In this post, I want to discuss a very beautiful piece of mathematics I stumbled upon recently. As a warning, this post will be more mathematical than most, but I will still try and sand off the roughest mathematical edges. This post is adapted from a much more comprehensive post by Paata Ivanishvili. My goal is to distill the main idea to its essence, deferring the stochastic calculus until it cannot be avoided.

Jensen’s inequality is one of the most important results in probability.

Jensen’s inequality. Let be a (real) random variable and a convex function such that both and are defined. Then .

Here is the standard proof. A convex function has supporting lines. That is, at a point , there exists a slope such that for all . Invoke this result at and and take expectations to conclude that

In this post, I will outline a proof of Jensen’s inequality which is much longer and more complicated. Why do this? This more difficult proof illustrates an incredible powerful technique for proving inequalities, interpolation. The interpolation method can be used to prove a number of difficult and useful inequalities in probability theory and beyond. As an example, at the end of this post, we will see the Gaussian Jensen inequality, a striking generalization of Jensen’s inequality with many applications.

The idea of interpolation is as follows: Suppose I wish to prove for two numbers and . This may hard to do directly. With the interpolation method, I first construct a family of numbers , , such that and and show that is (weakly) increasing in . This is typically accomplished by showing the derivative is nonnegative:

To prove Jensen’s inequality by interpolation, we shall begin with a special case. As often in probability, the simplest case is that of a Gaussian random variable.

Jensen’s inequality for a Gaussian. Let be a standard Gaussian random variable (i.e., mean-zero and variance ) and let be a thrice-differentiable convex function satisfying a certain technical condition.¹Specifically, we assume the regularity condition for some for any Gaussian random variable . Then

   

Note that the conclusion is exactly Jensen’s inequality, as we have assumed is mean-zero.

The difficulty with any proof by interpolation is to come up with the “right” . For us, the “right” answer will take the form

where starts with no randomness and is our standard Gaussian. To interpolate between these extremes, we increase the variance linearly from to . Thus, we define

Here, and throughout, denotes a Gaussian random variable with zero mean and variance .

Let’s compute the derivative of . To do this, let denote a small parameter which we will later send to zero. For us, the key fact will be that a can be realized as a sum of independent and random variables. Therefore, we write

We now evaluate by using Taylor’s formula

(1)  

where lies between and . Now, take expectations,

The random variable has mean zero and variance so this gives

As we show below, the remainder term vanishes as . Thus, we can rearrange this expression to compute the derivative:

The second derivative of a convex function is nonnegative: for every . Therefore,

Jensen’s inequality is proven! In fact, we’ve proven the stronger version of Jensen’s inequality:

This strengthened version can yield improvements. For instance, if is -smooth

then we have

This inequality isn’t too hard to prove directly, but it does show that we’ve obtained something more than the simple proof of Jensen’s inequality.

Analyzing the Remainder Term

Let us quickly check that the remainder term vanishes as . Let’s do this. As an exercise, you can verify that our technical regularity condition implies . Thus, by Hölder’s inequality and setting to be ‘s Hölder conjugate (), we obtain

   

One can show that where is a function of alone. Therefore, as .

What’s Really Going On Here?

In our proof, we use a family of random variables , defined for each . Rather than treating these quantities as independent, we can think of them as a collective, comprising a random function known as a Brownian motion.

The Brownian motion is a very natural way of interpolating between a constant and a Gaussian with mean .²The Ornstein–Uhlenbeck process is another natural way of interpolating between a random variable and a Gaussian.

There is an entire subject known as stochastic calculus which allows us to perform computations with Brownian motion and other random processes. The rules of stochastic calculus can seem bizarre at first. For a function of a real number , we often write

For a function of a Brownian motion, the analog is Itô’s formula

While this might seem odd at first, this formula may seem more sensible if we compare with (1) above. The idea, very roughly, is that for an increment of the Brownian motion over a time interval , is a random variable with mean , so we cannot drop the second term in the Taylor series, even up to first order in . Fully diving into the subtleties of stochastic calculus is far beyond the scope of this short post. Hopefully, the rest of this post, which outlines some extensions of our proof of Jensen’s inequality that require more stochastic calculus, will serve as an enticement to learn more about this beautiful subject.

Proving Jensen by Interpolation

For the rest of this post, we will be less careful with mathematical technicalities. We can use the same idea that we used to prove Jensen’s inequality for a Gaussian random variable to prove Jensen’s inequality for any random variable :

Here is the idea of the proof.

First, realize that we can write any random variable as a function of a standard Gaussian random variable . Indeed, letting and denote the cumulative distribution functions of and , one can show that

has the same distribution as .

Now, as before, we can interpolate between and using a Brownian motion. As a first, idea, we might try

Unfortunately, this choice of does not work! Indeed, does not even equal to ! Instead, we must define

We define using the conditional expectation of the final value conditional on the Brownian motion at an earlier time . Using a bit of elbow grease and stochastic calculus, one can show that

This provides a proof of Jensen’s inequality in general by the method if interpolation.

Gaussian Jensen Inequality

Now, we’ve come to the real treat, the Gaussian Jensen inequality. In the last section, we saw the sketch of a proof of Jensen’s inequality using interpolation. While it is cool that this proof is possible, we learned anything new since we can prove Jensen’s inequality in other ways. The Gaussian Jensen inequality provides an application of this technique which is hard to prove other ways. This section, in particular, is cribbing quite heavily from Paata Ivanishvili‘s excellent post on the topic.

Here’s the big question:

If are “somewhat dependent”, for which functions does the multivariate Jensen’s inequality

()  

hold?

Considering extreme cases, if are entirely dependent, then we would only expect () to hold when is convex. But if are independent, then we can apply Jensen’s inequality to each coordinate one at a time to deduce

We would like a result which interpolates between extremes {fully dependent, fully convex} and {independent, separately convex}. The Gaussian Jensen inequality provides exactly this tool.

As in the previous section, we can generate arbitrary random variables as functions of Gaussian random variables . We will use the covariance matrix of the Gaussian random variables as our measure of the dependence of the random variables . With this preparation in place, we have the following result:

Gaussian Jensen inequality. The conclusion of Jensen’s inequality

(2)  

holds for all test functions if and only if

   

Here, is the Hessian matrix at and denotes the entrywise product of matrices.

This is a beautiful result with striking consequences (see Ivanishvili‘s post). The proof is essentially the same as the proof as Jensen’s inequality by interpolation with a little additional bookkeeping.

Let us confirm this result respects our extreme cases. In the case where are equal (and variance one), is a matrix of all ones and for all . Thus, the Gaussian Jensen inequality states that (2) holds if and only if is positive semidefinite for every , which occurs precisely when is convex.

Next, suppose that are independent and variance one, then is the identity matrix and

A diagonal matrix is positive semidefinite if and only if its entries are nonnegative. Thus, (2) holds if and only if each of ‘s diagonal second derivatives are nonnegative : this is precisely the condition for to be separately convex in each argument.

There’s much more to be said about the Gaussian Jensen inequality, and I encourage you to read Ivanishvili‘s post to see the proof and applications. What I find so compelling about this result—so compelling that I felt the need to write this post—is how interpolation and stochastic calculus can be used to prove inequalities which don’t feel like stochastic calculus problems. The Gaussian Jensen inequality is a statement about functions of dependent Gaussian random variables; there’s nothing dynamic happening. Yet, to prove this result, we inject dynamics into the problem, viewing the two sides of our inequality as endpoints of a random process connecting them. This is a such a beautiful idea that I couldn’t help but share it.

This post is co-written by my brother, Aidan Epperly, for the second Summer of Math Exposition (SoME2).

Let’s start with a classical problem: connect-the-dots. As we know from geometry, any two points in the plane are connected by one and only one straight line:

But what if we have more than two points? How should we connect them? One natural way is by parabola. Any three points (with distinct coordinates) are connected by one and only one parabola :

And we can keep extending this. Any points¹The degree of the polynomial is one less than the number of points because a degree- polynomial is described by coefficients. For instance, a degree-two parabola has three coefficients , , and . (with distinct coordinates) are connected by a unique degree- polynomial :

This game of connect-the-dots with polynomials is known more formally as polynomial interpolation. We can use polynomial interpolation to approximate functions. For instance, we can approximate the function on the interval to visually near-perfect accuracy by connecting the dots between seven points :

But something very peculiar happens when we try and apply this trick to the specially chosen function on the interval :

Unlike , the polynomial interpolant for is terrible! What’s going on? Why doesn’t polynomial interpolation work here? Can we fix it? The answer to the last question is yes and the solution is Chebyshev polynomials.

Reverse-Engineering Chebyshev

The failure of polynomial interpolation for is known as Runge’s phenomenon after Carl Runge who discovered this curious behavior in 1901. The function is called the Runge function. Our goal is to find a fix for polynomial interpolation which crushes the Runge phenomenon, allowing us to reliably approximate every sensible²A famous theorem of Faber states that there does not exist any set of points through which the polynomial interpolants converge for every continuous function. This is not as much of a problem as it may seem. As the famous Weierstrass function shows, arbitrary continuous functions can be very weird. If we restrict ourselves to nicer functions, such as Lipschitz continuous functions, there does exist a set of points through which the polynomial interpolant always converges to the underlying function. Thus, in this senses, it is possible to crush the Runge phenomenon. function with polynomial interpolation.

Carl Runge

Let’s put on our thinking caps and see if we can discover the fix for ourselves. In order to discover a fix, we must first identify the problem. Observe that the polynomial interpolant is fine near the center of the interval; it only fails near the boundary.

This leads us to a guess for what the problem might be; maybe we need more interpolation points near the boundaries of the interval. Indeed, tipping our hand a little bit, this turns out to be the case. For instance, connecting the dots for the following set of “mystery points” clustered at the endpoints works just fine:

Let’s experiment a little and see if we can discover a nice set of interpolation points, which we will call , like this for ourselves. We’ll assume the interpolation points are given by a function so we can form the polynomial interpolant for any desired polynomial degree .³Technically, we should insist on the function being \textit{injective} so that the points are guaranteed to be distinct. For instance, if we pick , the points look like this:

Equally spaced points (shown on vertical axis) give rise to
non-equally spaced points (shown on horizontal axis)

How should we pick the function ? First observe that, even for the Runge function, equally spaced interpolation points are fine near the center of the interval. We thus have at least two conditions for our desired interpolation points:

1. The interior points should maintain their spacing of roughly .
2. The points must cluster near both boundaries.

As a first attempt let’s divide the interval into thirds and halve the spacing of points except in the middle third. This leads to the function

These interpolation points initially seem promising, even successfully approximating the Runge function itself.

