Formulating the Support Vector Machine Optimization Problem

The hypothesis and the setup

This blog post has an interactive demo (mostly used toward the end of the post). The source for this demo is available in a Github repository.

Last time we saw how the inner product of two vectors gives rise to a decision rule: if w is the normal to a line (or hyperplane) L, the sign of the inner product \langle x, w \rangle tells you whether x is on the same side of L as w.

Let’s translate this to the parlance of machine-learning. Let x \in \mathbb{R}^n be a training data point, and y \in \{ 1, -1 \} is its label (green and red, in the images in this post). Suppose you want to find a hyperplane which separates all the points with -1 labels from those with +1 labels (assume for the moment that this is possible). For this and all examples in this post, we’ll use data in two dimensions, but the math will apply to any dimension.


Some data labeled red and green, which is separable by a hyperplane (line).

The hypothesis we’re proposing to separate these points is a hyperplane, i.e. a linear subspace that splits all of \mathbb{R}^n into two halves. The data that represents this hyperplane is a single vector w, the normal to the hyperplane, so that the hyperplane is defined by the solutions to the equation \langle x, w \rangle = 0.

As we saw last time, w encodes the following rule for deciding if a new point z has a positive or negative label.

\displaystyle h_w(z) = \textup{sign}(\langle w, x \rangle)

You’ll notice that this formula only works for the normals w of hyperplanes that pass through the origin, and generally we want to work with data that can be shifted elsewhere. We can resolve this by either adding a fixed term b \in \mathbb{R}—often called a bias because statisticians came up with it—so that the shifted hyperplane is the set of solutions to \langle x, w \rangle + b = 0. The shifted decision rule is:

\displaystyle h_w(z) = \textup{sign}(\langle w, x \rangle + b)

Now the hypothesis is the pair of vector-and-scalar w, b. A trickier but perhaps cleaner way to do the same thing is to make the normal w \in \mathbb{R}^{n+1}, i.e., add one dimension, and then augment all your data with a new dimension whose coordinate is always 1. This makes the normal inner product output the same formula above. We’ll do this, and call the new coordinate x_0, so that the original data is x = (x_1, \dots, x_n).

Now, the key intuitive idea behind the formulation of the SVM problem is that there are many possible separating hyperplanes for a given set of labeled training data. For example, here is a gif showing infinitely many choices.


The question is: how can we find the separating hyperplane that not only separates the training data, but generalizes as well as possible to new data? The assumption of the SVM is that a hyperplane which separates the points, but is also as far away from any training point as possible, will generalize best.


While contrived, it’s easy to see that the separating hyperplane is as far as possible from any training point.

More specifically, fix a labeled dataset of points (x_i, y_i), or more precisely:

\displaystyle D = \{ (x_i, y_i) \mid i = 1, \dots, m, x_i \in \mathbb{R}^{n}, y_i \in \{1, -1\}  \}

And a hypothesis defined by the normal w \in \mathbb{R}^{n}. Let’s also suppose that w defines a hyperplane that correctly separates all the training data into the two labeled classes, and we just want to measure its quality. That measure of quality is the length of its margin.

Definition: The geometric margin of a hyperplane w with respect to a dataset D is the shortest distance from a training point x_i to the hyperplane defined by w.

The best hyperplane has the largest possible margin.

This margin can even be computed quite easily using our work from last post. The distance from x to the hyperplane defined by w is the same as the length of the projection of x onto w. And this is just computed by an inner product.


If the tip of the x arrow is the point in question, then a is the dot product, and the distance from x to the hyperplane L defined by w.

A naive optimization objective

If we wanted to, we could stop now and define an optimization problem that would be very hard to solve. It would look like this:

\displaystyle \begin{aligned} & \max_{w} \min_{x_i} \left | \left \langle x_i, \frac{w}{\|w\|} \right \rangle \right | & \\ \textup{subject to \ \ } & \textup{sign}(\langle x_i, w \rangle) = \textup{sign}(y_i) & \textup{ for every } i = 1, \dots, m \end{aligned}

The reasons this is hard boil down to this formulation being horrifyingly nonlinear. In more detail:

  1. The constraints are nonlinear due to the sign comparisons.
  2. There’s a min and a max! A priori, we have to do this because we don’t know which point is going to be the closest to the hyperplane.
  3. The objective is nonlinear in two ways: the absolute value and the projection requires you to take a norm and divide.

