Martingales and the Optional Stopping Theorem

This is a guest post by my colleague Adam Lelkes.

The goal of this primer is to introduce an important and beautiful tool from probability theory, a model of fair betting games called martingales. In this post I will assume that the reader is familiar with the basics of probability theory. For those that need to refresh their knowledge, Jeremy’s excellent primers (1, 2) are a good place to start.

The Geometric Distribution and the ABRACADABRA Problem

Before we start playing with martingales, let’s start with an easy exercise. Consider the following experiment: we throw an ordinary die repeatedly until the first time a six appears. How many throws will this take in expectation? The reader might recognize immediately that this exercise can be easily solved using the basic properties of the geometric distribution, which models this experiment exactly. We have independent trials, every trial succeeding with some fixed probability p. If X denotes the number of trials needed to get the first success, then clearly \Pr(X = k) = (1-p)^{k-1} p (since first we need k-1 failures which occur independently with probability 1-p, then we need one success which happens with probability p). Thus the expected value of X is

\displaystyle E(X) = \sum_{k=1}^\infty k P(X = k) = \sum_{k=1}^\infty k (1-p)^{k-1} p = \frac1p

by basic calculus. In particular, if success is defined as getting a six, then p=1/6 thus the expected time is 1/p=6.

Now let us move on to a somewhat similar, but more interesting and difficult problem, the ABRACADABRA problem. Here we need two things for our experiment, a monkey and a typewriter. The monkey is asked to start bashing random keys on a typewriter. For simplicity’s sake, we assume that the typewriter has exactly 26 keys corresponding to the 26 letters of the English alphabet and the monkey hits each key with equal probability. There is a famous theorem in probability, the infinite monkey theorem, that states that given infinite time, our monkey will almost surely type the complete works of William Shakespeare. Unfortunately, according to astronomists the sun will begin to die in a few billion years, and the expected time we need to wait until a monkey types the complete works of William Shakespeare is orders of magnitude longer, so it is not feasible to use monkeys to produce works of literature.

So let’s scale down our goals, and let’s just wait until our monkey types the word ABRACADABRA. What is the expected time we need to wait until this happens? The reader’s first idea might be to use the geometric distribution again. ABRACADABRA is eleven letters long, the probability of getting one letter right is \frac{1}{26}, thus the probability of a random eleven-letter word being ABRACADABRA is exactly \left(\frac{1}{26}\right)^{11}. So if typing 11 letters is one trial, the expected number of trials is

\displaystyle \frac1{\left(\frac{1}{26}\right)^{11}}=26^{11}

which means 11\cdot 26^{11} keystrokes, right?

Well, not exactly. The problem is that we broke up our random string into eleven-letter blocks and waited until one block was ABRACADABRA. However, this word can start in the middle of a block. In other words, we considered a string a success only if the starting position of the word ABRACADABRA was divisible by 11. For example, FRZUNWRQXKLABRACADABRA would be recognized as success by this model but the same would not be true for AABRACADABRA. However, it is at least clear from this observation that 11\cdot 26^{11} is a strict upper bound for the expected waiting time. To find the exact solution, we need one very clever idea, which is the following:

Let’s Open a Casino!

Do I mean that abandoning our monkey and typewriter and investing our time and money in a casino is a better idea, at least in financial terms? This might indeed be the case, but here we will use a casino to determine the expected wait time for the ABRACADABRA problem. Unfortunately we won’t make any money along the way (in expectation) since our casino will be a fair one.

Let’s do the following thought experiment: let’s open a casino next to our typewriter. Before each keystroke, a new gambler comes to our casino and bets $1 that the next letter will be A. If he loses, he goes home disappointed. If he wins, he bets all the money he won on the event that the next letter will be B. Again, if he loses, he goes home disappointed. (This won’t wreak havoc on his financial situation, though, as he only loses $1 of his own money.) If he wins again, he bets all the money on the event that the next letter will be R, and so on.

If a gambler wins, how much does he win? We said that the casino would be fair, i.e. the expected outcome should be zero. That means that it the gambler bets $1, he should receive $26 if he wins, since the probability of getting the next letter right is exactly \frac{1}{26} (thus the expected value of the change in the gambler’s fortune is \frac{25}{26}\cdot (-1) + \frac{1}{26}\cdot (+25) = 0.

Let’s keep playing this game until the word ABRACADABRA first appears and let’s denote the number of keystrokes up to this time as T. As soon as we see this word, we close our casino. How much was the revenue of our casino then? Remember that before each keystroke, a new gambler comes in and bets $1, and if he wins, he will only bet the money he has received so far, so our revenue will be exactly T dollars.

How much will we have to pay for the winners? Note that the only winners in the last round are the players who bet on A. How many of them are there? There is one that just came in before the last keystroke and this was his first bet. He wins $26. There was one who came three keystrokes earlier and he made four successful bets (ABRA). He wins \$26^4. Finally there is the luckiest gambler who went through the whole ABRACADABRA sequence, his prize will be \$26^{11}. Thus our casino will have to give out 26^{11}+26^4+26 dollars in total, which is just under the price of 200,000 WhatsApp acquisitions.

Now we will make one crucial observation: even at the time when we close the casino, the casino is fair! Thus in expectation our expenses will be equal to our income. Our income is T dollars, the expected value of our expenses is 26^{11}+26^4+26 dollars, thus E(T)=26^{11}+26^4+26. A beautiful solution, isn’t it? So if our monkey types at 150 characters per minute on average, we will have to wait around 47 million years until we see ABRACADABRA. Oh well.

Time to be More Formal

After giving an intuitive outline of the solution, it is time to formalize the concepts that we used, to translate our fairy tales into mathematics. The mathematical model of the fair casino is called a martingale, named after a class of betting strategies that enjoyed popularity in 18th century France. The gambler’s fortune (or the casino’s, depending on our viewpoint) can be modeled with a sequence of random variables. X_0 will denote the gambler’s fortune before the game starts, X_1 the fortune after one round and so on. Such a sequence of random variables is called a stochastic process. We will require the expected value of the gambler’s fortune to be always finite.

How can we formalize the fairness of the game? Fairness means that the gambler’s fortune does not change in expectation, i.e. the expected value of X_n, given X_1, X_2, \ldots, X_{n-1} is the same as X_{n-1}. This can be written as E(X_n | X_1, X_2, \ldots, X_{n-1}) = X_{n-1} or, equivalently, E(X_n - X_{n-1} | X_1, X_2, \ldots, X_{n-1}) = 0.