Unfortunately, this set of points fails when we consider other functions. For instance, if we use the Runge-like function , we see that these interpolation points now lead to a failure to approximate the function at the middle of the interval, even if we use a lot of interpolation points!

Maybe the reason this set of interpolation points didn’t work is that the points are too close at the endpoints. Or maybe we should have divided the interval as quarter–half–quarter rather than thirds. There are lots of variations of this strategy for choosing points to explore and all of them eventually lead to failure on some Runge-flavored example. We need a fundamentally different strategy then making the points times closer within distance of the endpoints.

Let’s try a different approach. The closeness of the points at the endpoints is determined by the slope of the function at and . The smaller that and are, the more clustered the points will be. For instance,

When we halved the distance between points, we instead had

So if we want the points to be much more clustered together, it is natural to require

It also makes sense for the function to cluster points equally near both endpoints, since we see no reason to preference one end over the other. Collecting together all the properties we want the function to have, we get the following list:

1. spans the whole range ,
2. , and
3. is symmetric about , .

Mentally scrolling through our Rolodex of friendly functions, a natural one that might come to mind meeting these three criteria is the cosine function, specifically . This function yields points which are more clustered at the endpoints:

The points

we guessed our way into are known as the Chebyshev points.⁴Some authors refer to these as the “Chebyshev points of the second kind” or use other names. We follow the convention in Approximation Theory and Approximation Practice (Chapter 1) and simply refer to these points simply as the Chebyshev points. The Chebyshev points prove themselves perfectly fine for the Runge function:

As we saw earlier, success on the Runge function alone is not enough to declare victory for the polynomial interpolation problem. However, in this case, there are no other bad examples left to find. For any nice function with no jumps, polynomial interpolation through the Chebyshev points works excellently.⁵Specifically, for a function which not too rough (i.e., Lipschitz continuous), the degree- polynomial interpolant of through the Chevyshev points converges uniformly to as .

Why the Chebyshev Points?

We’ve guessed our way into a solution to the polynomial interpolation problem, but we still really don’t know what’s going on here. Why are the Chebyshev points much better at polynomial interpolation than equally spaced ones?

Now that we know that the Chebyshev points are a right answer to the interpolation problem,⁶Indeed, there are other sets of interpolation points through which polynomial interpolation also works well, such as the Legendre points. let’s try and reverse engineer a principled reason for why we would expect them to be effective for this problem. To do this, we ask:

What is special about the cosine function?

From high school trigonometry, we know that gives the coordinate of a point radians along the unit circle. This means that the Chebyshev points are the coordinates of equally spaced points on the unit circle (specifically the top half of the unit circle ).

Chebyshev points are the coordinates of equally spaced points on the unit circle.

This raises the question:

What does the interpolating polynomial look like as a function of the angle ?

To convert between and we simply plug in to :

This new function depending on , which we can call , is a polynomial in the variable . Powers of cosines are not something we encounter every day, so it makes sense to try and simplify things using some trig identities. Here are the first couple powers of cosines:

A pattern has appeared! The powers always take the form⁷As a fun exercise, you might want to try and prove this using mathematical induction.

The significance of this finding is that, by plugging in each of these formulas for , we see that our polynomial in the variable has morphed into a Fourier cosine series in the variable :

For anyone unfamiliar with Fourier series, we highly encourage the 3Blue1Brown video on the subject, which explains why Fourier series are both mathematically beautiful and practically useful. The basic idea is that almost any function can be expressed as a combination of waves (that is, sines and cosines) with different frequencies.⁸More precisely, we might call these angular frequencies. In our case, this formula tells us that is equal to units of frequency , plus units of frequency , all the way up to units of frequency . Different types of Fourier series are appropriate in different contexts. Since our Fourier series only possesses cosines, we call it a Fourier cosine series.

We’ve discovered something incredibly cool:

Polynomial interpolation through the Chebyshev points is equivalent to finding a Fourier cosine series for equally spaced angles .

We’ve arrived at an answer to why the Chebyshev points work well for polynomial interpolation.

Polynomial interpolation through the Chebyshev points is effective because Fourier cosine series through equally spaced angles is effective.

Of course, this explanation just raises the further question: Why do Fourier cosine series give effective interpolants through equally spaced angles ? This question has a natural answer as well, involving the convergence theory and aliasing formula (see Section 3 of this paper) for Fourier series. We’ll leave the details to the interested reader for investigation. The success of Fourier cosines series in interpolating equally spaced data is a fundamental observation that underlies the field of digital signal processing. Interpolation through the Chebyshev points effectively hijacks this useful fact and applies it to the seemingly unrelated problem of polynomial interpolation.

Another question this explanation raises is the precise meaning of “effective”. Just how good are polynomial interpolants through the Chebyshev points at approximating functions? As is discussed at length in another post on this blog, the degree to which a function can be effectively approximated is tied to how smooth or rough it is. Chebyshev interpolants approximate nice analytic functions like or with exponentially small errors in the number of interpolation points used. By contrast, functions with kinks like are approximated with errors which decay much more slowly. See theorems 2 and 3 on this webpage for more details.

Chebyshev Polynomials

We’ve now discovered a set of points, the Chebyshev points, through which polynomial interpolation works well. But how should we actually compute the interpolating polynomial

Again, it will be helpful to draw on the connection to Fourier series. Computations with Fourier series are highly accurate and can be made lightning fast using the fast Fourier transform algorithm. By comparison, directly computing with a polynomial through its coefficients is a computational nightmare.

In the variable , the interpolant takes the form

To convert back to , we use the inverse function⁹One always has to be careful when going from to since multiple values get mapped to a single value by the cosine function. Fortunately, we’re working with variables and , between which the cosine function is one-to-one with the inverse function being given by the arccosine. to obtain:

This is a striking formula. Given all of the trigonometric functions, it’s not even obvious that is a polynomial (it is)!

Despite its seeming peculiarity, this is a very powerful way of representing the polynomial . Rather than expressing using monomials , we’ve instead written as a combination of more exotic polynomials

The polynomials are known as the Chebyshev polynomials,¹⁰More precisely, the polynomials are known as the Chebyshev polynomials of the first kind. named after Pafnuty Chebyshev who studied the polynomials intensely.¹¹The letter “T” is used for Chebyshev polynomials since the Russian name “Chebyshev” is often alternately transliterated to English as “Tchebychev”.

Pafnuty Chebyshev

Writing out the first few Chebyshev polynomials shows they are indeed polynomials:

The first four Chebyshev polynomials

To confirm that this pattern does continue, we can use trig identities to derive¹²Specifically, the recurrence is a consequence of applying the sum-to-product identity to for . the following recurrence relation for the Chebyshev polynomials:

Since and are both polynomials, every Chebyshev polynomial is as well.

We’ve arrived at the following amazing conclusion:

Under the change of variables , the Fourier cosine series

   

becomes the combination of Chebyshev polynomials

   

This simple and powerful observations allows us to apply the incredible speed and accuracy of Fourier series to polynomial interpolation.

Beyond being a neat idea with some nice mathematics, this connection between Fourier series and Chebyshev polynomials is a powerful tool for solving computational problems. Once we’ve accurately approximated a function by a polynomial interpolant, many quantities of interest (derivatives, integrals, zeros) become easy to compute—after all, we just have to compute them for a polynomial! We can also use Chebyshev polynomials to solve differential equations with much faster rates of convergence than other methods. Because of the connection to Fourier series, all of these computations can be done to high accuracy and blazingly fast via the fast Fourier transform, as is done in the software package Chebfun.

The Chebyshev polynomials have an array of amazing properties and they appear all over mathematics and its applications in other fields. Indeed, we have only scratched the surface of the surface. Many questions remain:

• What is the connection between the Chebyshev points and the Chebyshev polynomials?
• The cosine functions are orthogonal to each other; are the Chebyshev polynomials?
• Are the Chebyshev points the best points for polynomial interpolation? What does “best” even mean in this context?
• Every “nice” even periodic function has an infinite Fourier cosine series which converges to it. Is there a Chebyshev analog? Is there a relation between the infinite Chebyshev series and the (finite) interpolating polynomial through the Chebyshev points?

All of these questions have beautiful and fairly simple answers. The book Approximation Theory and Approximation Practice is a wonderfully written book that answers all of these questions in its first six chapters, which are freely available on the author’s website. We recommend the book highly to the curious reader.

TL;DR: To get an accurate polynomial approximation, interpolate through the Chebyshev points.
To compute the resulting polynomial, change variables to , compute the Fourier cosine series interpolant, and obtain your polynomial interpolant as a combination of Chebyshev polynomials.

The (ordinary) linear least squares problem is as follows: given an matrix and a vector of length , find the vector such that is as close to as possible, when measured using the two-norm . That is, we seek to

(1)  

From this equation, the name “least squares” is self-explanatory: we seek which minimizes the sum of the squared discrepancies between the entries of and .

The least squares problem is ubiquitous in science, engineering, mathematics, and statistics. If we think of each row of as an input and its corresponding entry of as an output, then the solution to the least squares model gives the coefficients of a linear model for the input–output relationship. Given a new previously unseen input , our model predicts the output is approximately . The vector consists of coefficients for this linear model. The least squares solution satisfies the property that the average squared difference between the output and the prediction is as small as it could possibly be for all choices of coefficient vectors .

How do we solve the least squares problem? A classical solution approach, ubiquitous in textbooks, is to solve a system of linear equations known as the normal equations. The normal equations associated with the least squares problem (1) are given by

(2)  

This system of equations always has a solution. If has full column-rank, then is invertible and the unique least squares solution to (1) is given by . We assume that has full column-rankQ for the rest of this discussion. To solve the normal equations in software, we compute and and solve (2) using a linear solver like MATLAB’s “\”.¹Even better, we could us a Cholesky decomposition since the matrix is positive definite. (As is generally true in matrix computations, it is almost never a good idea to explicitly form the inverse of the matrix , or indeed any matrix.) We also can solve the normal equations using an iterative method like (preconditioned) conjugate gradient.

The purpose of the article is to advocate against the use of the normal equations for solving the least squares problems, at least in most cases. So what’s wrong with the normal equations? The problem is not that the normal equations aren’t mathematically correct. Instead, the problem is that the normal equations often lead to poor accuracy for the least squares solution using computer arithmetic.

