square.
let untranslatedMidpoints = [];
for (let i = 0; i  square.topRight.x && point.y > square.topRight.y) {
return square.mirrorTop(square.mirrorRight(point));
} else if (point.x > square.topRight.x && point.y <= square.topRight.y) {
return square.mirrorRight(point);
} else if (point.x  square.topRight.y) {
return square.mirrorTop(point);
} else {
return point;
}
}

return untranslatedMidpoints.map(unmirror);


And that’s all there is to it!

## Improvements, if I only had the time

There are a few improvements I’d like to make to this puzzle, but haven’t made the time (I’m writing a book, after all!).

1. Be able to drag the guards around.
2. Create new guards from an empty set of guards, with a button to “reveal” the solution.
3. Include a toggle that, when pressed, darkens the entire region of the square that can be hit by the assassin. For example, this would allow you to see if the target is in the only possible safe spot, or if there are multiple safe spots for a given configuration.
4. Perhaps darken the vulnerable spots by the number of possible paths that hit it, up to some limit.
5. The most complicated one: generalize to an arbitrary polygon (convex or not!), for which there may be no optional solution. The visualization would allow you to look for a solution using 2-4.

Pull requests are welcome if you attempt any of these improvements.

Until next time!

# The Čech Complex and the Vietoris-Rips Complex

It’s about time we got back to computational topology. Previously in this series we endured a lightning tour of the fundamental group and homology, then we saw how to compute the homology of a simplicial complex using linear algebra.

What we really want to do is talk about the inherent shape of data. Homology allows us to compute some qualitative features of a given shape, i.e., find and count the number of connected components or a given shape, or the number of “2-dimensional holes” it has. This is great, but data doesn’t come in a form suitable for computing homology. Though they may have originated from some underlying process that follows nice rules, data points are just floating around in space with no obvious connection between them.

Here is a cool example of Thom Yorke, the lead singer of the band Radiohead, whose face was scanned with a laser scanner for their music video “House of Cards.”

Radiohead’s Thom Yorke in the music video for House of Cards (click the image to watch the video).

Given a point cloud such as the one above, our long term goal (we’re just getting started in this post) is to algorithmically discover what the characteristic topological features are in the data. Since homology is pretty coarse, we might detect the fact that the point cloud above looks like a hollow sphere with some holes in it corresponding to nostrils, ears, and the like. The hope is that if the data set isn’t too corrupted by noise, then it’s a good approximation to the underlying space it is sampled from. By computing the topological features of a point cloud we can understand the process that generated it, and Science can proceed.

But it’s not always as simple as Thom Yorke’s face. It turns out the producers of the music video had to actually degrade the data to get what you see above, because their lasers were too precise and didn’t look artistic enough! But you can imagine that if your laser is mounted on a car on a bumpy road, or tracking some object in the sky, or your data comes from acoustic waves traveling through earth, you’re bound to get noise. Or more realistically, if your data comes from thousands of stock market prices then the process generating the data is super mysterious. It changes over time, it may not follow any discernible pattern (though speculators may hope it does), and you can’t hope to visualize the entire dataset in any useful way.

But with persistent homology, so the claim goes, you’d get a good qualitative understanding of the dataset. Your results would be resistant to noise inherent in the data. It also wouldn’t be sensitive to the details of your data cleaning process. And with a dash of ingenuity, you can come up with a reasonable mathematical model of the underlying generative process. You could use that model to design algorithms, make big bucks, discover new drugs, recognize pictures of cats, or whatever tickles your fancy.

But our first problem is to resolve the input data type error. We want to use homology to describe data, but our data is a point cloud and homology operates on simplicial complexes. In this post we’ll see two ways one can do this, and see how they’re related.

## The Čech complex

Let’s start with the Čech complex. Given a point set $X$ in some metric space and a number $\varepsilon > 0$, the Čech complex $C_\varepsilon$ is the simplicial complex whose simplices are formed as follows. For each subset $S \subset X$ of points, form a $(\varepsilon/2)$-ball around each point in $S$, and include $S$ as a simplex (of dimension $|S|$) if there is a common point contained in all of the balls in $S$. This structure obviously satisfies the definition of a simplicial complex: any sub-subset $S' \subset S$ of a simplex $S$ will be also be a simplex. Here is an example of the epsilon balls.