The reader might be less comfortable with the first formulation. What does it mean, after all, that the conditional expected value of a random variable is another random variable? Shouldn’t the expected value be a number? The answer is that in order to have solid theoretical foundations for the definition of a martingale, we need a more sophisticated notion of conditional expectations. Such sophistication involves measure theory, which is outside the scope of this post. We will instead naively accept the definition above, and the reader can look up all the formal details in any serious probability text (such as [1]).

Clearly the fair casino we constructed for the ABRACADABRA exercise is an example of a martingale. Another example is the simple symmetric random walk on the number line: we start at 0, toss a coin in each step, and move one step in the positive or negative direction based on the outcome of our coin toss.

The Optional Stopping Theorem

Remember that we closed our casino as soon as the word ABRACADABRA appeared and we claimed that our casino was also fair at that time. In mathematical language, the closed casino is called a stopped martingale. The stopped martingale is constructed as follows: we wait until our martingale X exhibits a certain behaviour (e.g. the word ABRACADABRA is typed by the monkey), and we define a new martingale X’ as follows: let X'_n = X_n if n < T and X'_n = X_T if n \ge T where T denotes the stopping time, i.e. the time at which the desired event occurs. Notice that T itself is a random variable.

We require our stopping time T to depend only on the past, i.e. that at any time we should be able to decide whether the event that we are waiting for has already happened or not (without looking into the future). This is a very reasonable requirement. If we could look into the future, we could obviously cheat by closing our casino just before some gambler would win a huge prize.

We said that the expected wealth of the casino at the stopping time is the same as the initial wealth. This is guaranteed by Doob’s optional stopping theorem, which states that under certain conditions, the expected value of a martingale at the stopping time is equal to its expected initial value.

Theorem: (Doob’s optional stopping theorem) Let X_n be a martingale stopped at step T, and suppose one of the following three conditions hold:

  1. The stopping time T is almost surely bounded by some constant;
  2. The stopping time T is almost surely finite and every step of the stopped martingale X_n is almost surely bounded by some constant; or
  3. The expected stopping time E(T) is finite and the absolute value of the martingale increments |X_n-X_{n-1}| are almost surely bounded by a constant.

Then E(X_T) = E(X_0).

We omit the proof because it requires measure theory, but the interested reader can see it in these notes.

For applications, (1) and (2) are the trivial cases. In the ABRACADABRA problem, the third condition holds: the expected stopping time is finite (in fact, we showed using the geometric distribution that it is less than 26^{12}) and the absolute value of a martingale increment is either 1 or a net payoff which is bounded by 26^{11}+26^4+26. This shows that our solution is indeed correct.

Gambler’s Ruin

Another famous application of martingales is the gambler’s ruin problem. This problem models the following game: there are two players, the first player has a dollars, the second player has b dollars. In each round they toss a coin and the loser gives one dollar to the winner. The game ends when one of the players runs out of money. There are two obvious questions: (1) what is the probability that the first player wins and (2) how long will the game take in expectation?

Let X_n denote the change in the second player’s fortune, and set X_0 = 0. Let T_k denote the first time s when X_s = k. Then our first question can be formalized as trying to determine \Pr(T_{-b} < T_a). Let t = \min \{ T_{-b}, T_a\}. Clearly t is a stopping time. By the optional stopping theorem we have that

\displaystyle 0=E(X_0)=E(X_t)=-b\Pr(T_{-b} < T_a)+a(1-\Pr(T_{-b} < T_a))

thus \Pr(T_{-b} < T_a)=\frac{a}{a+b}.

I would like to ask the reader to try to answer the second question. It is a little bit trickier than the first one, though, so here is a hint: X_n^2-n is also a martingale (prove it), and applying the optional stopping theorem to it leads to the answer.

A Randomized Algorithm for 2-SAT

The reader is probably familiar with 3-SAT, the first problem shown to be NP-complete. Recall that 3-SAT is the following problem: given a boolean formula in conjunctive normal form with at most three literals in each clause, decide whether there is a satisfying truth assignment. It is natural to ask if or why 3 is special, i.e. why don’t we work with k-SAT for some k \ne 3 instead? Clearly the hardness of the problem is monotone increasing in k since k-SAT is a special case of (k+1)-SAT. On the other hand, SAT (without any bound on the number of literals per clause) is clearly in NP, thus 3-SAT is just as hard as k-SAT for any k>3. So the only question is: what can we say about 2-SAT?

It turns out that 2-SAT is easier than satisfiability in general: 2-SAT is in P. There are many algorithms for solving 2-SAT. Here is one deterministic algorithm: associate a graph to the 2-SAT instance such that there is one vertex for each variable and each negated variable and the literals x and y are connected by a directed edge if there is a clause (\bar x \lor y). Recall that \bar x \lor y is equivalent to x \implies y, so the edges show the implications between the variables. Clearly the 2-SAT instance is not satisfiable if there is a variable x such that there are directed paths x \to \bar x and \bar x \to x (since x \Leftrightarrow \bar x is always false). It can be shown that this is not only a sufficient but also a necessary condition for unsatisfiability, hence the 2-SAT instance is satisfiable if and only if there is are no such path. If there are directed paths from one vertex of a graph to another and vice versa then they are said to belong to the same strongly connected component. There are several graph algorithms for finding strongly connected components of directed graphs, the most well-known algorithms are all based on depth-first search.

Now we give a very simple randomized algorithm for 2-SAT (due to Christos Papadimitriou in a ’91 paper): start with an arbitrary truth assignment and while there are unsatisfied clauses, pick one and flip the truth value of a random literal in it. Stop after O(n^2) rounds where n denotes the number of variables. Clearly if the formula is not satisfiable then nothing can go wrong, we will never find a satisfying truth assignment. If the formula is satisfiable, we want to argue that with high probability we will find a satisfying truth assignment in O(n^2) steps.

The idea of the proof is the following: fix an arbitrary satisfying truth assignment and consider the Hamming distance of our current assignment from it. The Hamming distance of two truth assignments (or in general, of two binary vectors) is the number of coordinates in which they differ. Since we flip one bit in every step, this Hamming distance changes by \pm 1 in every round. It also easy to see that in every step the distance is at least as likely to be decreased as to be increased (since we pick an unsatisfied clause, which means at least one of the two literals in the clause differs in value from the satisfying assignment).