Most of the time when using computers, we store real numbers as floating point numbers.²One can represent rational numbers on a computer as fractions of integers and operations can be done exactly. However, this is prone to gross inefficiencies as the number of digits in the rational numbers can grow to be very large, making the storage and time to solve linear algebra problems with rationals dramatically more expensive. For these reasons, the vast majority of numerical computations use floating point numbers which store only a finite number of digits for any given real number. In this model, except for extremely rare circumstances, rounding errors during arithmetic operations are a fact of life. At a coarse level, the right model to have in your head is that real numbers on a computer are stored in scientific notation with only 16 decimal digits after the decimal point.³This is a simplification in multiple ways. First, computers store numbers in binary and thus, rather than storing 16 decimal digits after the decimal point, they store 52 binary digits. This amounts to roughly 16 decimal digits. Secondly, there are different formats for storing real numbers as floating point on a computer with different amounts of stored digits. The widely used IEEE double precision format has about 16 decimal digits of accuracy; the IEEE single precision format has roughly 8. When two numbers are added, subtracted, multiplied, and divided, the answer is computed and then rounded to 16 decimal digits; any extra digits of information are thrown away. Thus, the result of our arithmetic on a computer is the true answer to the arithmetic problem plus a small rounding error. These rounding errors are small individually, but solving an even modestly sized linear algebra problem requires thousands of such operations. Making sure many small errors don’t pile up into a big error is part of the subtle art of numerical computation.

To make a gross simplification, if one solves a system of linear equations on a computer using a well-designed piece of software, one obtains an approximate solution which is, after accounting for the accumulation of rounding errors, close to . But just how close the computed solution and the true solution are depends on how “nice” the matrix is. The “niceness” of a matrix is quantified by a quantity known as the condition number of , which we denote .⁴In fact, there are multiple definitions of the condition number depending on the norm which one uses the measure the sizes of vectors. Since we use the 2-norm, the appropriate 2-norm condition number is the ratio of the largest and smallest singular values of . As a rough rule of thumb, the relative error between and is roughly bounded as

(3)  

The “ corresponds to the fact we have roughly 16 decimal digits of accuracy in double precision floating point arithmetic. Thus, if the condition number of is roughly , then we should expect around digits of accuracy in our computed solution.

The accuracy of the least squares problem is governed by its own condition number . We would hope that we can solve the least squares problem with an accuracy like the rule-of-thumb error bound (3) we had for linear systems of equations, namely a bound like . But this is not the kind of accuracy we get for the least squares problem when we solve it using the normal equations. Instead, we get accuracy like

(4)  

By solving the normal equations we effectively square the condition number! Perhaps this is not surprising as the normal equations also more-or-less square the matrix by computing . This squared condition number drastically effects the accuracy of the computed solution. If the condition number of is , then the normal equations give us absolute nonsense for ; we expect to get no digits of the answer correct. Contrast this to above, where we were able to get correct digits in the solution to despite the condition number of being times larger than !

All of this would be just a sad fact of life for the least squares problem if the normal equations and their poor accuracy properties were the best we could do for the least squares problem. But we can do better! One can solve linear least squares problems by computing a so-called QR factorization of the matrix .⁵In MATLAB, the least squares problem can be solved with QR factorization by calling “A\b”. Without going into details, the upshot is that the least squares solution by a well-designed⁶One way of computing the QR factorization is by Gram–Schmidt orthogonalization, but the accuracy properties of this are poor too. A gold-standard way of computing the QR factorization by means of Householder reflectors, which has excellent accuracy properties. QR factorization requires a similar amount of time to solving the normal equations and has dramatically improved accuracy properties, achieving the desirable rule-of-thumb behavior⁷More precisely, the rule of thumb is like . So even if we solve the least squares problem with QR factorization, we still get a squared condition number in our error bound, but this condition number squared is multiplied by the residual , which is small if the least squares fit is good. The least squares solution is usually only interesting when the residual is small, thus justifying dropping it in the rule of thumb.

(5)  

I have not described how the QR factorization is accurately computed nor how to use the QR factorization to solve least squares problems nor even what the QR factorization is. All of these topics are explained excellently by the standard textbooks in this area, as well as by publicly available resources like Wikipedia. There’s much more that can be said about the many benefits of solving the least squares problem with the QR factorization,⁸E.g., it can work for sparse matrices while the normal equations often do not, it has superior accuracy to Gaussian elimination with partial pivoting even for solving linear systems, the “” matrix in the QR factorization can be represented implicitly as a product of easy-to-compute-with Householder reflectors which is much more efficient when, etc. but in the interest of brevity let me just say this: TL;DR when presented in the wild with a least squares problem, the solution method one should default to is one based on a well-implemented QR factorization, not solving the normal equations.

Suppose for whatever reason we don’t have a high quality QR factorization algorithm at our disposal. Must we then resort to the normal equations? Even in this case, there is a way we can reduce the problem of solving a least squares problems to a linear system of equations without squaring the condition number! (For those interested, to do this, we recognize the normal equations as a Schur complement of a somewhat larger system of linear equations and then solve that. See Eq. (7) in this post for more discussion of this approach.) 

The title of this post Don’t Solve the Normal Equations is deliberately overstated. There are times when solving the normal equations is appropriate. If is well-conditioned with a small condition number, squaring the condition number might not be that bad. If the matrix is too large to store in memory, one might want to solve the least squares problem using the normal equations and the conjugate gradient method.

However, the dramatically reduced accuracy of solving the normal equations should disqualify the approach from being the de-facto way of solving least squares problems. Unless you have good reason to think otherwise, when you see , solve a different way.

In this post, I want to discuss two of my favorite topics in applied mathematics: the Sherman–Morrison formula and integral equations. As a bridge between these ideas, we’ll use an integral equation analog of the Sherman–Morrison formula to derive the solution for the Laplace equation with Dirichlet boundary conditions in the 2D disk.

Laplace’s Equation

Suppose we have a thin, flat (two-dimensional) plate of homogeneous material and we measure the temperature at the border. What is the temperature inside the material? The solution to this problem is described by Laplace’s equation, one of the most ubiquitous partial differential equations in physics. Let denote the temperature of the material at point . Laplace’s equation states that, at any point on the interior of the material,

(1)  

Laplace’s equation (1) and the specification of the temperature on the boundary form a well-posed mathematical problem in the sense that the temperature is uniquely determined at each point .¹A well-posed problem is also required to depend continuously on the input data which, in this case, are the boundary temperatures. Indeed, the Laplace problem with boundary data is well-posed in this sense. We call this problem the Laplace Dirichlet problem since the boundary conditions

are known as Dirichlet boundary conditions.

The Double Layer Potential

Another area of physics where the Laplace equation (1) appears is the study of electrostatics. In this case, represents the electric potential at the point . The Laplace Dirichlet problem is to find the electric potential in the interior of the region with knowledge of the potential on the boundary.

The electrostatic application motivates a different way of thinking about the Laplace equation. Consider the following question:

How would I place electric charges on the boundary to produce the electric potential at each point on the boundary?

This is a deliciously clever question. If I were able to find an arrangement of charges answering the question, then I could calculate the potential at each point in the interior by adding up the contribution to the electric potential of each element of charge on the boundary. Thus, I can reduce the problem of finding the electric potential at each point in the 2D region to finding a charge distribution on the 1D boundary to that region.

We shall actually use a slight variant of this charge distribution idea which differs in two ways:

• Rather than placing simple charges on the boundary of the region, we place charge dipoles.²The reason for why this modification works better is an interesting question, but answering it properly would take us too far afield from the goals of this article.
• Since we are considering a two-dimensional problem, we use a different formula for the electric potential than given by Coulomb’s law for charges in 3D. Also, since we are interested in solving the Laplace Dirichlet problem in general, we can choose a convenient dimensionless system of units. We say that the potential at a point induced by a unit “charge” at the origin is given by .

With these modifications, our new question is as follows:

How would I place a density of “charge” dipoles on the boundary to produce the electric potential at each point on the boundary?

We call this function the double layer potential for the Laplace Dirichlet problem. One can show the double layer potential satisfies a certain integral equation. To write down this integral equation, let’s introduce some more notation. Let be the region of interest and its boundary. Denote points concisely as vectors , with the length of denoted . The double layer potential satisfies

(2)  

where the integral is taken over the surface of the region ; denotes the directional derivative taken in the direction normal (perpendicular) to the surface at the point . Note we choose a unit system for which hides physical constants particular to the electrostatic context, since we are interested in applying this methodology to the Laplace Dirichlet problem in general (possibly non-electrostatic) applications.

There’s one last ingredient: How do we compute the electric potential at points in the interior of the region? This is answered by the following formula:

(3)  

The integral equation (2) is certainly nothing to sneeze at. Rather than trying to comprehend it in its full glory, we shall focus on a special case for the rest of our discussion. Suppose the region is a circular disk with radius centered at . The the partial derivative in the integrand in (2) then is readily computed for points and both on the boundary of the circle:

Substituting in (2) then gives

(4)  

The Sherman–Morrison Formula

We are interested in solving the integral equation (4) to obtain an expression for the double-layer potential , as this will give us a solution formula for the Laplace Dirichlet problem. Ultimately, we accomplish this by using a clever trick. In an effort to make this trick seem more self-evident and less of a “rabbit out of a hat”, I want to draw an analogy to a seemingly unrelated problem: rank-one updates to linear systems of equations and the Sherman–Morrison formula.³In accordance with Stigler’s law of eponymy, the Sherman–Morrison formula was actually discovered by William J. Duncan five years before Sherman, Morrison, and Woodbury. For a more general perspective on the Sherman–Morrison formula and its generalization to the Sherman–Morrison–Woodbury formula, you may be interested in the following post of mine on Schur complements and block Gaussian elimination.

Suppose we want to solve the system of linear equations

(5)  

where is an square matrix and , , and are length- vectors. We are ultimately interested in finding from . To gain insight into this problem, it will be helpful to first carefully considered the problem in reverse: computing from . We could, of course, perform this computation by forming the matrix in memory and multiplying it with , but there is a more economical way:

1. Form .
2. Compute .

Standing back, observe that we now have a system of equations for unknowns and . Specifically, our first equation can be rewritten as

which combined with the second equation

gives the by system⁴This “state space approach” of systematically writing out a matrix–vector multiply algorithm and then realizing this yields a larger system of linear equations was initially taught to be by my mentor Shiv Chandrasekaran; this approach has much more powerful uses, such as in the theory of rank-structured matrices.