The rest of this post (and indeed, a lot of the work in grokking SVMs) is dedicated to converting this optimization problem to one in which the constraints are all linear inequalities and the objective is a single, quadratic polynomial we want to minimize or maximize.

Along the way, we’ll notice some neat features of the SVM.

Trick 1: linearizing the constraints

To solve the first problem, we can use a trick. We want to know whether \textup{sign}(\langle x_i, w \rangle) = \textup{sign}(y_i) for a labeled training point (x_i, y_i). The trick is to multiply them together. If their signs agree, then their product will be positive, otherwise it will be negative.

So each constraint becomes:

\displaystyle \langle x_i, w \rangle \cdot y_i \geq 0

This is still linear because y_i is a constant (input) to the optimization problem. The variables are the coefficients of w.

The left hand side of this inequality is often called the functional margin of a training point, since, as we will see, it still works to classify x_i, even if w is scaled so that it is no longer a unit vector. Indeed, the sign of the inner product is independent of how w is scaled.

Trick 1.5: the optimal solution is midway between classes

This small trick is to notice that if w is the supposed optimal separating hyperplane, i.e. its margin is maximized, then it must necessarily be exactly halfway in between the closest points in the positive and negative classes.

In other words, if x_+ and x_- are the closest points in the positive and negative classes, respectively, then \langle x_{+}, w \rangle = -\langle x_{-}, w \rangle . If this were not the case, then you could adjust the bias entry of w until it they are exactly equal, and you will have increased the margin. The closest point, say x_+ will have gotten farther away, and the closest point in the opposite class, x_- will have gotten closer, but will not be closer than x_+.

Trick 2: getting rid of the max + min

Resolving this problem essentially uses the fact that the hypothesis, which comes in the form of the normal vector w, has a degree of freedom in its length.

Indeed, in the animation below, I can increase or decrease the length of w without changing the decision boundary.


I have to keep my hand very steady (because I was too lazy to program it so that it only increases/decreases in length), but you can see the point. The line is perpendicular to the normal vector, and it doesn’t depend on the length.

Let’s combine this with tricks 1 and 1.5. If we increase the length of w, that means the absolute values of the dot products \langle x_i, w \rangle used in the constraints will all increase by the same amount (without changing their sign). Indeed, for any vector a we have \langle a, w \rangle = \|w \| \cdot \langle a, w / \| w \| \rangle.

In this world, the inner product measurement of distance from a point to the hyperplane is no longer faithful. The true distance is \langle a, w / \| w \| \rangle, but the distance measured by \langle a, w \rangle is measured in units of 1 / \| w \|.


In this example, the two numbers next to the green dot represent the true distance of the point from the hyperplane, and the dot product of the point with the normal (respectively). The dashed lines are the solutions to <x, w> = 1. The magnitude of w is 2.2, the inverse of that is 0.46, and indeed 2.2 = 4.8 * 0.46 (we’ve rounded the numbers).

Now suppose we had the optimal hyperplane and its normal w. No matter how near (or far) the nearest positively labeled training point x is, we could scale the length of w to force \langle x, w \rangle = 1. This is the core of the trick. One consequence is that the actual distance from x to the hyperplane is \frac{1}{\| w \|} = \langle x, w / \| w \| \rangle.


The same as above, but with the roles reversed. We’re forcing the inner product of the point with w to be 1. The true distance is unchanged.