Thus this is an unfair “gambler’s ruin” problem where the gambler’s fortune is the Hamming distance from the solution, and it decreases with probability at least \frac{1}{2}. Such a stochastic process is called a supermartingale — and this is arguably a better model for real-life casinos. (If we flip the inequality, the stochastic process we get is called a submartingale.) Also, in this case the gambler’s fortune (the Hamming distance) cannot increase beyond n. We can also think of this process as a random walk on the set of integers: we start at some number and in each round we make one step to the left or to the right with some probability. If we use random walk terminology, 0 is called an absorbing barrier since we stop the process when we reach 0. The number n, on the other hand, is called a reflecting barrier: we cannot reach n+1, and whenever we get close we always bounce back.

There is an equivalent version of the optimal stopping theorem for supermartingales and submartingales, where the conditions are the same but the consequence holds with an inequality instead of equality. It follows from the optional stopping theorem that the gambler will be ruined (i.e. a satisfying truth assignment will be found) in O(n^2) steps with high probability.

[1] For a reference on stochastic processes and martingales, see the text of Durrett .

About these ads

(Finite) Fields — A Primer

So far on this blog we’ve given some introductory notes on a few kinds of algebraic structures in mathematics (most notably groups and rings, but also monoids). Fields are the next natural step in the progression.

If the reader is comfortable with rings, then a field is extremely simple to describe: they’re just commutative rings with 0 and 1, where every nonzero element has a multiplicative inverse. We’ll give a list of all of the properties that go into this “simple” definition in a moment, but an even more simple way to describe a field is as a place where “arithmetic makes sense.” That is, you get operations for +,-, \cdot , / which satisfy the expected properties of addition, subtraction, multiplication, and division. So whatever the objects in your field are (and sometimes they are quite weird objects), they behave like usual numbers in a very concrete sense.

So here’s the official definition of a field. We call a set F a field if it is endowed with two binary operations addition (+) and multiplication (\cdot, or just symbol juxtaposition) that have the following properties:

  • There is an element we call 0 which is the identity for addition.
  • Addition is commutative and associative.
  • Every element a \in F has a corresponding additive inverse b (which may equal a) for which a + b = 0.

These three properties are just the axioms of a (commutative) group, so we continue:

  • There is an element we call 1 (distinct from 0) which is the identity for multiplication.
  • Multiplication is commutative and associative.
  • Every nonzero element a \in F has a corresponding multiplicative inverse b (which may equal a) for which ab = 1.
  • Addition and multiplication distribute across each other as we expect.

If we exclude the existence of multiplicative inverses, these properties make F a commutative ring, and so we have the following chain of inclusions that describes it all

\displaystyle \textup{Fields} \subset \textup{Commutative Rings} \subset \textup{Rings} \subset \textup{Commutative Groups} \subset \textup{Groups}

The standard examples of fields are the real numbers \mathbb{R}, the rationals \mathbb{Q}, and the complex numbers \mathbb{C}. But of course there are many many more. The first natural question to ask about fields is: what can they look like?

For example, can there be any finite fields? A field F which as a set has only finitely many elements?

As we saw in our studies of groups and rings, the answer is yes! The simplest example is the set of integers modulo some prime p. We call them \mathbb{Z} / p \mathbb{Z}, or sometimes just \mathbb{Z}/p for short, and let’s rederive what we know about them now.

As a set, \mathbb{Z}/p consists of the integers \left \{ 0, 1, \dots, p-1 \right \}. The addition and multiplication operations are easy to define, they’re just usual addition and multiplication followed by a modulus. That is, we add by a + b \mod p and multiply with ab \mod p. This thing is clearly a commutative ring (because the integers form a commutative ring), so to show this is a field we need to show that everything has a multiplicative inverse.

There is a nice fact that allows us to do this: an element a has an inverse if and only if the only way for it to divide zero is the trivial way 0a = 0. Here’s a proof. For one direction, suppose a divides zero nontrivially, that is there is some c \neq 0 with ac = 0. Then if a had an inverse b, then 0 = b(ac) = (ba)c = c, but that’s very embarrassing for c because it claimed to be nonzero. Now suppose a only divides zero in the trivial way. Then look at all possible ways to multiply a by other nonzero elements of F. No two can give you the same result because if ax = ay then (without using multiplicative inverses) a(x-y) = 0, but we know that a can only divide zero in the trivial way so x=y. In other words, the map “multiplication by a” is injective. Because the set of nonzero elements of F is finite you have to hit everything (the map is in fact a bijection), and some x will give you ax = 1.

Now let’s use this fact on \mathbb{Z}/p in the obvious way. Since p is a prime, there are no two smaller numbers a, b < p so that ab = p. But in \mathbb{Z}/p the number p is equivalent to zero (mod p)! So \mathbb{Z}/p has no nontrivial zero divisors, and so every element has an inverse, and so it’s a finite field with p elements.

The next question is obvious: can we get finite fields of other sizes? The answer turns out to be yes, but you can’t get finite fields of any size. Let’s see why.

Characteristics and Vector Spaces

Say you have a finite field k (lower-case k is the standard letter for a field, so let’s forget about F). Beacuse the field is finite, if you take 1 and keep adding it to itself you’ll eventually run out of field elements. That is, n = 1 + 1 + \dots + 1 = 0 at some point. How do I know it’s zero and doesn’t keep cycling never hitting zero? Well if at two points n = m \neq 0, then n-m = 0 is a time where you hit zero, contradicting the claim.

Now we define \textup{char}(k), the characteristic of k, to be the smallest n (sums of 1 with itself) for which n = 0. If there is no such n (this can happen if k is infinite, but doesn’t always happen for infinite fields), then we say the characteristic is zero. It would probably make more sense to say the characteristic is infinite, but that’s just the way it is. Of course, for finite fields the characteristic is always positive. So what can we say about this number? We have seen lots of example where it’s prime, but is it always prime? It turns out the answer is yes!

For if ab = n = \textup{char}(k) is composite, then by the minimality of n we get a,b \neq 0, but ab = n = 0. This can’t happen by our above observation, because being a zero divisor means you have no inverse! Contradiction, sucker.

But it might happen that there are elements of k that can’t be written as 1 + 1 + \dots + 1 for any number of terms. We’ll construct examples in a minute (in fact, we’ll classify all finite fields), but we already have a lot of information about what those fields might look like. Indeed, since every field has 1 in it, we just showed that every finite field contains a smaller field (a subfield) of all the ways to add 1 to itself. Since the characteristic is prime, the subfield is a copy of \mathbb{Z}/p for p = \textup{char}(k). We call this special subfield the prime subfield of k.