An example of a point cloud (left) and a corresponding choice of (epsilon/2)-balls. To get the Cech complex, we add a k-simplex any time we see a subset of k points with common intersection.  [Image credit: Robert Ghrist]

Let me superscript the Čech complex to illustrate the pieces. Specifically, we’ll let $C_\varepsilon^{j}$ denote all the simplices of dimension up to $j$. In particular, $C_\varepsilon^1$ is a graph where an edge is placed between $x,y$ if $d(x,y) < \varepsilon$, and $C_{\varepsilon}^2$ places triangles (2-simplices) on triples of points whose balls have a three-way intersection.

A topologist will have a minor protest here: the simplicial complex is supposed to resemble the structure inherent in the underlying points, but how do we know that this abstract simplicial complex (which is really hard to visualize!) resembles the topological space we used to make it? That is, $X$ was sitting in some metric space, and the union of these epsilon-balls forms some topological space $X(\varepsilon)$ that is close in structure to $X$. But is the Čech complex $C_\varepsilon$ close to $X(\varepsilon)$? Do they have the same topological structure? It’s not a trivial theorem to prove, but it turns out to be true.

The Nerve Theorem: The homotopy types of $X(\varepsilon)$ and $C_\varepsilon$ are the same.

We won’t remind the readers about homotopy theory, but suffice it to say that when two topological spaces have the same homotopy type, then homology can’t distinguish them. In other words, if homotopy type is too coarse for a discriminator for our dataset, then persistent homology will fail us for sure.

So this theorem is a good sanity check. If we want to learn about our point cloud, we can pick a $\varepsilon$ and study the topology of the corresponding Čech complex $C_\varepsilon$. The reason this is called the “Nerve Theorem” is because one can generalize it to an arbitrary family of convex sets. Given some family $F$ of convex sets, the nerve is the complex obtained by adding simplices for mutually overlapping subfamilies in the same way. The nerve theorem is actually more general, it says that with sufficient conditions on the family $F$ being “nice,” the resulting Čech complex has the same topological structure as $F$.

The problem is that Čech complexes are tough to compute. To tell whether there are any 10-simplices (without additional knowledge) you have to inspect all subsets of size 10. In general computing the entire complex requires exponential time in the size of $X$, which is extremely inefficient. So we need a different kind of complex, or at least a different representation to compensate.

## The Vietoris-Rips complex

The Vietoris-Rips complex is essentially the same as the Čech complex, except instead of adding a $d$-simplex when there is a common point of intersection of all the $(\varepsilon/2)$-balls, we just do so when all the balls have pairwise intersections. We’ll denote the Vietoris-Rips complex with parameter $\varepsilon$ as $VR_{\varepsilon}$.

Here is an example to illustrate: if you give me three points that are the vertices of an equilateral triangle of side length 1, and I draw $(1/2)$-balls around each point, then they will have all three pairwise intersections but no common point of intersection.

Three balls which intersect pairwise, but have no point of triple intersection. With appropriate parameters, the Cech and V-R complexes are different.

So in this example the Vietoris-Rips complex is a graph with a 2-simplex, while the Čech complex is just a graph.

One obvious question is: do we still get the benefits of the nerve theorem with Vietoris-Rips complexes? The answer is no, obviously, because the Vietoris-Rips complex and Čech complex in this triangle example have totally different topology! But everything’s not lost. What we can do instead is compare Vietoris-Rips and Čech complexes with related parameters.

Theorem: For all $\varepsilon > 0$, the following inclusions hold

$\displaystyle C_{\varepsilon} \subset VR_{\varepsilon} \subset C_{2\varepsilon}$

So if the Čech complexes for both $\varepsilon$ and $2\varepsilon$ are good approximations of the underlying data, then so is the Vietoris-Rips complex. In fact, you can make this chain of inclusions slightly tighter, and if you’re interested you can see Theorem 2.5 in this recent paper of de Silva and Ghrist.

Now your first objection should be that computing a Vietoris-Rips complex still requires exponential time, because you have to scan all subsets for the possibility that they form a simplex. It’s true, but one nice thing about the Vietoris-Rips complex is that it can be represented implicitly as a graph. You just include an edge between two points if their corresponding balls overlap. Once we want to compute the actual simplices in the complex we have to scan for cliques in the graph, so that sucks. But it turns out that computing the graph is the first step in other more efficient methods for computing (or approximating) the VR complex.