In particular, if we force the closest point to have inner product 1, then all other points will have inner product at least 1. This has two consequences. First, our constraints change to \langle x_i, w \rangle \cdot y_i \geq 1 instead of \geq 0. Second, we no longer need to ask which point is closest to the candidate hyperplane! Because after all, we never cared which point it was, just how far away that closest point was. And now we know that it’s exactly 1 / \| w \| away. Indeed, if the optimal points weren’t at that distance, then that means the closest point doesn’t exactly meet the constraint, i.e. that \langle x, w \rangle > 1 for every training point x. We could then scale w shorter until \langle x, w \rangle = 1, hence increasing the margin 1 / \| w \|.

In other words, the coup de grâce, provided all the constraints are satisfied, the optimization objective is just to maximize 1 / \| w \|, a.k.a. to minimize \| w \|.

This intuition is clear from the following demonstration, which you can try for yourself. In it I have a bunch of positively and negatively labeled points, and the line in the center is the candidate hyperplane with normal w that you can drag around. Each training point has two numbers next to it. The first is the true distance from that point to the candidate hyperplane; the second is the inner product with w. The two blue dashed lines are the solutions to \langle x, w \rangle = \pm 1. To solve the SVM by hand, you have to ensure the second number is at least 1 for all green points, at most -1 for all red points, and then you have to make w as short as possible. As we’ve discussed, shrinking w moves the blue lines farther away from the separator, but in order to satisfy the constraints the blue lines can’t go further than any training point. Indeed, the optimum will have those blue lines touching a training point on each side.



I bet you enjoyed watching me struggle to solve it. And while it’s probably not the optimal solution, the idea should be clear.

The final note is that, since we are now minimizing \| w \|, a formula which includes a square root, we may as well minimize its square \| w \|^2 = \sum_j w_j^2. We will also multiply the objective by 1/2, because when we eventually analyze this problem we will take a derivative, and the square in the exponent and the 1/2 will cancel.

The final form of the problem

Our optimization problem is now the following:

\displaystyle \begin{aligned} & \min_{w}  \frac{1}{2} \| w \|^2 & \\ \textup{subject to \ \ } & \langle x_i, w \rangle \cdot y_i \geq 1 & \textup{ for every } i = 1, \dots, m \end{aligned}

This is much simpler to analyze. The constraints are all linear equalities (which, because of linear programming, we know are tractable to optimize). The objective to minimize, however, is a convex quadratic function of the input variables—a sum of squares of the inputs.

Such problems are generally called quadratic programming problems (or QPs, for short). There are general methods to find solutions! However, they often suffer from numerical stability issues and have less-than-satisfactory runtime. Luckily, the form in which we’ve expressed the support vector machine problem is specific enough that we can analyze it directly, and find a way to solve it without appealing to general-purpose numerical solvers.

We will tackle this problem in a future post (planned for two posts sequel to this one). Before we close, let’s just make a few more observations about the solution to the optimization problem.

Support Vectors

In Trick 1.5 we saw that the optimal separating hyperplane has to be exactly halfway between the two closest points of opposite classes. Moreover, we noticed that, provided we’ve scaled \| w \| properly, these closest points (there may be multiple for positive and negative labels) have to be exactly “distance” 1 away from the separating hyperplane.

Another way to phrase this without putting “distance” in scare quotes is to say that, if w is the normal vector of the optimal separating hyperplane, the closest points lie on the two lines \langle x_i, w \rangle = \pm 1.

Now that we have some intuition for the formulation of this problem, it isn’t a stretch to realize the following. While a dataset may include many points from either class on these two lines \langle x_i, w \rangle = \pm 1, the optimal hyperplane itself does not depend on any of the other points except these closest points.

This fact is enough to give these closest points a special name: the support vectors.

We’ll actually prove that support vectors “are all you need” with full rigor and detail next time, when we cast the optimization problem in this post into the “dual” setting. To avoid vague names, the formulation described in this post called the “primal” problem. The dual problem is derived from the primal problem, with special variables and constraints chosen based on the primal variables and constraints. Next time we’ll describe in brief detail what the dual does and why it’s important, but we won’t have nearly enough time to give a full understanding of duality in optimization (such a treatment would fill a book).