The relationship between the possible other elements of k and the prime subfield is very neat. Because think about it: if k is your field and F is your prime subfield, then the elements of k can interact with F just like any other field elements. But if we separate k from F (make a separate copy of F), and just think of k as having addition, then the relationship with F is that of a vector space! In fact, whenever you have two fields k \subset k', the latter has the structure of a vector space over the former.

Back to finite fields, k is a vector space over its prime subfield, and now we can impose all the power and might of linear algebra against it. What’s it’s dimension? Finite because k is a finite set! Call the dimension m, then we get a basis v_1, \dots, v_m. Then the crucial part: every element of k has a unique representation in terms of the basis. So they are expanded in the form

\displaystyle f_1v_1 + \dots + f_mv_m

where the f_i come from F. But now, since these are all just field operations, every possible choice for the f_i has to give you a different field element. And how many choices are there for the f_i? Each one has exactly |F| = \textup{char}(k) = p. And so by counting we get that k has p^m many elements.

This is getting exciting quickly, but we have to pace ourselves! This is a constraint on the possible size of a finite field, but can we realize it for all choices of p, m? The answer is again yes, and in the next section we’ll see how.  But reader be warned: the formal way to do it requires a little bit of familiarity with ideals in rings to understand the construction. I’ll try to avoid too much technical stuff, but if you don’t know what an ideal is, you should expect to get lost (it’s okay, that’s the nature of learning new math!).

Constructing All Finite Fields

Let’s describe a construction. Take a finite field k of characteristic p, and say you want to make a field of size p^m. What we need to do is construct a field extension, that is, find a bigger field containing k so that the vector space dimension of our new field over k is exactly m.

What you can do is first form the ring of polynomials with coefficients in k. This ring is usually denoted k[x], and it’s easy to check it’s a ring (polynomial addition and multiplication are defined in the usual way). Now if I were speaking to a mathematician I would say, “From here you take an irreducible monic polynomial p(x) of degree m, and quotient your ring by the principal ideal generated by p. The result is the field we want!”

In less compact terms, the idea is exactly the same as modular arithmetic on integers. Instead of doing arithmetic with integers modulo some prime (an irreducible integer), we’re doing arithmetic with polynomials modulo some irreducible polynomial p(x). Now you see the reason I used p for a polynomial, to highlight the parallel thought process. What I mean by “modulo a polynomial” is that you divide some element f in your ring by p as much as you can, until the degree of the remainder is smaller than the degree of p(x), and that’s the element of your quotient. The Euclidean algorithm guarantees that we can do this no matter what k is (in the formal parlance, k[x] is called a Euclidean domain for this very reason). In still other words, the “quotient structure” tells us that two polynomials f, g \in k[x] are considered to be the same in k[x] / p if and only if f - g is divisible by p. This is actually the same definition for \mathbb{Z}/p, with polynomials replacing numbers, and if you haven’t already you can start to imagine why people decided to study rings in general.

Let’s do a specific example to see what’s going on. Say we’re working with k = \mathbb{Z}/3 and we want to compute a field of size 27 = 3^3. First we need to find a monic irreducible polynomial of degree 3. For now, I just happen to know one: p(x) = x^3 - x + 1. In fact, we can check it’s irreducible, because to be reducible it would have to have a linear factor and hence a root in \mathbb{Z}/3. But it’s easy to see that if you compute p(0), p(1), p(2) and take (mod 3) you never get zero.

So I’m calling this new ring

\displaystyle \frac{\mathbb{Z}/3[x]}{(x^3 - x + 1)}

It happens to be a field, and we can argue it with a whole lot of ring theory. First, we know an irreducible element of this ring is also prime (because the ring is a unique factorization domain), and prime elements generate maximal ideals (because it’s a principal ideal domain), and if you quotient by a maximal ideal you get a field (true of all rings).

But if we want to avoid that kind of argument and just focus on this ring, we can explicitly construct inverses. Say you have a polynomial f(x), and for illustration purposes we’ll choose f(x) = x^4 + x^2 - 1. Now in the quotient ring we could do polynomial long division to find remainders, but another trick is just to notice that the quotient is equivalent to the condition that x^3 = x - 1. So we can reduce f(x) by applying this rule to x^4 = x^3 x to get

\displaystyle f(x) = x^2 + x(x-1) - 1 = 2x^2 - x - 1

Now what’s the inverse of f(x)? Well we need a polynomial g(x) = ax^2 + bx + c whose product with f gives us something which is equivalent to 1, after you reduce by x^3 - x + 1. A few minutes of algebra later and you’ll discover that this is equivalent to the following polynomial being identically 1

\displaystyle (a-b+2c)x^2 + (-3a+b-c)x + (a - 2b - 2c) = 1

In other words, we get a system of linear equations which we need to solve:

\displaystyle \begin{aligned} a & - & b & + & 2c & = 0 \\ -3a & + & b & - & c &= 0 \\ a & - & 2b & - & 2c &= 1 \end{aligned}

And from here you can solve with your favorite linear algebra techniques. This is a good exercise for working in fields, because you get to abuse the prime subfield being characteristic 3 to say terrifying things like -1 = 2 and 6b = 0. The end result is that the inverse polynomial is 2x^2 + x + 1, and if you were really determined you could write a program to compute these linear systems for any input polynomial and ensure they’re all solvable. We prefer the ring theoretic proof.

In any case, it’s clear that taking a polynomial ring like this and quotienting by a monic irreducible polynomial gives you a field. We just control the size of that field by choosing the degree of the irreducible polynomial to our satisfaction. And that’s how we get all finite fields!

One Last Word on Irreducible Polynomials

One thing we’ve avoided is the question of why irreducible monic polynomials exist of all possible degrees m over any \mathbb{Z}/p (and as a consequence we can actually construct finite fields of all possible sizes).

The answer requires a bit of group theory to prove this, but it turns out that the polynomial x^{p^m} - x has all degree m monic irreducible polynomials as factors. But perhaps a better question (for computer scientists) is how do we work over a finite field in practice? One way is to work with polynomial arithmetic as we described above, but this has some downsides: it requires us to compute these irreducible monic polynomials (which doesn’t sound so hard, maybe), to do polynomial long division every time we add, subtract, or multiply, and to compute inverses by solving a linear system.

But we can do better for some special finite fields, say where the characteristic is 2 (smells like binary) or we’re only looking at F_{p^2}. The benefit there is that we aren’t forced to use polynomials. We can come up with some other kind of structure (say, matrices of a special form) which happens to have the same field structure and makes computing operations relatively painless. We’ll see how this is done in the future, and see it applied to cryptography when we continue with our series on elliptic curve cryptography.

Until then!