(6)  

The original equation for (5) can be derived from the “lifted” equation (6) by applying Gaussian elimination and eliminating the first row of the linear system (6). But now that we have the lifted equation (6), one can naturally wonder what would happen if we instead used Gaussian elimination to eliminate the last rows of (6); this will give us an equation for which we can solved without first computing . Doing this so-called block Gaussian elimination yields

Solving this, we deduce that

From the equation , we have that

Since this formula holds for every vector , we deduce the famous Sherman–Morrison formula

This example shows how it can be conceptually useful to lift a linear system of equations by adding additional variables and equations and then “do Gaussian elimination in a different order”. The same insight shall be useful in solving integral equations like (4).

Solving for the Double Layer Potential

Let’s try repeating the playbook we executed for the rank-one-updated linear system (5) and apply it to the integral equation (4). We are ultimately interested in computing from but, as we did last section, let’s first consider the reverse. To compute from , we first evaluate the integral

Substituting this into (4) gives the system of equations

(7)  

(8)  

In order to obtain (4) from (7) and (8), we add times equation (8) to equation (7). Following last section, we now instead eliminate from equation (8) using equation (7). To do this, we need to integrate equation (7) in order to cancel the integral in equation (8):

Adding times this integrated equation to equation (8) yields

Thus plugging this expression for into equation (7) yields

We’ve solved for our double layer potential!

As promised, the double layer potential can be used to give a solution formula (known as the Poisson integral formula) for the Laplace Dirichet problem. The details are a mechanical, but also somewhat technical, exercise in vector calculus identities. We plug through the details in the following extra section.

Poisson Integral Formula

Let’s finish this up by using the double layer to derive a solution formula for the electric potential at a point in the interior of the region. To do this, we use equation (3):

(9)  

We now need to do a quick calculation which is somewhat technical and not particularly enlightening. We evaluate using the divergence theorem:

   

We denote for the gradient, for the normal vector to at the point , for the dot product, for the Laplace operator, and for the Dirac delta “function”. The last equality holds because the function is a so-called fundamental solution for Laplace equation in the sense that . Therefore, (9) simplifies to

   

Computing the boundary derivative for the spherical region centered at the origin with radius , we obtain the formula

   

We’ve succeeded at deriving a solution formula for for points in the interior of the disk in terms of for points on the boundary of the disk. This is known as the Poisson integral formula for the disk in two dimensions. This formula can be generalized to balls in higher dimensions, though this proof technique using “Sherman–Morrison” fails to work in more than two dimensions.

Sherman–Morrison for Integral Equations

Having achieved our main goal of deriving a solution formula for the 2D Laplace Dirichlet problem for a circular domain, I want to take a step back to present the approach from two sections ago in more generality. Consider a more general integral equation of the form

(10)  

where is some region in space, , , and are functions of one or two arguments on , and is a nonzero constant. Such an integral equation is said to be of the second kind. The integral equation for the Laplace Dirichlet problem (2) is of this form with , , , and . We say the kernel is separable with rank if can be expressed in the form

With the circular domain, the Laplace Dirichlet integral equation (2) is separable with rank .⁵E.g., set and . We shall focus on the second kind integral equation (10) assuming the kernel is separable with rank (for simplicity, we set ):

(11)  

Let’s try and write this equation in a way that’s more similar to the linear system of equation (5). To do this, we make use of linear operators defined on functions:

• Let denote the identity operator on functions: It takes as inputs function and outputs the function unchanged.
• Let denote the “integration against operator”: It takes as input a function and outputs the number .

With these notations, equation (11) can be written as

Using the same derivation which led to the Sherman–Morrison formula for linear systems of equations, we can apply the Sherman–Morrison formula to this integral equation in operator form, yielding

Therefore, the solution to the integral equation (11) is

This can be interpreted as a kind of Sherman–Morrison formula for the integral equation (11).

One can also generalize to provide a solution formula for the second-kind integral equation (10) for a separable kernel with rank ; in this case, the natural matrix analog is now the Sherman–Morrison–Woodbury identity rather than the Sherman–Morrison formula. Note that this solution formula requires the solution of a system of linear equations. One can use this as a numerical method to solve second-kind integral equations: First, we approximate by a separable kernel of a modest rank and then compute the exact solution of the resulting integral equation with the approximate kernel.⁶A natural question is why one might want to solve an integral equation formulation of a partial differential equations like the Laplace or Helmholtz equation. An answer is that that formulations based on second-kind integral equations tend to lead to systems of linear equations which much more well-conditioned as compared to other methods like the finite element method. They have a number of computational difficulties as well, as the resulting linear systems of equations are dense and may require elaborate quadrature rules to accurately compute.

My goal in writing this post was to discuss two topics which are both near and dear to my heart, integral equations and the Sherman–Morrison formula. I find the interplay of these two ideas to be highly suggestive. It illustrates the power of the analogy between infinite-dimensional linear equations, like differential and integral equations, and finite-dimensional ones, which are described by matrices. Infinite dimensions certainly do have their peculiarities and technical challenges, but it can be very useful to first pretend infinite-dimensional linear operators (like integral operators) are matrices, do calculations to derive some result, and then justify these computations rigorously post hoc.⁷The utility of this technique is somewhat of an open secret among some subset of mathematicians and scientists, but such heuristics are usually not communicated to students explicitly, at least in rigorous mathematics classes.

In this post, I want to discuss a beautiful and simple geometric picture of the perturbation theory of definite generalized eigenvalue problems. As a culmination, we’ll see a taste of the beautiful perturbation theory of Mathias and Li, which appears to be not widely known in virtue of only being explained in a technical report. Perturbation theory for the generalized eigenvalue problem is a bit of a niche subject, but I hope you will stick around for some elegant arguments. In addition to explaining the mathematics, I hope this post serves as an allegory for the importance of having the right way of thinking about a problem; often, the solution to a seemingly unsolvable problem becomes almost self-evident when one has the right perspective.

What is a Generalized Eigenvalue Problem?

This post is about the definite generalized eigenvalue problem, so it’s probably worth spending a few words talking about what generalized eigenvalue problems are and why you would want to solve them. Slightly simplifying some technicalities, a generalized eigenvalue problem consists of finding nonzero vectors and a (possibly complex) numbers such that .¹In an unfortunate choice of naming, there is actually a completely different sense in which it makes sense to talk about generalized eigenvectors, in the context of the Jordan normal form for standard eigenvalue problems. The vector is called an eigenvector and its eigenvalue. For our purposes, and will be real symmetric (or even complex Hermitian) matrices; one can also consider generalized eigenvalue problemss for nonsymmetric and even non-square matrices and , but the symmetric case covers many applications of practical interest. The generalized eigenvalue problem is so-named because it generalizes the standard eigenvalue problem , which is a special case of the generalized eigenvalue problem with .²One can also further generalize the generalized eigenvalue problem to polynomial and nonlinear eigenvalue problems.

Why might we want so solve a generalized eigenvalue problem? The applications are numerous (e.g., in chemistry, quantum computation, systems and control theory, etc.). My interest in perturbation theory for generalized eigenvalue problems arose in the analysis of a quantum algorithm for eigenvalue problems in chemistry, and the theory discussed in this article played a big role in that analysis. To give an application which is more easy to communicate than the quantum computation application which motivated my own interest, let’s discuss an application in classical mechanics.

The Lagrangian formalism is a way of reformulating Newton’s laws of motion in a general coordinate system.³The benefits of the Lagrangian framework are far deeper than working in generalized coordinate systems, but this is beyond the scope of our discussion and mostly beyond the scope of what I am knowledgeable enough to write meaningfully about. If denotes a vector of generalized coordinates describing our system and denotes ‘s time derivative, then the time evolution of a system with Lagrangian functional are given by the Euler–Lagrange equations . If we choose to represent the deviation of our system from equilibrium,⁴That is, in our generalized coordinate system, is a (static) equilibrium configuration for which whenever and . then our Lagrangian is well-approximated by it’s second order Taylor series:

By the Euler–Lagrange equations, the equations of motion for small deviations from this equillibrium point are described by

A fundamental set of solutions of this system of differential equations is given by , where and are the generalized eigenvalues and eigenvectors of the pair .⁵That is, all solutions to can be written (uniquely) as linear combinations of solutions of the form . In particular, if all the generalized eigenvalues are positive, then the equillibrium is stable and the square roots of the eigenvalues represent the modes of vibration. In the most simple mechanical systems, such as masses in one dimension connected by springs with the natural coordinate system, the matrix is diagonal with diagonal entries equal to the different masses. In even slightly more complicated “freshman physics” problems, it is quite easy to find examples where, in the natural coordinate system, the matrix is nondiagonal.⁶Almost always, the matrix is positive definite. As this example shows, generalized eigenvalue problems aren’t crazy weird things since they emerge as natural descriptions of simple mechanical systems like coupled pendulums.

One reason generalized eigenvalue problems aren’t more well-known is that one can easily reduce a generalized eigenvalue problem into a standard one. If the matrix is invertible, then the generalized eigenvalues of are just the eigenvalues of the matrix . For several reasons, this is a less-than-appealing way of reducing a generalized eigenvalue problem to a standard eigenvalue problem. A better way, appropriate when and are both symmetric and is positive definite, is to reduce the generalized eigenvalue problem for to the symmetrically reduced matrix , which also possesses the same eigenvalues as . In particular, the matrix remains symmetric, which shows that has real eigenvalues by the spectral theorem. In the mechanical context, one can think of this reformulation as a change of coordinate system in which the “mass matrix” becomes the identity matrix .

There are several good reasons to not simply reduce a generalized eigenvalue problem to a standard one, and perturbation theory gives a particular good reason. In order for us to change coordinates to change the matrix into an identity matrix, we must first know the matrix. If I presented you with an elaborate mechanical system which you wanted to study, you would need to perform measurements to determine the and matrices. But all measurements are imperfect and the entries of and are inevitably corrupted by measurement errors. In the presence of these measurement errors, we must give up on computing the normal modes of vibration perfectly; we must content ourselves with computing the normal modes of vibration plus-or-minus some error term we hope we can be assured is small if our measurement errors are small. In this setting, reducing the problem to seems less appealing, as I have to understand how the measurement errors in and are amplified in computing the triple product . This also suggests that computing may be a poor algorithmic strategy in practice: if the matrix is ill-conditioned, there might be a great deal of error amplification in the computed product . One might hope that one might be able to devise algorithms with better numerical stability properties if we don’t reduce the matrix pair to a single matrix. This is not to say that reducing a generalized eigenvalue problem to a standard one isn’t a useful tool—it definitely is. However, it is not something one should do reflexively. Sometimes, a generalized eigenvalue problem is best left as is and analyzed in its native form.