When we compute the dual of the SVM problem, we will see explicitly that the hyperplane can be written as a linear combination of the support vectors. As such, once you’ve found the optimal hyperplane, you can compress the training set into just the support vectors, and reproducing the same optimal solution becomes much, much faster. You can also use the support vectors to augment the SVM to incorporate streaming data (throw out all non-support vectors after every retraining).

Eventually, when we get to implementing the SVM from scratch, we’ll see all this in action.

Until then!


Holidays and Homicide

A Study In Data

Just before midnight on Thanksgiving, there was a murder by gunshot about four blocks from my home. Luckily I was in bed by then, but all of the commotion over the incident got me thinking: is murder disproportionately more common on Thanksgiving? What about Christmas, Valentine’s Day, or Saint Patrick’s Day?

Of course, with the right data set these are the kinds of questions one can answer! After an arduous Google search for agreeable data sets, I came across this project called the History of Violence Database (perhaps the most ominous database name ever!). Unfortunately most of the work in progress there puts the emphasis on history, cataloging homicide incidents only earlier than the 1920’s.

But one page I came across contained a complete list of the dates of each known homicide in San Francisco from 1849-2003. What’s more, it is available in a simple, comma-delimited format. (To all data compilers everywhere: this is how data should be transferred! Don’t only provide it in Excel, or SPSS, or whatever proprietary software you might use. Text files are universally accessible.)

With a little sed preprocessing and some Mathematica magic, I whipped together this chart of homicide counts by day of the year (click on the image to get a larger view):

Homicides in San Francsico 1849 – 2003, organized by day of the year.

Here the red grid lines mark the highest ten homicide counts, the green grid lines mark the lowest ten, and the blue lines mark specific holidays. Some of the blue and red lines cover the same date, and so in that case the red line is the one displayed. Finally, the horizontal yellow line represents the median homicide count of 19, so one can compare individual dates against the median.

Now it would be a terrible thing to “conclude” something about general human behavior from this particular data set. But I’m going to do it anyway because it’s fun, and it lets me make fascinating and controversial observations. Plus, I need interesting tidbits of information to drop at parties with math and statistics students.

Here are my observations:

  • There is a correlation between some holidays and homicide.
  • New Year’s Day is by far the most violent day of the year, followed by Christmas Day. On the other hand, Christmas Eve is only slightly above average.
  • The safest days of the year is January 5th. Having no other special recognition, it should be deemed National Peace Day, or at least National Too Pooped from New Year’s to Commit Murder Day.
  • New Year’s Day (likely, Morning) is not dangerous because of excessive alcohol consumption alone. If it were, Saint Patrick’s Day would surely be similar. Although to be fair, one should compare this with the same statistic for Dublin.
  • None of the following holidays are significantly more dangerous than the average day: Groundhog Day, Valentine’s Day, St. Patrick’s Day, April Fool’s Day, Cinco de Mayo, my birthday (August 28th), Halloween, Veteran’s Day, and New Year’s Eve (before midnight).
  • On the other hand, the days following July 4th are quite vicious, even though July 3rd is very quiet.
  • The days near November 24th are particularly violent because Thanksgiving (and Black Friday?) often fall on those days. Family gatherings are clearly high-risk events.
  • Last-minute Christmas shopping (Dec. 13th, 15th) obviously brings out the rage in everyone.
  • March and October are the docile months, while January, June, July, and December are the worst. Murder happens more often in the usual vacation months than at other times of the year.

Of course, some of the above points are meant to be facetious, but they bring up some interesting questions. Why does homicide show up more often during certain months of the year? It’s well known that most murder victims are personal acquaintances of the assailant (specifically, friends and family members); does this mean that family time rejuvenates or fuels strife? Are people more stressed during these times of year? Are June and July more violent simply because heat aggravates people, or at least gets them to interact with others more?

Unfortunately these aren’t the kinds of questions that lend themselves to mathematical proof, and as such I can’t give a definitive answer. But feel free to voice your own opinion in the comments!