Let’s go ahead and write a (trivial) program that computes the graph representation of the Vietoris-Rips complex of a given data set.

import numpy
def naiveVR(points, epsilon):
points = [numpy.array(x) for x in points]
vrComplex = [(x,y) for (x,y) in combinations(points, 2) if norm(x - y) < 2*epsilon]
return numpy.array(vrComplex)


Let’s try running it on a modestly large example: the first frame of the Radiohead music video. It’s got about 12,000 points in $\mathbb{R}^4$ (x,y,z,intensity), and sadly it takes about twenty minutes. There are a couple of ways to make it more efficient. One is to use specially-crafted data structures for computing threshold queries (i.e., find all points within $\varepsilon$ of this point). But those are only useful for small thresholds, and we’re interested in sweeping over a range of thresholds. Another is to invoke approximations of the data structure which give rise to “approximate” Vietoris-Rips complexes.

## Other stuff

In a future post we’ll implement a method for speeding up the computation of the Vietoris-Rips complex, since this is the primary bottleneck for topological data analysis. But for now the conceptual idea of how Čech complexes and Vietoris-Rips complexes can be used to turn point clouds into simplicial complexes in reasonable ways.

Before we close we should mention that there are other ways to do this. I’ve chosen the algebraic flavor of topological data analysis due to my familiarity with algebra and the work based on this approach. The other approaches have a more geometric flavor, and are based on the Delaunay triangulation, a hallmark of computational geometry algorithms. The two approaches I’ve heard of are called the alpha complex and the flow complex. The downside of these approaches is that, because they are based on the Delaunay triangulation, they have poor scaling in the dimension of the data. Because high dimensional data is crucial, many researchers have been spending their time figuring out how to speed up approximations of the V-R complex. See these slides of Afra Zomorodian for an example.

Until next time!

# Fixing Bugs in “Computing Homology”

A few awesome readers have posted comments in Computing Homology to the effect of, “Your code is not quite correct!” And they’re right! Despite the almost year since that post’s publication, I haven’t bothered to test it for more complicated simplicial complexes, or even the basic edge cases! When I posted it the mathematics just felt so solid to me that it had to be right (the irony is rich, I know).

As such I’m apologizing for my lack of rigor and explaining what went wrong, the fix, and giving some test cases. As of the publishing of this post, the Github repository for Computing Homology has been updated with the correct code, and some more examples.

The main subroutine was the simultaneousReduce function which I’ll post in its incorrectness below

def simultaneousReduce(A, B):
if A.shape[1] != B.shape[0]:
raise Exception("Matrices have the wrong shape.")

numRows, numCols = A.shape # col reduce A

i,j = 0,0
while True:
if i >= numRows or j >= numCols:
break

if A[i][j] == 0:
nonzeroCol = j
while nonzeroCol < numCols and A[i,nonzeroCol] == 0:
nonzeroCol += 1

if nonzeroCol == numCols:
j += 1
continue

colSwap(A, j, nonzeroCol)
rowSwap(B, j, nonzeroCol)

pivot = A[i,j]
scaleCol(A, j, 1.0 / pivot)
scaleRow(B, j, 1.0 / pivot)

for otherCol in range(0, numCols):
if otherCol == j:
continue
if A[i, otherCol] != 0:
scaleAmt = -A[i, otherCol]
colCombine(A, otherCol, j, scaleAmt)
rowCombine(B, j, otherCol, -scaleAmt)

i += 1; j+= 1

return A,B


It’s a beast of a function, and the persnickety detail was just as beastly: this snippet should have an $i += 1$ instead of a $j$.

if nonzeroCol == numCols:
j += 1
continue


This is simply what happens when we’re looking for a nonzero entry in a row to use as a pivot for the corresponding column, but we can’t find one and have to move to the next row. A stupid error on my part that would be easily caught by proper test cases.

The next mistake is a mathematical misunderstanding. In short, the simultaneous column/row reduction process is not enough to get the $\partial_{k+1}$ matrix into the right form! Let’s see this with a nice example, a triangulation of the Mobius band. There are a number of triangulations we could use, many of which are seen in these slides. The one we’ll use is the following.