The rest of this post will focus on the question if and are real symmetric matrices (satisfying a definiteness condition, to be elaborated upon below), how do the eigenvalues of the pair compare to those of , where and are small real symmetric perturbations? In fact, it shall be no additional toil to handle the complex Hermitian case as well while we’re at it, so we shall do so. (Recall that a Hermitian matrix satisfies , where is the conjugate transpose. Since the complex conjugate does not change a real number, a real Hermitian matrix is necesarily symmetric .) For the remainder of this post, let , , , and be Hermitian matrices of the same size. Let and denote the perturbations.

Symmetric Treatment

As I mentioned at the top of this post, our mission will really be to find the right way of thinking about perturbation theory for the generalized eigenvalue problem, after which the theory will follow much more directly than if we were to take a frontal assault on the problem. As we go, we shall collect nuggets of insight, each of which I hope will follow quite naturally from the last. When we find such an insight, we shall display it on its own line.

The first insight is that we can think of the pair and interchangeably. If is a nonzero eigenvalue of the pair , satisfying , then . That is, is an eigenvalue of the pair . Lack of symmetry is an ugly feature in a mathematical theory, so we seek to smooth it out. After thinking a little bit, notice that we can phrase the generalized eigenvalue condition symmetrically as with the associated eigenvalue being given by . This observation may seem trivial at first, but let us collect it for good measure.

Treat and symmetrically by writing the eigenvalue as with .

Before proceeding, let’s ask a question that, in our new framing, becomes quite natural: what happens when ? The case is problematic because it leads to a division by zero in the expression . However, if we have , this expression still makes sense: we’ve found a vector for which but . It makes sense to consider still an eigenvector of with eigenvalue ! Dividing by zero should justifiably make one squeemish, but it really is quite natural in the case to treat as a genuine eigenvector with eigenvalue .

Things get even worse if we find a vector for which . Then, any can reasonably considered an eigenvalue of since . In such a case, all complex numbers are simultaneously eigenvalues of , in which case we call singular.⁷More precisely, a pair is singular if the determinant is identically zero for all . For the generalized eigenvalue problem to make sense for a pair , it is natural to require that not be singular. In fact, we shall assume an even stronger “definiteness” condition which ensures that has only real (or infinite) eigenvalues. Let us return to this question of definiteness in a moment and for now assume that is not singular and possesses real eigenvalues.

With this small aside taken care of, let us return to the main thread. By modeling eigenvalues as pairs , we’ve traded one ugliness for another. While reformulating the eigenvalue as a pair treats and symmetrically, it also adds an additional indeterminacy, scale. For instance, if is an eigenvalue of , then so is . Thus, it’s better not to think of so much as a pair of numbers together with all of its possible scalings.⁸Projective space provides a natural framework for studying such vectors up to scale indeterminacy. For reasons that shall hopefully become more clear as we go forward, it will be helpful to only consider all the possible positive scalings of —e.g., all for . Geometrically, the set of all positive scalings of a point in two-dimensional space is precisely just a ray emanating from the origin.

Represent eigenvalue pairs as rays emanating from the origin to account for scale ambiguity.

Now comes a standard eigenvalue trick. It’s something you would never think to do originally, but once you see it once or twice you learn to try it as a matter of habit. The trick: multiply the eigenvalue-eigenvector relation by the (conjugate) transpose of :⁹For another example of the trick, try applying it to the standard eigenvalue problem . Multiplying by and rearranging gives —the eigenvalue is equal to the expression , which is so important it is given a name: the Rayleigh quotient. In fact, the largest and smallest eigenvalues of can be found by maximizing and minimizing the Rayleigh quotient.

The above equation is highly suggestive: since and are only determined up to a scaling factor, it shows we can take and . And by different scalings of the eigenvector , we can scale and by any positive factor we want. (This retroactively shows why it makes sense to only consider positive scalings of and .¹⁰To make this point more carefully, we shall make great use of the identification between pairs and the pair of quadratic forms . Thus, even though and lead to equivalent eigenvalues since , and don’t necessarily both arise from a pair of quadratic forms: if , this does not mean there exists such that . Therefore, we only consider equivalent to if .) The expression is so important that we give it a name: the quadratic form (associated with and evaluated at ).

The eigenvalue pair can be taken equal to the pair of quadratic forms .

Complexifying Things

Now comes another standard mathematical trick: represent points in two-dimensional space by complex numbers. In particular, we identify the pair with the complex number .¹¹Recall that we are assuming that is real, so we can pick a scaling in which both and are real numbers. Assume we have done this. Similar to the previous trick, it’s not fully clear why this will pay off, but let’s note it as an insight.

Identify the pair with the complex number .

Now, we combine all the previous observations. The eigenvalue is best thought of as a pair which, up to scale, can be taken to be and . But then we represent as the complex number

Let’s stop for a moment and appreciate how far we’ve come. The generalized eigenvalue problem is associated with the expression .If we just went straight from one to the other, this reduction would appear like some crazy stroke of inspiration: why would I ever think to write down ? However, just following our nose lead by a desire to treat and symmetrically and applying a couple standard tricks, this expression appears naturally. The expression will be very useful to us because it is linear in and , and thus for the perturbed problem , we have that : consequently, is a small perturbation of . This observation will be very useful to us.

If is the eigenvector, then the complex number is .

Definiteness and the Crawford Number

With these insights in hand, we can now return to the point we left earlier about what it means for a generalized eigenvalue problem to be “definite”. We know that if there exists a vector for which , then the problem is singular. If we multiply by , we see that this means that as well and thus . It is thus quite natural to assume the following definiteness condition:

The pair is said to be definite if for all complex nonzero vectors .

A definite problem is guaranteed to be not singular, but the reverse is not necessarily true; one can easily find pairs which are not definite and also not singular.¹²For example, consider . is not definite since for . However, is not singular; the only eigenvalue of the pair is and is not identically zero.. (Note does not imply unless and are both positive (or negative) semidefinite.)

The “natural” symmetric condition for to be “definite” is for for all vectors .

Since the expression is just scaled by a positive factor by scaling the vector , it is sufficient to check the definiteness condition for only complex unit vectors . This leads naturally to a quantitative characterization of the degree of definiteness of a pair :

The Crawford number¹³The name Crawford number was coined by G. W. Stewart in 1979 in recognition of Crawford’s pioneering work on the perturbation theory of the definite generalized eigenvalue problem. of a pair is the minimum value of over all complex unit vectors .

The Crawford number naturally quantifies the degree of definiteness.¹⁴In fact, it has been shown that the Crawford number is, in a sense, the distance from a definite matrix pair to a pair which is not simultaneously diagonalizable by congruence. A problem which has a large Crawford number (relative to a perturbation) will remain definite after perturbation, whereas the pair may become indefinite if the size of the perturbation exceeds the Crawford number. Geometrically, the Crawford number has the following interpretation: must lie on or outside the circle of radius centered at for all (complex) unit vectors .

The “degree of definiteness” can be quantified by the Crawford number .

Now comes another step in our journey which is a bit more challenging. For a matrix (in our case ), the set of complex numbers for all unit vectors has been the subject of considerable study. In fact, this set has a name

The field of values of a matrix is the set .

In particular, the Crawford number is just the absolute value of the closest complex number in the field of values to zero.

It is a very cool and highly nontrivial fact (called the Toeplitz–Hausdorff Theorem) that the field of values is always a convex set, with every two points in the field of values containing the line segment connecting them. Thus, as a consequence, the field of values for a definite matrix pair is always on “one side” of the complex plane (in the sense that there exists a line through zero which lies strictly on one side of¹⁵This is a consequence of the hyperplane separation theorem together with the fact that .).

The numbers for unit vectors lie on one half of the complex plane.

From Eigenvalues to Eigenangles

All of this geometry is lovely, but we need some way of relating it to the eigenvalues. As we observed a while ago, each eigenvalue is best thought of as a ray emanating from the origin, owing to the fact that the pair can be scaled by an arbitrary positive factor. A ray is naturally associated with an angle, so it is natural to characterize an eigenvalue pair by the angle describing its arc.

But the angle of a ray is only defined up additions by full rotations ( radians). As such, to associate each ray a unique angle we need to pin down this indeterminacy in some fixed way. Moreover, this indeterminacy should play nice with the field of values and the field of values of the perturbation. But in the last section, we say that each of these field of angles lies (strictly) on one half of the complex plane. Thus, we can find a ray which does not intersect either field of values!

One possible choice is to measure the angle from this ray. We shall make a slightly different choice which plays better when we treat as a complex number . Recall that a number is an argument for if for some real number . The argument is multi-valued since is an argument for as long as is (for all integers ). However, once we exclude our ray , we can assign each complex number not on this ray a unique argument which depends continuously on . Denote this “branch” of the argument by . If represents an eigenvalue , we call an eigenangle.

Represent an eigenvalue pair by its associated eigenangle .

How are these eigenangles related to the eigenvalues? It’s now a trigonometry problem:

The eigenvalues are the cotangents of the eigenangles!

The eigenvalue is the cotangent of the eigenangle .

Variational Characterization

Now comes another difficulty spike in our line of reasoning, perhaps the largest in our whole deduction. To properly motivate things, let us first review some facts about the standard Hermitian/symmetric eigenvalue problem. The big idea is that eigenvalues can be thought of as the solution to a certain optimization problem. The largest eigenvalue of a Hermitian/symmetric matrix is given by the maximization problem

The largest eigenvalue is the maximum of the quadratic form over unit vectors . What about the other eigenvalues? The answer is not obvious, but the famous Courant–Fischer Theorem shows that the th largest eigenvalue can be written as the following minimax optimization problem

The minimum is taken over all subspaces of dimension whereas the maximum is taken over all unit vectors within the subspace . Symmetrically, one can also formulate the eigenvalues as a max-min optimization problem

These variational/minimax characterizations of the eigenvalues of a Hermitian/symmetric matrix are essential to perturbation theory for Hermitian/symmetric eigenvalue problems, so it is only natural to go looking for a variational characterization of the generalized eigenvalue problem. There is one natural way of doing this that works for positive definite: specifically, one can show that

This characterization, while useful in its own right, is tricky to deal with because it is nonlinear in and . It also treats and non-symmetrically, which should set off our alarm bells that there might be a better way. Indeed, the ingenious idea, due to G. W. Stewart in 1979, is to instead provide a variational characterization of the eigenangles! Specifically, Stewart was able to show¹⁶Stewart’s original definition of the eigenangles differs from ours; we adopt the definition of Mathias and Li. The result amounts to the same thing.