For your viewing pleasure, here are the homicide counts on each day for the top and bottom 67 days, respectively, in ascending order:

{{"04/05", 24}, {"04/19", 24}, {"05/15", 24}, {"05/24", 24},
 {"05/25", 24}, {"06/28", 24}, {"08/05", 24}, {"08/17", 24},
 {"09/01", 24}, {"09/03", 24}, {"09/19", 24}, {"09/20", 24},
 {"10/26", 24}, {"11/02", 24}, {"11/23", 24}, {"02/01", 25},
 {"02/08", 25}, {"02/11", 25}, {"02/15", 25}, {"04/21", 25}, 
 {"05/07", 25}, {"06/04", 25}, {"06/07", 25}, {"07/24", 25},
 {"08/01", 25}, {"08/25", 25}, {"09/07", 25}, {"10/21", 25},
 {"10/23", 25}, {"11/03", 25}, {"11/10", 25}, {"11/27", 25},
 {"01/06", 26}, {"01/18", 26}, {"03/01", 26}, {"03/24", 26},
 {"05/30", 26}, {"07/04", 26}, {"07/29", 26}, {"08/09", 26},
 {"09/21", 26}, {"11/09", 26}, {"11/25", 26}, {"12/06", 26},
 {"05/02", 27}, {"09/06", 27}, {"09/24", 27}, {"10/11", 27},
 {"11/08", 27}, {"12/12", 27}, {"12/20", 27}, {"07/18", 28},
 {"09/04", 28}, {"12/02", 28}, {"05/18", 29}, {"07/01", 29},
 {"07/07", 29}, {"10/27", 29}, {"11/24", 29}, {"01/28", 30},
 {"01/24", 31}, {"07/05", 31}, {"08/02", 31}, {"12/15", 31},
 {"12/13", 32}, {"12/25", 36}, {"01/01", 43}}
{{"01/05", 6}, {"02/29", 6}, {"06/20", 7}, {"07/03", 7},
 {"04/15", 8}, {"02/03", 9}, {"02/10", 9}, {"09/16", 9},
 {"03/04", 10}, {"05/16", 10}, {"09/29", 10}, {"10/12", 10},
 {"12/16", 10}, {"02/04", 11}, {"05/17", 11}, {"05/23", 11},
 {"06/06", 11}, {"06/12", 11}, {"07/11", 11}, {"09/22", 11},
 {"11/12", 11}, {"11/16", 11}, {"12/19", 11}, {"03/13", 12},
 {"03/20", 12}, {"05/21", 12}, {"05/29", 12}, {"07/14", 12},
 {"08/13", 12}, {"09/05", 12}, {"10/28", 12}, {"11/22", 12},
 {"01/19", 13}, {"02/06", 13}, {"02/09", 13}, {"04/22", 13},
 {"04/28", 13}, {"05/04", 13}, {"05/11", 13}, {"08/27", 13},
 {"09/15", 13}, {"10/03", 13}, {"10/13", 13}, {"12/03", 13},
 {"01/10", 14}, {"01/26", 14}, {"01/31", 14}, {"02/13", 14},
 {"02/18", 14}, {"02/23", 14}, {"03/14", 14}, {"03/15", 14},
 {"03/26", 14}, {"03/31", 14}, {"04/11", 14}, {"05/12", 14},
 {"05/28", 14}, {"06/19", 14}, {"06/24", 14}, {"07/20", 14},
 {"07/31", 14}, {"08/10", 14}, {"10/22", 14}, {"12/22", 14},
 {"01/07", 15}, {"01/14", 15}, {"01/16", 15}}

Unfortunately many holidays are defined year by year. Thanksgiving, Easter, Mother’s Day, Father’s Day, MLK Jr. Day, Memorial Day, and the Chinese New Year all can’t be analyzed with this chart because they fall on different days each year. It would not be very difficult to organize this data set according to the rules of those special holidays. We leave it as an exercise to the reader to do so. Remember that the data and Mathematica notebook used in this post are available from this blog’s Github page.

And of course, it would be quite amazing to find a data set which provides the dates of (even the last fifty years of) homicide history in the entire US, and not just one city. If any readers know of such a data set, please let me know.

Until next time!