It’s first and second boundary maps are as follows (in code, because latex takes too much time to type out)

mobiusD1 = numpy.array([
[-1,-1,-1,-1, 0, 0, 0, 0, 0, 0],
[ 1, 0, 0, 0,-1,-1,-1, 0, 0, 0],
[ 0, 1, 0, 0, 1, 0, 0,-1,-1, 0],
[ 0, 0, 0, 1, 0, 0, 1, 0, 1, 1],
])

mobiusD2 = numpy.array([
[ 1, 0, 0, 0, 1],
[ 0, 0, 0, 1, 0],
[-1, 0, 0, 0, 0],
[ 0, 0, 0,-1,-1],
[ 0, 1, 0, 0, 0],
[ 1,-1, 0, 0, 0],
[ 0, 0, 0, 0, 1],
[ 0, 1, 1, 0, 0],
[ 0, 0,-1, 1, 0],
[ 0, 0, 1, 0, 0],
])


And if we were to run the above code on it we’d get a first Betti number of zero (which is incorrect, it’s first homology group has rank 1). Here are the reduced matrices.

>>> A1, B1 = simultaneousReduce(mobiusD1, mobiusD2)
>>> A1
array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]])
>>> B1
array([[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  1,  0,  0,  0],
[ 1, -1,  0,  0,  0],
[ 0,  0,  0,  0,  1],
[ 0,  1,  1,  0,  0],
[ 0,  0, -1,  1,  0],
[ 0,  0,  1,  0,  0]])


The first reduced matrix looks fine; there’s nothing we can do to improve it. But the second one is not quite fully reduced! Notice that rows 5, 8 and 10 are not linearly independent. So we need to further row-reduce the nonzero part of this matrix before we can read off the true rank in the way we described last time. This isn’t so hard (we just need to reuse the old row-reduce function we’ve been using), but why is this allowed? It’s just because the corresponding column operations for those row operations are operating on columns of all zeros! So we need not worry about screwing up the work we did in column reducing the first matrix, as long as we only work with the nonzero rows of the second.

Of course, nothing is stopping us from ignoring the “corresponding” column operations, since we know we’re already done there. So we just have to finish row reducing this matrix.

This changes our bettiNumber function by adding a single call to a row-reduce function which we name so as to be clear what’s happening. The resulting function is

def bettiNumber(d_k, d_kplus1):
A, B = numpy.copy(d_k), numpy.copy(d_kplus1)
simultaneousReduce(A, B)
finishRowReducing(B)

dimKChains = A.shape[1]
kernelDim = dimKChains - numPivotCols(A)
imageDim = numPivotRows(B)

return kernelDim - imageDim


And running this on our Mobius band example gives:

>>> bettiNumber(mobiusD1, mobiusD2))
1


As desired. Just to make sure things are going swimmingly under the hood, we can check to see how finishRowReducing does after calling simultaneousReduce

>>> simultaneousReduce(mobiusD1, mobiusD2)
(array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]]), array([[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  0,  0,  0,  0],
[ 0,  1,  0,  0,  0],
[ 1, -1,  0,  0,  0],
[ 0,  0,  0,  0,  1],
[ 0,  1,  1,  0,  0],
[ 0,  0, -1,  1,  0],
[ 0,  0,  1,  0,  0]]))
>>> finishRowReducing(mobiusD2)
array([[1, 0, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 1],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]])


Indeed, finishRowReducing finishes row reducing the second boundary matrix. Note that it doesn’t preserve how the rows of zeros lined up with the pivot columns of the reduced version of $\partial_1$ as it did in the previous post, but since in the end we’re only counting pivots it doesn’t matter how we switch rows. The “zeros lining up” part is just for a conceptual understanding of how the image lines up with the kernel for a valid simplicial complex.

In fixing this issue we’ve also fixed an issue another commenter mentioned, that you couldn’t blindly plug in the zero matrix for $\partial_0$ and get zeroth homology (which is the same thing as connected components). After our fix you can.

Of course there still might be bugs, but I have so many drafts lined up on this blog (and research papers to write, experiments to run, theorems to prove), that I’m going to put off writing a full test suite. I’ll just have to update this post with new bug fixes as they come. There’s just so much math and so little time 🙂 But extra kudos to my amazing readers who were diligent enough to run examples and spot my error. I’m truly blessed to have you on my side.

Also note that this isn’t the most efficient way to represent the simplicial complex data, or the most efficient row reduction algorithm. If you’re going to run the code on big inputs, I suggest you take advantage of sparse matrix algorithms for doing this sort of stuff. You can represent the simplices as entries in a dictionary and do all sorts of clever optimizations to make the algorithm effectively linear time in the number of simplices.

Until next time!