(1)  

for the eigenangles .¹⁷Note that since the cotangent is decreasing on , this means that the eigenvalues are now in increasing order, in contrast to our convention from earlier in this section. This shows, in particular, that the field of values is subtended by the smallest and largest eigenangles.

The eigenangles satisfy a minimax variational characterization.

How Big is the Perturbation?

We’re tantalizingly close to our objective. The final piece in our jigsaw puzzle before we’re able to start proving perturbation theorems is to quantify the size of the perturbing matrices and . Based on what we’ve done so far, we see that the eigenvalues are natural associated with the complex number , so it is natural to characterize the size of the perturbing pair by the distance between and . But the difference between these two quantities is just

We’re naturally led to the question: how big can be? If the vector has a large norm, then quite large, so let’s fix to be a unit vector. With this assumption in place, the maximum size of is simple the distance of the farthest point in the field of values from zero. This quantity has a name:

The numerical radius of a matrix (in our case ) is .¹⁸This maximum is taken over all complex unit vectors .

The size of the perturbation is the numerical radius .

It is easy to upper-bound the numerical radius by more familiar quantities. For instance, once can straightforwardly show the bound , where is the spectral norm. We prefer to state results using the numerical radius because of its elegance: it is, in some sense, the “correct” measure of the size of the pair in the context of this theory.

Stewart’s Perturbation Theory

Now, after many words of prelude, we finally get to our first perturbation theorem. With the work we’ve done in place, the result is practically effortless.

Let denote the eigenangles of the perturbed pair and consider the th eigenangle. Let be the subspace of dimension achieving the minimum in the first equation of the variational principle (1) for the original unperturbed pair . Then we have

(2)  

This is something of a standard trick when dealing with variational problems in matrix analysis: take the solution (in this case the minimizing subspace) for the original problem and plug it in for the perturbed problem. The solution may no longer be optimal, but it at least gives an upper (or lower) bound. The complex number must lie at least a distance from zero and . We’re truly toast if the perturbation is large enough to perturb to be equal to zero, so we should assume that .

For our perturbation theory to work, we must assume .

Making the assumption that , bounding the right-hand side of (2) requires finding the most-counterclockwise angle necessary to subtend a circle of radius centered at , which must lie a distance from the origin. The worst-case scenario is when is exactly a distance from the origin, as is shown in the following diagram.

Solving the geometry problem for the counterclockwise-most subtending angle in this worst-case sitation, we conclude the eigenangle bound . An entirely analogous argument using the max-min variational principle (1) proves an identical lower bound, thus showing

(3)  

In the language of eigenvalues, we have¹⁹I’m being a little sloppy here. For a result like this to truly hold, I believe all of the perturbed and unperturbed eigenangles should all be contained in one half of the complex plane.

Interpreting Stewart’s Theory

After much work, we have finally proven our first generalized eigenvalue perturbation theorem. After taking a moment to celebrate, let’s ask ourselves: what does this result tell us?

Let’s start with the good. This result shows us that if the perturbation, measured by the numerical radius , is much smaller than the definiteness of the original problem, measured by the Crawford number , then the eigenangles change by a small amount. What does this mean in terms of the eigenvalues? For small eigenvalues (say, less than one in magnitude), small changes in the eigenangles also lead to small changes of the eigenvalues. However, for large eigenangles, small changes in the eigenangle are magnified into potentially large changes in the eigenvalues. One can view this result in a positive or negative framing. On the one hand, large eigenvalues could be subject to dramatic changes by small perturbations; on the other hand, the small eigenvalues aren’t “taken down with the ship” and are much more well-behaved.

Stewart’s theory is beautiful. The variational characterization of the eigenangles (1) is a master stroke and exactly the extension one would want from the standard Hermitian/symmetric theory. From the variational characterization, the perturbation theorem follows almost effortlessly from a little trigonometry. However, Stewart’s theory has one important deficit: the Crawford number. All that Stewart’s theory tells is that all of the eigenangles change by at most roughly “perturbation size over Crawford number”. If the Crawford number is quite small since the problem is nearly indefinite, this becomes a tough pill to swallow.

The Crawford number is in some ways essential: if the perturbation size exceeds the Crawford number, the problem can become indefinite or even singular. Thus, we have no hope of fully removing the Crawford number from our analysis. But might it be the case that some eigenangles change by much less than “pertrubation size over Crawford number”? Could we possibly improve to a result of the form “the eigenangles change by roughly perturbation size over something (potentially) much less than the Crawford number”? Sun improved Stewart’s analysis in 1982, but the scourge of the Crawford number remained.²⁰Sun’s bound does not explicitly have the Crawford number, instead using the quantity and another hard-to-concisely describe quantity. In many cases, one has nothing better to do than to bound , in which case the Crawford number has appeared again. The theory of Mathias and Li, published in a technical report in 2004, finally produced a bound where the Crawford number is replaced.

The Mathias–Li Insight and Reduction to Diagonal Form

Let’s go back to the Stewart theory and look for a possible improvement. Recall in the Stewart theory that we considered the point on the complex plane. We then argued that, in the worst case, this point would lie a distance from the origin and then drew a circle around it with radius . To improve on Stewart’s bound, we must somehow do something better than using the fact that . The insight of the Mathias–Li theory is, in some sense, as simple as this: rather than using the fact that (as in Stewart’s analysis), use how far actually is from zero, where is chosen to be the unit norm eigenvectors of .²¹This insight is made more nontrivial by the fact that, in the context of generalized eigenvalue problems, it is often not convenient to choose the eigenvectors to have unit norm. As Mathias and Li note, there are often two more popular normalizations for . If is positive definite, one often normalizes such that —the eigenvectors are thus made “-orthonormal”, generalizing the fact that the eigenvectors of a Hermitian/symmetric matrix are orthonormal. Another popular normalization is to scale such that . In this way, just taking the eigenvector to have unit norm is already a nontrivial insight.

Before going further, let us quickly make a small reduction which will simplify our lives greatly. Letting denote a matrix whose columns are the unit-norm eigenvectors of , one can verify that and are diagonal matrices with entries and respectively. With this in mind, it can make our lives a lot easy to just do a change of variables and (which in turn sends and ). The change of variables is very common in linear algebra and is called a congruence transformation.

Perform a change of variables by a congruence transformation with the matrix of eigenvectors.

While this change of variables makes our lives a lot easier, we must first worry about how this change of variables might effect the size of the perturbation matrices . It turns out this change of variables is not totally benign, but it is not maximally harmful either. Specifically, the spectral radius can grow by as much as a factor of .²²This is because, in virtue of having unit-norm columns, the spectral norm of the matrix is . Further, note the following variational characterization of the spectral radius . Plugging these two facts together yields . This factor of isn’t great, but it is much better than if the bound were to degrade by a factor of the condition number , which can be arbitrarily large.

This change of variables may increase by at most a factor of .

From now on, we shall tacitly assume that this change of variables has taken place, with and being diagonal and and being such that is at most a factor larger than it was previously. We denote by and the th diagonal element of and , which are given by and where is the th unit-norm eigenvector

Mathias and Li’s Perturbation Theory

We first assume the perturbation is smaller than the Crawford number in the sense , which is required to be assured that the perturbed problem does not lose definiteness. This will be the only place in this analysis where we use the Crawford number.

Draw a circle of radius around .

If is the associated eigenangle, then this circle is subtended by arcs with angles

It would be nice if the perturbed eigenangles were guaranteed to lie in these arcs (i.e., ). Unfortunately this is not necessarily the case. If one is close to the origin, it will have a large arc which may intersect with other arcs; if this happens, we can’t guarantee that each perturbed eigenangle will remain within its individual arc. We can still say something though.

What follows is somewhat technical, so let’s start with the takeaway conclusion: is larger than any of the lower bounds . In particular, this means that is larger than the th largest of all the lower bounds. That is, if we rearrange the lower bounds in decreasing order , we hace . An entirely analogous argument will give an upper bound, yielding

(4)  

For those interested in the derivation, read on the in the following optional section:

Derivation of the Mathias–Li Bounds

Since and are diagonal, the eigenvectors of the pair are just the standard basis vectors, the th of which we will denote . The trick will be to use the max-min characterization (1) with the subspace spanned by some collection of basis vectors . Churning through a couple inequalities in quick fashion,²³See pg. 17 of the Mathias and Li report. we obtain

   

Here, denotes the convex hull. Since this holds for every set of indices , it in particular holds for the set of indices which makes the largest. Thus, .

How to Use Mathias–Li’s Perturbation Theory

The eigenangle perturbation bound (4) can be instantiated in a variety of ways. We briefly sketch two. The first is to bound by its minimum over all , which then gives a bound on (and )

Plugging into (4) and simplifying gives

(5)  

This improves on Stewart’s bound (3) by replacing the Crawford number by ; as Mathias and Li show is always smaller than or equal to and can be much much smaller.²⁴Recall that Mathias and Li’s bound first requires us to do a change of variables where and both become diagonal, which can increase by a factor of . Thus, for an apples-to-apples comparison with Stewart’s theory where and are non-diagonal, (5) should be interpreted as .

For the second instantiation (4), we recognize that if an eigenangle is sufficiently well-separated from other eigenangles (relative to the size of the perturbation and ), then we have and . (The precise instantiation of “sufficiently well-separated” requires some tedious algebra; if you’re interested, see Footnote 7 in Mathias and Li’s paper.²⁵You may also be interested in Corollary 2.2 in this preprint by myself and coauthors.) Under this separation condition, (4) then reduces to

(6)  

This result improves on Stewart’s result (4) by even more, since we have now replaced the Crawford number by for a sufficiently small perturbation. In fact, a result of this form is nearly as good as one could hope for.²⁶Specifically, the condition number of the eigenangle is , so we know for sufficiently small perturbations we have and is the smallest number for which such a relation holds. Mathias and Li’s theory allows for a statement of this form to be made rigorous for a finite-size perturbation. Again, the only small deficit is the additional factor of “” from the change of variables to diagonal form.