How to Conquer Tensorphobia

A professor at Stanford once said,

If you really want to impress your friends and confound your enemies, you can invoke tensor products… People run in terror from the \otimes symbol.

He was explaining some aspects of multidimensional Fourier transforms, but this comment is only half in jest; people get confused by tensor products. It’s often for good reason. People who really understand tensors feel obligated to explain it using abstract language (specifically, universal properties). And the people who explain it in elementary terms don’t really understand tensors.

This post is an attempt to bridge the gap between the elementary and advanced understandings of tensors. We’ll start with the elementary (axiomatic) approach, just to get a good feel for the objects we’re working with and their essential properties. Then we’ll transition to the “universal” mode of thought, with the express purpose of enlightening us as to why the properties are both necessary and natural.

But above all, we intend to be sufficiently friendly so as to not make anybody run in fear. This means lots of examples and preferring words over symbols. Unfortunately, we simply can’t get by without the reader knowing the very basics of linear algebra (the content of our first two primers on linear algebra (1) (2), though the only important part of the second is the definition of an inner product).

So let’s begin.

Tensors as a Bunch of Axioms

Before we get into the thick of things I should clarify some basic terminology. Tensors are just vectors in a special vector space. We’ll see that such a vector space comes about by combining two smaller vector spaces via a tensor product. So the tensor product is an operation combining vector spaces, and tensors are the elements of the resulting vector space.

Now the use of the word product is quite suggestive, and it may lead one to think that a tensor product is similar or related to the usual direct product of vector spaces. In fact they are related (in very precise sense), but they are far from similar. If you were pressed, however, you could start with the direct product of two vector spaces and take a mathematical machete to it until it’s so disfigured that you have to give it a new name (the tensor product).

With that image in mind let’s see how that is done. For the sake of generality we’ll talk about two arbitrary finite-dimensional vector spaces V, W of dimensions n, m. Recall that the direct product  V \times W is the vector space of pairs (v,w) where v comes from V and w from W. Recall that addition in this vector space is defined componentwise ((v_1,w_1) + (v_2, w_2) = (v_1 + v_2, w_1 + w_2)) and scalar multiplication scales both components \lambda (v,w) = (\lambda v, \lambda w).

To get the tensor product space V \otimes W, we make the following modifications. First, we redefine what it means to do scalar multiplication. In this brave new tensor world, scalar multiplication of the whole vector-pair is declared to be the same as scalar multiplication of any component you want. In symbols,

\displaystyle \lambda (v, w) = (\lambda v, w) = (v, \lambda w)

for all choices of scalars \lambda and vectors v, w. Second, we change the addition operation so that it only works if one of the two components are the same. In symbols, we declare that

(v, w) + (v', w) = (v + v', w)

only works because w is the same in both pieces, and with the same rule applying if we switch the positions of v,w above. All other additions are simply declared to be new vectors. I.e. (x,y) + (z,w) is simply itself. It’s a valid addition — we need to be able to add stuff to be a vector space — but you just can’t combine it any further unless you can use the scalar multiplication to factor out some things so that y=w or x=z. To say it still one more time, a general element of the tensor V \otimes W is a sum of these pairs that can or can’t be combined by addition (in general things can’t always be combined).

Finally, we rename the pair (v,w) to v \otimes w, to distinguish it from the old vector space V \times W that we’ve totally butchered and reanimated, and we call the tensor product space as a whole V \otimes W. Those familiar with this kind of abstract algebra will recognize quotient spaces at work here, but we won’t use that language except to note that we cover quotients and free spaces elsewhere on this blog, and that’s the formality we’re ignoring.

As an example, say we’re taking the tensor product of two copies of \mathbb{R}. This means that our space \mathbb{R} \otimes \mathbb{R} is comprised of vectors like 3 \otimes 5, and moreover that the following operations are completely legitimate.

3 \otimes 5 + 1 \otimes (-5) = 3 \otimes 5 + (-1) \otimes 5 = 2 \otimes 5

6 \otimes 1 + 3\pi \otimes \pi = 3 \otimes 2 + 3 \otimes \pi^2 = 3 \otimes (2 + \pi^2)

Cool. This seemingly innocuous change clearly has huge implications on the structure of the space. We’ll get to specifics about how different tensors are from regular products later in this post, but for now we haven’t even proved this thing is a vector space. It might not be obvious, but if you go and do the formalities and write the thing as a quotient of a free vector space (as we mentioned we wouldn’t do) then you know that quotients of vector spaces are again vector spaces. So we get that one for free. But even without that it should be pretty obvious: we’re essentially just declaring that all the axioms of a vector space hold when we want them to. So if you were wondering whether

\lambda (a \otimes b + c \otimes d) = \lambda(a \otimes b) + \lambda(c \otimes d)

The answer is yes, by force of will.

So just to recall, the axioms of a tensor space V \otimes W are

  1. The “basic” vectors are v \otimes w for v \in V, w \in W, and they’re used to build up all other vectors.
  2. Addition is symbolic, unless one of the components is the same in both addends, in which case (v_1, w) + (v_2, w) = (v_1+ v_2, w) and (v, w_1) + (v,w_2) = (v, w_1 + w_2).
  3. You can freely move scalar multiples around the components of v \otimes w.
  4. The rest of the vector space axioms (distributivity, additive inverses, etc) are assumed with extreme prejudice.

Naturally, one can extend this definition to n-fold tensor products, like V_1 \otimes V_2 \otimes \dots \otimes V_d. Here we write the vectors as sums of things like v_1 \otimes \dots \otimes v_d, and we enforce that addition can only be combined if all but one coordinates are the same in the addends, and scalar multiples move around to all coordinates equally freely.

So where does it come from?!

By now we have this definition and we can play with tensors, but any sane mathematically minded person would protest, “What the hell would cause anyone to come up with such a definition? I thought mathematics was supposed to be elegant!”

It’s an understandable position, but let me now try to convince you that tensor products are very natural. The main intrinsic motivation for the rest of this section will be this:

We have all these interesting mathematical objects, but over the years we have discovered that the maps between objects are the truly interesting things.

A fair warning: although we’ll maintain a gradual pace and informal language in what follows, by the end of this section you’ll be reading more or less mature 20th-century mathematics. It’s quite alright to stop with the elementary understanding (and skip to the last section for some cool notes about computing), but we trust that the intrepid readers will push on.

So with that understanding we turn to multilinear maps. Of course, the first substantive thing we study in linear algebra is the notion of a linear map between vector spaces. That is, a map f: V \to W that factors through addition and scalar multiplication (i.e. f(v + v') = f(v) + f(v') and f(\lambda v) = \lambda f(v)).

But it turns out that lots of maps we work with have much stronger properties worth studying. For example, if we think of matrix multiplication as an operation, call it m, then m takes in two matrices and spits out their product

m(A,B) = AB

Now what would be an appropriate notion of linearity for this map? Certainly it is linear in the first coordinate, because if we fix B then

m(A+C, B) = (A+C)B = AB + CB = m(A,B) + m(C,B)

And for the same reason it’s linear in the second coordinate. But it is most definitely not linear in both coordinates simultaneously. In other words,

m(A+B, C+D) = (A+B)(C+D) = AC + AD + BC + BD \neq AC + BD = m(A,C) + m(B,D)

In fact, if the only operation satisfying linearity in its two coordinates separately and also this kind of linearity is the zero map! (Try to prove this as an exercise.) So the strongest kind of linearity we could reasonably impose is that m is linear in each coordinate when all else is fixed. Note that this property allows us to shift around scalar multiples, too. For example,

\displaystyle m(\lambda A, B) = \lambda AB = A (\lambda B) = m(A, \lambda B) = \lambda m(A,B)

Starting to see the wispy strands of a connection to tensors? Good, but hold it in for a bit longer. This single-coordinate-wise-linear property is called bilinearity when we only have two coordinates, and multilinearity when we have more.

Here are some examples of nice multilinear maps that show up everywhere:

  • If V is an inner product space over \mathbb{R}, then the inner product is bilinear.
  • The determinant of a matrix is a multilinear map if we view the columns of the matrix as vector arguments.
  • The cross product of vectors in \mathbb{R}^3 is bilinear.

There are many other examples, but you should have at least passing familiarity with these notions, and it’s enough to convince us that multilinearity is worth studying abstractly.

And so what tensors do is give a sort of classification of multilinear maps. The idea is that every multilinear map f from a product vector space U_1 \times \dots \times U_d to any vector space Y can be written first as a multilinear map to the tensor space

\displaystyle \alpha : U_1 \times \dots \times U_d \to U_1 \otimes \dots \otimes U_d

Followed by a linear map to Y,

\displaystyle \hat{f} : U_1 \otimes \dots \otimes U_d \to Y

And the important part is that \alpha doesn’t depend on the original f (but \hat{f} does). One usually draws this as a single diagram:


And to say this diagram commutes is to say that all possible ways to get from one point to another are equivalent (the compositions of the corresponding maps you follow are equal, i.e. f = \hat{f} \alpha).

In fuzzy words, the tensor product is like the gatekeeper of all multilinear maps, and \alpha is the gate. Yet another way to say this is that \alpha is the most general possible multilinear map that can be constructed from U_1 \times \dots \times U_d. Moreover, the tensor product itself is uniquely defined by having a “most-general” \alpha (up to isomorphism). This notion is often referred to by mathematicians as the “universal property” of the tensor product. And they might say something like “the tensor product is initial with respect to multilinear mappings from the standard product.” We discuss language like this in detail in this blog’s series on category theory, but it’s essentially a super-compact (and almost too vague) way of saying what the diagram says.

Let’s explore this definition when we specialize to a tensor of two vector spaces, and it will give us a good understanding of \alpha (which is really incredibly simple, but people like to muck it up with choices of coordinates and summations). So fix V, W as vector spaces and look at the diagram


What is \alpha in this case? Well it just sends (v,w) \mapsto v \otimes w. Is this map multilinear? Well if we fix w then

\displaystyle \alpha(v_1 + v_2, w) = (v_1 + v_2) \otimes w = v_1 \otimes w + v_2 \otimes w = \alpha(v_1, w) + \alpha (v_2, w)


\displaystyle \alpha(\lambda v, w) = (\lambda v) \otimes w = (\lambda) (v \otimes w) = \lambda \alpha(v,w)

And our familiarity with tensors now tells us that the other side holds too. Actually, rather than say this is a result of our “familiarity with tensors,” the truth is that this is how we know that we need to define the properties of tensors as we did. It’s all because we designed tensors to be the gatekeepers of multilinear maps!

So now let’s prove that all maps f : V \times W \to Y can be decomposed into an \alpha part and a \hat{f} part. To do this we need to know what data uniquely defines a multilinear map. For usual linear maps, all we had to do was define the effect of the map on each element of a basis (the rest was uniquely determined by the linearity property). We know what a basis of V \times W is, it’s just the union of the bases of the pieces. Say that V has a basis v_1, \dots, v_n and W has w_1, \dots, w_m, then a basis for the product is just ((v_1, 0), \dots, (v_n,0), (0,w_1), \dots, (0,w_m)).

But multilinear maps are more nuanced, because they have two arguments. In order to say “what they do on a basis” we really need to know how they act on all possible pairs of basis elements. For how else could we determine f(v_1 + v_2, w_1)? If there are n of the v_i‘s and m of the w_i‘s, then there are nm such pairs f(v_i, w_j).

Uncoincidentally, as V \otimes W is a vector space, its basis can also be constructed in terms of the bases of V and W. You simply take all possible tensors v_i \otimes w_j. Since every v \in V, w \in W can be written in terms of their bases, it’s clear than any tensor \sum_{k} a_k \otimes b_k can also be written in terms of the basis tensors v_i \otimes w_j (by simply expanding each a_k, b_k in terms of their respective bases, and getting a larger sum of more basic tensors).

Just to drive this point home, if (e_1, e_2, e_3) is a basis for \mathbb{R}^3, and (g_1, g_2) a basis for \mathbb{R}^2, then the tensor space \mathbb{R}^3 \otimes \mathbb{R}^2 has basis

(e_1 \otimes g_1, e_1 \otimes g_2, e_2 \otimes g_1, e_2 \otimes g_2, e_3 \otimes g_1, e_3 \otimes g_2)

It’s a theorem that finite-dimensional vector spaces of equal dimension are isomorphic, so the length of this basis (6) tells us that \mathbb{R}^3 \otimes \mathbb{R}^2 \cong \mathbb{R}^6.

So fine, back to decomposing f. All we have left to do is use the data given by f (the effect on pairs of basis elements) to define \hat{f} : V \otimes W \to Y. The definition is rather straightforward, as we have already made the suggestive move of showing that the basis for the tensor space (v_i \otimes w_j) and the definition of f (f(v_i, w_j)) are essentially the same.