The Elegant Geometry of Generalized Eigenvalue Perturbation Theory

As I said at the start of this post, what fascinates me about this generalized eigenvalue perturbation is the beautiful and elegant geometry. When I saw it for the first time, it felt like a magic trick: a definite generalized eigenvalue problem with real eigenvalues was transformed by sleight of hand into a geometry problem on the complex plane, with solutions involving just a little high school geometry and trigonometry. Upon studying the theory, I began to appreciate it for a different reason. Upon closer examination, the magic trick was revealed to be a sequence of deductions, each logically following naturally from the last. To the pioneers of this subject—Stewart, Sun, Mathias, Li, and others—this sequence of logical deductions was not preordained, and their discovery of this theory doubtlessly required careful thought and leaps of insight. Now that this theory has been discovered, however, we get the benefit of retrospection, and can retell a narrative of this theory where each step follows naturally from the last. When told this way, one almost imagines being able to develop this theory by oneself, where at each stage we appeal to some notion of mathematical elegance (e.g., by treating and symmetrically) or by applying a standard trick (e.g., identifying a pair with the complex number ). Since this theory took several decades to fall into place, we should not let this storytelling exercise fool us into thinking the prospective act of developing a new theory will be as straightforward and linear as this retelling, pruned of dead ends and halts in progress, might suggest.

That said, I do think the development of the perturbation theory of the generalized eigenvalue problem does have a lesson for those of us who seek to develop mathematical theories: be guided by mathematical elegance. At several points in the development of the perturbation theory, we obtained great gains by treating quantities which play a symmetric role in the problem symmetrically in the theory or by treating a pair of real numbers as a complex number and asking how to interpret that complex number. My hope is that this perturbation theory serves as a good example for how letting oneself be guided by intuition, a small array of standard tricks, and a search for elegance can lead one to conceptualize a problem in the right way which leads (after a considerable amount of effort and a few lucky breaks) to a natural solution.

In this post, I want to answer a simple question: how can randomness help in solving a deterministic (non-random) problem?

Let’s start by defining some terminology. An algorithm is just a precisely defined procedure to solve a problem. For example, one algorithm to compute the integral of a function on the interval is to pick 100 equispaced points on this interval and output the Riemann sum . A randomized algorithm is simply an algorithm which uses, in some form, randomly chosen quantities. For instance, a randomized algorithm¹This randomized algorithm is an example of the strategy of Monte Carlo integration, which has proved particularly effective at computing integrals of functions on high-dimensional spaces. for computing integrals would be to pick 100 random points uniformly distributed in the interval and output the average . To help to distinguish, an algorithm which does not use randomness is called a deterministic algorithm.

To address the premise implicit in our central question, there are problems where randomized algorithms provably outperform the best possible deterministic algorithms.²Examples abound in online decision making, distributed computation, linear algebra in the matrix-vector query model, and in simple computational models like single tape Turing machines. Additionally, even for problems for which randomized algorithms have not been formally proven to outperform deterministic ones, the best randomized algorithms we know for many problems dramatically outperform the best-known deterministic algorithms. For example, testing whether an -digit number is prime takes roughly operations using the best-known deterministic algorithm and only roughly operations allowing randomness. The power of randomness extends beyond discrete problems typically considered by computer scientists to continuous problems of interest to computational mathematicians and scientists. For instance, the (asymptotically) fastest known algorithms to solve Laplacian linear system of equations use random sampling as a key ingredient. The workhorse optimization routines in machine learning are mostly variants of stochastic gradient descent, a randomized algorithm. To list off a few more, randomized algorithms have proven incredibly effective in solving eigenvalue and singular value problems, approximating the trace of a very large matrix, computing low-rank approximations, evaluating integrals, and simulating subgrid phenomena in fluid problems.

This may seem puzzling and even paradoxical. There is no randomness in, say, a system of linear equations, so why should introducing randomness help solve such a problem? It can seem very unintuitive that leaving a decision in an algorithm up to chance would be better than making an informed (and non-random) choice. Why do randomized algorithms work so well; what is the randomness actually buying us?

A partial answer to this question is that it can be very costly to choose the best possible option at runtime for an algorithm. Often, a random choice is “good enough”.

A Case Study: Quicksort

Consider the example of quicksort. Given a list of integers to sort in increasing order, quicksort works by selecting an element to be the pivot and then divides the elements of the list into groups larger and smaller than the pivot. One then recurses on the two groups, ending up with a sorted list.

The best choice of pivot is the median of the list, so one might naturally think that one should use the median as the pivot for quicksort. The problem with this reasoning is that finding the median is time-consuming; even using the fastest possible median-finding algorithm,³There is an algorithm guaranteed to find the median in time, which is asymptotically fast as this problem could possibly be solved since any median-finding algorithm must in general look at the entire list.quicksort with exact median pivot selection isn’t very quick. However, one can show quicksort with a pivot selected (uniformly) at random achieves the same expected runtime as quicksort with optimal pivot selection and is much faster in practice.⁴In fact, is the fastest possible runtime for any comparison sorting algorithm.

But perhaps we have given up on fast deterministic pivot selection in quicksort too soon. What if we just pick the first entry of the list as the pivot? This strategy usually works fairly well too, but it runs into an unfortunate shortfall: if one feeds in a sorted list, the first-element pivot selection is terrible, resulting in a algorithm. If one feeds in a list in a random order, the first-element pivot selection has a expected runtime,⁵Another way of implementing randomized quicksort is to first randomize the list and then always pick the first entry as the pivot. This is fully equivalent to using quicksort with random pivot selection in the given ordering. Note that randomizing the order of the list before its sorted is still a randomized algorithm because, naturally, randomness is needed to randomize order. but for certain specific orderings (in this case, e.g., the sorted ordering) the runtime is a disappointing . The first-element pivot selection is particularly bad since its nemesis ordering is the common-in-practice already-sorted ordering. But other simple deterministic pivot selections are equally bad. If one selects, for instance, the pivot to be the entry in the position , then we can still come up with an ordering of the input list that makes the algorithm run in time . And because the algorithm is deterministic, it runs in time every time with such-ordered inputs. If the lists we need to sort in our code just happen to be bad for our deterministic pivot selection strategy, quicksort will be slow every time.

We typically analyze algorithms based on their worst-case performance. And this can often be unfair to algorithms. Some algorithms work excellently in practice but are horribly slow on manufactured examples which never occur in “real life”.⁶The simplex algorithm is an example of algorithm which is often highly effective in practice, but can take a huge amount of time on some pathological cases. But average-case analysis of algorithms, where one measures the average performance of an algorithm over a distribution of inputs, can be equally misleading. If one were swayed by the average-case analysis of the previous section for quicksort with first-element pivot selection, one would say that this should be an effective pivot selection in practice. But sorting an already-sorted or nearly-sorted list occurs all the time in practice: how many times do programmers read in a sorted list from a data source and sort it again just to be safe? In real life, we don’t encounter random inputs to our algorithms: we encounter a very specific distribution of inputs specific to the application we use the algorithm in. An algorithm with excellent average-case runtime analysis is of little use to me if it is consistently and extremely slow every time I run it on my input data on the problem I’m working on. This discussion isn’t meant to disparage average-case analysis (indeed, very often the “random input” model is not too far off), but to explain why worst-case guarantees for algorithms can still be important.

To summarize, often we have a situation where we have an algorithm which makes some choice (e.g. pivot selection). A particular choice usually works well for a randomly chosen input, but can be stymied by certain specific inputs. However, we don’t get to pick which inputs we receive, and it is fully possible the user will always enter inputs which are bad for our algorithms. Here is where randomized algorithms come in. Since we are unable to randomize the input, we instead randomize the algorithm.

A randomized algorithm can be seen as a random selection from a collection of deterministic algorithms. Each individual deterministic algorithm may be confounded by an input, but most algorithms in the collection will do well on any given input. Thus, by picking a random algorithm from our collection, the probability of poor algorithmic performance is small. And if we run our randomized algorithm on an input and it happens to do poorly by chance, if we run it again it is unlikely to do so; there isn’t a specific input we can create which consistently confounds our randomized algorithm.

The Game Theory Framework

Let’s think about algorithm design as a game. The game has two players: you and an opponent. The game is played by solving a certain algorithmic problem, and the game has a score which quantifies how effectively the problem was solved. For example, the score might be the runtime of the algorithm, the amount of space required, or the error of a computed quantity. For simplicity of discussion, let’s use runtime as an example for this discussion. You have to pay your opponent a dollar for every second of runtime, so you want to have the runtime be as low as possible.⁷In this scenario, we consider algorithms which always produce the correct answer to the problem, and it is only a matter of how long it takes to do so. In the field of algorithms, randomized algorithms of this type are referred to as Las Vegas algorithms. Algorithms which can also give the wrong answer with some (hopefully small) probability are referred to as Monte Carlo algorithms.

Each player makes one move. Your move is to present an algorithm which solves the problem. Your opponent’s move is to provide an input to your algorithm. Your algorithm is then run on your opponents input and you pay your opponent a dollar for every second of runtime.

This setup casts algorithm design as a two-person, zero-sum game. It is a feature of such games that a player’s optimal strategy is often mixed, picking a move at random subject to some probability distribution over all possible moves.

To see why this is the case, let’s consider a classic and very simple game: rock paper scissors. If I use a deterministic strategy, then I am forced to pick one choice, say rock, and throw it every time. But then my savvy opponent could pick paper and be assured victory. By randomizing my strategy and selecting between rock, paper, and scissors (uniformly) at random, I improve my odds to winning a third of the time, losing a third of the time, and drawing a third of the time. Further, there is no way my opponent can improve their luck by adopting a different strategy. This strategy is referred to as minimax optimal: among all (mixed) strategies I could adopt, this strategy has the best performance over all strategies provided my opponent always counters my strategy with the best response strategy they can find.

Algorithm design is totally analogous. For the pivot selection problem, if I pick a the fixed strategy of choosing the first entry to be the pivot (analogous to always picking rock), then my opponent could always give me a sorted list (analogous to always picking paper) and I would always lose. My randomizing my pivot selection, my opponent could input the list in whatever order they choose and my pivot selection will always have the excellent (expected) runtime characteristic of quicksort. In fact, randomized pivot selection is the minimax optimal pivot selection strategy (assuming pivot selection is non-adaptive in that we don’t choose the pivot based on the values in the list).⁸This is not to say that quicksort with randomized pivot selection is necessarily the minimax optimal sorting algorithm, but to say that once we have fixed quicksort as our sorting algorithm, randomized pivot selection is the minimax optimal non-adaptive pivot selection strategy for quicksort.