That is, just take \hat{f}(v_i \otimes w_j) = f(v_i, w_j). Note that this is just defined on the basis elements, and so we extend to all other vectors in the tensor space by imposing linearity (defining \hat{f} to split across sums of tensors as needed). Is this well defined? Well, multilinearity of f forces it to be so. For if we had two equal tensors, say, \lambda v \otimes w = v \otimes \lambda w, then we know that f has to respect their equality, because f(\lambda v_i, w_j) = f(v_i, \lambda w_j), so \hat{f} will take the same value on equal tensors regardless of which representative we pick (where we decide to put the \lambda). The same idea works for sums, so everything checks out, and f(v,w) is equal to \hat{f} \alpha, as desired. Moreover, we didn’t make any choices in constructing \hat{f}. If you retrace our steps in the argument, you’ll see that everything was essentially decided for us once we fixed a choice of a basis (by our wise decisions in defining V \otimes W). Since the construction would be isomorphic if we changed the basis, our choice of \hat{f} is unique.

There is a lot more to say about tensors, and indeed there are some other useful ways to think about tensors that we’ve completely ignored. But this discussion should make it clear why we define tensors the way we do. Hopefully it eliminates most of the mystery in tensors, although there is still a lot of mystery in trying to compute stuff using tensors. So we’ll wrap up this post with a short discussion about that.

Computability and Stuff

It should be clear by now that plain product spaces V \times W and tensor product spaces V \otimes W are extremely different. In fact, they’re only related in that their underlying sets of vectors are built from pairs of vectors in V and W. Avid readers of this blog will also know that operations involving matrices (like row reduction, eigenvalue computations, etc.) are generally efficient, or at least they run in polynomial time so they’re not crazy impractically slow for modest inputs.

On the other hand, it turns out that almost every question you might want to ask about tensors is difficult to answer computationally. As with the definition of the tensor product, this is no mere coincidence. There is something deep going on with tensors, and it has serious implications regarding quantum computing. More on that in a future post, but for now let’s just focus on one hard problem to answer for tensors.

As you know, the most general way to write an element of a tensor space U_1 \otimes \dots \otimes U_d is as a sum of the basic-looking tensors.

\displaystyle \sum_{k} a_{1,k} \otimes a_{2,k} \otimes \dots \otimes a_{d,k}

where the a_{i,k} may be sums of vectors from U_i themselves. But as we saw with our examples over \mathbb{R}, there can be lots of different ways to write a tensor. If you’re lucky, you can write the entire tensor as a one-term sum, that is just a tensor a \otimes b. If you can do this we call the tensor a pure tensor, or a rank 1 tensor. We then have the following natural definition and problem:

Definition: The rank of a tensor x \in U_1 \otimes \dots \otimes U_d is the minimum number of terms in any representation of x as a sum of pure tensors. The one exception is the zero element, which has rank zero by convention.

Problem: Given a tensor x \in k^{n_1} \otimes k^{n_2} \otimes k^{n_3} where k is a field, compute its rank.

Of course this isn’t possible in standard computing models unless you can represent the elements of the field (and hence the elements of the vector space in question) in a computer program. So we restrict k to be either the rational numbers \mathbb{Q} or a finite field \mathbb{F}_{q}.

Even though the problem is simple to state, it was proved in 1990 (a result of Johan Håstad) that tensor rank is hard to compute. Specifically, the theorem is that

Theorem: Computing tensor rank is NP-hard when k = \mathbb{Q} and NP-complete when k is a finite field.

The details are given in Håstad’s paper, but the important work that followed essentially showed that most problems involving tensors are hard to compute (many of them by reduction from computing rank). This is unfortunate, but also displays the power of tensors. In fact, tensors are so powerful that many believe understanding them better will lead to insight in some very important problems, like finding faster matrix multiplication algorithms or proving circuit lower bounds (which is closely related to P vs NP). Finding low-rank tensor approximations is also a key technique in a lot of recent machine learning and data mining algorithms.

With this in mind, the enterprising reader will probably agree that understanding tensors is both valuable and useful. In the future of this blog we’ll hope to see some of these techniques, but at the very least we’ll see the return of tensors when we delve into quantum computing.

Until next time!

Lagrangians for the Amnesiac

For a while I’ve been meaning to do some more advanced posts on optimization problems of all flavors. One technique that comes up over and over again is Lagrange multipliers, so this post is going to be a leisurely reminder of that technique. I often forget how to do these basic calculus-type things, so it’s good practice.

We will assume something about the reader’s knowledge, but it’s a short list: know how to operate with vectors and the dot product, know how to take a partial derivative, and know that in single-variable calculus the local maxima and minima of a differentiable function f(x) occur when the derivative f'(x) vanishes. All of the functions we’ll work with in this post will have infinitely many derivatives (i.e. smooth). So things will be nice.

The Gradient

The gradient of a multivariable function is the natural extension of the derivative of a single-variable function. If f(x_1, \dots, x_n) is a differentiable function, the data of the gradient of f consists of all of the partial derivatives \partial f / \partial x_i. It’s usually written as a vector

\displaystyle \nabla f = \left ( \frac{\partial f}{\partial x_1}, \dots, \frac{\partial f}{\partial x_n} \right )

To make things easier for ourselves, we’ll just call f a function f(x) and understand x to be a vector in \mathbb{R}^n.

We can also think of \nabla f as a function which takes in vectors and spits out vectors, by plugging in the input vector into each \partial f / \partial x_i. And the reason we do this is because it lets us describe the derivative of f at a point as a linear map based on the gradient. That is, if we want to know how fast f is growing along a particular vector v and at a particular point (x, f(x)), we can just take a dot product of v with \nabla f(x). I like to call dot products inner products, and use the notation \left \langle \nabla f(x), v \right \rangle. Here v is a vector in \mathbb{R}^n which we think of as “tangent vectors” to the surface defined by f. And if we scale v bigger or smaller, the value of the derivative scales with it (of course, because the derivative is a linear map!). Usually we use unit vectors to represent directions, but there’s no reason we have to. Calculus textbooks often require this to define a “directional derivative,” but perhaps it is better to understand the linear algebra over memorizing these arbitrary choices.

For example, let f(x,y,z) = xyz. Then \nabla f = (yz, xz, xy), and \nabla f(1,2,1) = (2, 1, 2). Now if we pick a vector to go along, say, v = (0,-1,1), we get the derivative of f along v is \left \langle (2,1,2), (0,-1,1) \right \rangle = 1.

As importantly as computing derivatives is finding where the derivative is zero, and the geometry of the gradient can help us here. Specifically, if we think of our function f as a surface sitting in \mathbb{R}^{n+1} (as in the picture below), it’s not hard to see that the gradient vector points in the direction of steepest ascent of f. How do we know this? Well if you fix a point (x_1, \dots, x_n) and you’re forced to use a vector v of the same magnitude as \nabla f(x), how can you maximize the inner product \left \langle \nabla f(x), v \right \rangle? Well, you just pick v to be equal to \nabla f(x), of course! This will turn the dot product into the square norm of \nabla f(x).

The gradient points in the direction of steepest ascent

The gradient points in the direction of steepest ascent. (image source)

More generally, the operation of an inner product \left \langle -, v \right \rangle is geometrically the size of the projection of the argument onto v (scaled by the size of v), and projections of a vector w onto different directions than w can only be smaller in magnitude than w. Another way to see this is to know the “alternative” formula for the dot product

\displaystyle \left \langle v,w \right \rangle = \left \| v \right \| \left \| w \right \| \cos(\theta)

where \theta is the angle between the vectors (in \mathbb{R}^n). We might not know how to get that angle, and in this post we don’t care, but we do know that \cos(\theta) is between -1 and 1. And so if v is fixed and we can’t change the norm of w but only its direction, we will maximize the dot product when the two vectors point in the same direction, when \theta is zero.

All of this is just to say that the gradient at a point can be interpreted as having a specific direction. It’s the direction of steepest ascent of the surface f, and it’s size tells you how steep f is at that point. The opposite direction is the direction of steepest descent, and the orthogonal directions (when \theta = \pi /2) have derivative zero.

Now what happens if we’re at a local minimum or maximum? Well it’s necessary that f is flat, and so by our discussion above the derivatives in all directions must be zero. It’s a basic linear algebra proof to show that this means the gradient is the zero vector. You can prove this by asking what sorts of vectors w have a dot product of zero with all other vectors v?

Now once we have a local max or a local min, how do we tell which? The answer is actually a bit complicated, and it requires you to inspect the eigenvalues of the Hessian of f. We won’t dally on eigenvalues except to explain the idea in brief: for an n variable function f the Hessian of f at x is an n-by-n matrix where the i,j entry is the value of (\partial f / \partial x_i \partial x_j )(x). It just so turns out that if this matrix has only positive eigenvalues, then x is a local minimum. If the eigenvalues are all negative, it’s a local max. If some are negative and some are positive, then it’s a saddle point. And if zero is an eigenvalue then we’re screwed and can’t conclude anything without more work.

But all of this Hessian business isn’t particularly important for us, because most of our applications of the Lagrangian will work with functions where we already know that there is a unique global maximum or minimum. Finding where the gradient is zero is enough. As much as this author stresses the importance of linear algebra, we simply won’t need to compute any eigenvalues for this one.

What we will need to do is look at optimizing functions which are constrained by some equality conditions. This is where Lagrangians come into play.

Constrained by Equality

Often times we will want to find a minimum or maximum of a function f(x), but we will have additional constraints. The simplest kind is an equality constraint.

For example, we might want to find the maximum of the function f(x, y, z) = xyz requiring that the point (x,y,z) lies on the unit circle. One could write this in a “canonical form”

maximize xyz
subject to x^2 + y^2 + z^2 = 1

Way back in the scientific revolution, Fermat discovered a technique to solve such problems that was later generalized by Lagrange. The idea is to combine these constraints into one function whose gradient provides enough information to find a maximum. Clearly such information needs to include two things: that the gradient of xyz is zero, and that the constraint is satisfied.

First we rewrite the constraints as g(x,y,z) = x^2 + y^2 + x^2 - 1 = 0, because when we’re dealing with gradients we want things to be zero. Then we form the Lagrangian of the problem. We’ll give a precise definition in a minute, but it looks like this:

L(x,y,z,\lambda) = xyz + \lambda(x^2 + y^2 + z^2 - 1)

That is, we’ve added a new variable \lambda and added the two functions together. Let’s see what happens when we take a gradient:

\displaystyle \frac{\partial L}{\partial x} = yz + \lambda 2x

\displaystyle \frac{\partial L}{\partial y} = xz + \lambda 2y

\displaystyle \frac{\partial L}{\partial z} = xy + \lambda 2z

\displaystyle \frac{\partial L}{\partial \lambda} = x^2 + y^2 + z^2 - 1

Now if we require the gradient to be zero, the last equation is simply the original constraint, and the first three equations say that \nabla f (x,y,z) = \lambda \nabla g (x,y,z). In other words, we’re saying that the two gradients must point in the same direction for the function to provide a maximum. Solving for where these equations vanish gives some trivial solutions (one variable is \pm 1 and the rest zero, and \lambda = 0), and a solution defined by x^2 = y^2 = z^2 = 1/3 which is clearly the maximal of the choices.

Indeed, this will work in general, and you can see a geometric and analytic proof in these notes.

Specifically, if we have an optimization problem defined by an objective function f(x) to optimize, and a set of k equality constraints g_i(x) = 0, then we can form the Lagrangian

\displaystyle L(x, \lambda_1, \dots, \lambda_k) = f(x) + \sum_{i=1}^k \lambda_i g_i(x)

And then a theorem of Lagrange is that all optimal solutions x^* to the problem satisfy \nabla L(x^*, \lambda_1, \dots, \lambda_k) = 0 for some choice of \lambda _i. But then you have to go solve the system and figure out which of the solutions gives you your optimum.


As it turns out, there are some additional constraints you can add to your problem to guarantee your system has a solution. One nice condition is that f(x) is convexA function is convex if any point on a line segment between two points (x,f(x)) and (y,f(y)) has a value greater than f. In other words, for all 0 \leq t \leq 1:

\displaystyle f(tx + (1-t)y) \leq tf(x) + (1-t)f(y)

Some important examples of convex functions: exponentials, quadratics whose leading coefficient is positive, square norms of a vector variable, and linear functions.

Convex functions have this nice property that they have a unique local minimum value, and hence it must also be the global minimum. Why is this? Well if you have a local minimum x, and any other point y, then by virtue of being a local minimum there is some t sufficiently close to 1 so that:

\displaystyle f(x) \leq f(tx + (1-t)y) \leq tf(x) + (1-t)f(y)

And rearranging we get

\displaystyle (1-t)f(x) \leq (1-t)f(y)

So f(x) \leq f(y), and since y was arbitrary then x is the global minimum.

This alleviates our problem of having to sort through multiple solutions, and in particular it helps us to write programs to solve optimization problems: we know that techniques like gradient descent will never converge to a false local minimum.

That’s all for now! The next question we might shadowily ask: what happens if we add inequality constraints?