How Much Does Randomness Buy?

Hopefully, now, the utility of randomness is less opaque. Randomization, in effect, allows an algorithm designer to trade algorithm which runs fast for most inputs all of the time for an algorithm which runs fast for all inputs most of the time. They do this by introducing randomized decision-making to hedge against particular bad inputs which could confound their algorithm.

Randomness, of course, is not a panacea. Randomness does not allow us to solve every algorithmic problem arbitrarily fast. But how can we quantify this? Are there algorithmic speed limits for computational problems for which no algorithm, randomized or not, can exceed?

The game theoretic framework for randomized algorithms can shed light on this question. Let us return to the framing of last section where you choose an algorithm , your opponent chooses an input , and you pay your opponent a dollar for every second of runtime. Since this cost depends on the algorithm and the input, we can denote the cost .

Suppose you devise a randomized algorithm for this task, which can be interpreted as selecting an algorithm from a probability distribution over the class of all algorithms which solve the problem. (Less formally, we assign each possible algorithm a probability of picking it and pick one subject to these probabilities.) Once your opponent sees your randomized algorithm (equivalently, the distribution ), they can come up with the on-average slowest possible input and present it to your algorithm.

But now let’s switch places to see things from your opponent. Suppose they choose their own strategy of randomly selecting an input from among all inputs with some distribution . Once you see the distribution , you can come up with the best possible deterministic algorithm to counter their strategy.

The next part gets a bit algebraic. Suppose now we apply your randomized algorithm against your opponents strategy. Then, your randomized algorithm could only take longer on average than because, by construction, is the fastest possible algorithm against the input distribution . Symbolically, we have

(1)  

Here, and denote the average (expected) value of a random quantity when an input is drawn randomly with distribution or an algorithm is drawn randomly with distribution . But we know that is the slowest input for our randomized algorithm, so, on average, our randomized algorithm will take longer on worst-case input then a random input from . In symbols,

(2)  

Combining Eqs. (1) and (2) gives Yao’s minimax principle⁹Note that Yao’s minimax principle is a consequence of von Neumann’s minimax theorem in game theory.:

(3)  

In words, the average performance of any randomized algorithm on its worst-case input can be no better than the average performance of the best possible deterministic algorithm for a distribution of inputs.¹⁰In fact, there is a strengthening of Yao’s minimax principle. That Eqs. (1) and (2) are equalities when and are taken to be the minimax optimal randomized algorithm and input distribution, respectively. Specifically, assuming the cost is a natural number and we restrict to a finite class of potential inputs,   This nontrivial fact is a consequence of the full power of the von Neumann’s minimax theorem, which itself is a consequence of strong duality in linear programming. My thanks go to Professor Leonard Schulman for teaching me this result.

This, in effect, allows the algorithm analyst seeking to prove “no randomized algorithm can do better than this” to trade randomness in the algorithm to randomness in the input in their analysis. This is a great benefit because randomness in an algorithm can be used in arbitrarily complicated ways, whereas random inputs can be much easier to understand. To prove any randomized algorithm takes at least cost to solve a problem, the algorithm analyst can find a distribution on which every deterministic algorithm takes at least cost . Note that Yao’s minimax principle is an analytical tool, not an algorithmic tool. Yao’s principle establishes speed limits on the best possible randomized algorithm: it does not imply that one can just use deterministic algorithms and assume or make the input to be random.

There is a fundamental question concerning the power of randomized algorithms not answered by Yao’s principle that is worth considering: how much better can randomized algorithms be than deterministic ones? Could infeasible problems for deterministic computations be solved tractably by randomized algorithms?

Questions such as these are considered in the field of computational complexity theory. In this context, one can think of a problem as tractably solvable if it can be solved in polynomial time—that is, in an amount of time proportional to a polynomial function of the size of the input. Very roughly, we call the class of all such problems to be . If one in addition allows randomness, we call this class of problems .¹¹More precisely, is the class of problems solvable in polynomial time by a Monte Carlo randomized algorithm. The class of problems solvable in polynomial time by a Las Vegas randomized algorithm, which we’ve focused on for the duration of this article, is a possibly smaller class called .

It has widely been conjectured that : all problems that can be tractably solved with randomness can tractably be solved without. There is some convincing evidence for this belief. Thus, provided this conjecture turns out to be true, randomness can give us reductions in operation counts by a polynomial amount in the input size for problems already in , but they cannot efficiently solve -hard computational problems like the traveling salesman problem.¹²Assuming the also quite believable conjecture that .

So let’s return to the question which is also this article’s title: why randomized algorithms? Because randomized algorithms are often faster. Why, intuitively, is this the case? Randomization can us to upgrade simple algorithms that are great for most inputs to randomized algorithms which are great most of the time for all inputs. How much can randomization buy? A randomized algorithm on its worst input can be no better than a deterministic algorithm on a worst-case distribution. Assuming a widely believed and theoretically supported, but not yet proven, conjecture, randomness can’t make intractable problems into tractable ones. Still, there is great empirical evidence that randomness can be an immensely effective algorithm design tool in practice, including computational math and science problems like trace estimation and solving Laplacian linear systems.

A singular value decomposition of an matrix is a factorization of the form , where and are square, orthogonal matrices and is a diagonal matrix with th entry .¹Everything carries over essentially unchanged for complex-valued matrices with and being unitary matrices and every being replaced by for the Hermitian transpose. The diagonal entries of are referred to as the singular values of and are conventionally ordered . The columns of the matrices and are referred to as the right- and left- singular vectors of and satisfy the relations and .

One can obtain the singular values and right and left singular vectors of from the eigenvalues and eigenvectors of and . This follows from the calculations and . In other words, the nonzero singular values of are the square roots of the nonzero eigenvalues of and . If one merely solves one of these problems, computing along with or , one can obtain the other matrix or by computing or . (These formulas are valid for invertible square matrices , but similar formulas hold for singular or rectangular to compute the singular vectors with nonzero singular values.)

This approach is often unundesirable for several reasons. Here are a few I’m aware of:

1. Accuracy: Roughly speaking, in double-precision arithmetic, accurate stable numerical methods can resolve differences on the order of 16 orders of magnitude. This means an accurately computed SVD of can resolve the roughly 16 orders of magnitude of decaying singular values, with singular values smaller than that difficult to compute accurately. By computing , we square all of our singular values, so resolving 16 orders of magnitude of the eigenvalues of means we only resolve 8 orders of magnitude of the singular values of .²Relatedly, the two-norm condition number of is twice that of . The dynamic range of our numerical computations has been cut in half!
2. Loss of orthogonality: While and are valid formulas in exact arithmetic, they fair poorly when implemented numerically. Specifically, the numerically computed values and may not be orthogonal matrices with, for example, not even close to the identity matrix. One can, of course, orthogonalize the computed or , but this doesn’t fix the underlying problem that or have not been computed accurately.
3. Loss of structure: If possesses additional structure (e.g. sparsity), this structure may be lost or reduced by computing the product .
4. Nonlinearity: Even if we’re not actually computing the SVD numerically but doing analysis with pencil and paper, finding the SVD of from has the disadvantage of performing a nonlinear transformation on . This prevents us from utilizing additive perturbation theorems for sums of symmetric matrices in our analysis.³For instance, one cannot prove Weyl’s perturbation theorem for singular values by considering and applying Weyl’s perturbation theorem for symmetric eigenvalues.

There are times where these problems are insignificant and this approach is sensible: we shall return to this point in a bit. However, these problems should disqualify this approach from being the de facto way we reduce SVD computation to a symmetric eigenvalue problem. This is especially true since we have a better way.

The better way is by constructing the so-called Hermitian dilation⁴As Stewart and Sun detail in Section 4 of Chapter 1 of their monograph Matrix Perturbation Theory, the connections between the Hermitian dilation and the SVD go back to the discovery of the SVD itself, as it is used in Jordan’s construction of the SVD in 1874. (The SVD was also independently discovered by Beltrami the year previous.) Stewart and Sun refer to this matrix as the Jordan-Wiedlant matrix associated with , as they attribute the widespread use of the matrix today to the work of Wiedlant. We shall stick to the term Hermitian dilation to refer to this matrix. of , which is defined to be the matrix

(1)  

One can show that the nonzero eigenvalues of are precisely plus-or-minus the singular values of . More specifically, we have

(2)  

All of the remaining eigenvalues of not of this form are zero.⁵This follows by noting and thus for account for all the nonzero eigenvalues of . Thus, the singular value decomposition of is entirely encoded in the eigenvalue decomposition of .

This approach of using the Hermitian dilation to compute the SVD of fixes all the issues identified with the “” approach. We are able to accurately resolve a full 16 orders of magnitude of singular values. The computed singular vectors are accurate and numerically orthogonal provided we use an accurate method for the symmetric eigenvalue problem. The Hermitian dilation preserves important structural characteristics in like sparsity. For purposes of theoretical analysis, the mapping is linear.⁶The linearity of the Hermitian dilation gives direct extensions of most results about the symmetric eigenvalues to singular values; see Exercise 22.

Often one can work with the Hermitian dilation only implicitly: the matrix need not actually be stored in memory with all its extra zeros. The programmer designs and implements an algorithm with in mind, but deals with the matrix directly for their computations. In a pinch, however, forming directly in software and utilizing symmetric eigenvalue routines directly is often not too much less efficient than a dedicated SVD routine and can cut down on programmer effort significantly.

As with all things in life, there’s no free lunch here. There are a couple of downsides to the Hermitian dilation approach. First, is, except for the trivial case , an indefinite symmetric matrix. By constast, and are positive semidefinite, which can be helpful in some contexts.⁷This is relevant if, say, we want to find the small singular values by inverse iteration. Positive definite linear systems are easier to solve by either direct or iterative methods. Further, if (respectively, ), then (respectively, ) is tiny compared to , so it might be considerably cheaper to compute an eigenvalue decomposition of (or ) than .

Despite the somewhat salacious title of this article, the and Hermitian dilation approaches both have their role, and the purpose of this article is not to say the approach should be thrown in the dustbin. However, in my experience, I frequently hear the approach stated as the definitive way of converting an SVD into an eigenvalue problem, with the Hermitian dilation approach not even mentioned. This, in my opinion, is backwards. For accuracy reasons alone, the Hermitian dilation should be the go-to tool for turning SVDs into symmetric eigenvalue problems, with the approach only used when the problem is known to have singular values which don’t span many orders of magnitude or is tall and skinny and the computational cost savings of the approach are vital.