Low-Rank Approximation Toolbox: Analysis of the Randomized SVD

In the previous post, we looked at the randomized SVD. In this post, we continue this discussion by looking at the analysis of the randomized SVD. Our approach is adapted from a new analysis of the randomized SVD by Joel A. Tropp and Robert J. Webber.

There are many types of analysis one can do for the randomized SVD. For instance, letting B be a matrix and X the rank-k output of the randomized SVD, natural questions include:

  • What is the expected error \mathbb{E} \norm{X-B} measured in some norm \norm{\cdot}? What about expected squared error \mathbb{E} \left\|X-B\right\|^2? How do these answers change with different norms?
  • How large can the randomized SVD error \norm{X - B} get except for some small failure probability \delta?
  • How close is the randomized SVD truncated to rank \rho compared to the best rank-\rho approximation to B?
  • How close are the singular values and vectors of the randomized SVD approximation X compared to those of B?

Implicitly and explicitly, the analysis of Tropp and Webber provides satisfactory answers to a number of these questions. In the interest of simplifying the presentation, we shall focus our presentation on just one of these questions, proving following result:

    \[\mathbb{E} \left\|B-X\right\|_{\rm F}^2 \le \left( 1 + \frac{k}{k-(r+1)}\right) \left\|B - \lowrank{B}_r \right\|_{\rm F}^2 \quad \text{for every $r \le k-2$}.\]

Here, \left\|\cdot\right\|_{\rm F} is the Frobenius norm. We encourage the interested reader to check out Tropp and Webber’s paper to see the methodology we summarize here used to prove numerous facts about the randomized SVD and its extensions using subspace iteration and block Krylov iteration.

Projection Formula

Let us recall the randomized SVD as we presented it in last post:

  1. Collect information. Form Y = B\Omega where \Omega is an appropriately chosen n\times k random matrix.
  2. Orthogonalize. Compute an orthonormal basis Q for the column space of Y by, e.g., thin QR factorization.
  3. Project. Form C \coloneqq Q^*B, where {}^* denotes the conjugate transpose.

The randomized SVD is the approximation

    \[B\approx X \coloneqq QC.\]

It is easy to upgrade X = QC into a compact SVD form X = U\Sigma V^*, as we did as steps 4 and 5 in the previous post.

For the analysis of the randomized SVD, it is helpful to notice that the approximation X takes the form

    \[X = QC = QQ^*B = \Pi_{B\Omega} B.\]

Here, \Pi_{B\Omega} denotes the orthogonal projection onto the column space of the matrix B\Omega. We call X = \Pi_{B\Omega} B the projection formula for the randomized SVD.1This formula bares a good deal of similarity to the projection formula for the Nyström approximation. This is not a coincidence.

Aside: Quadratic Unitarily Invariant Norms

To state our error bounds in the most general language, we can adopt the language of quadratic unitarily invariant norms. Recall that a square matrix U is unitary if

    \[U^*U = UU^* = I.\]

You may recall from my post on Nyström approximation that a norm \left\|\cdot\right\|_{\rm UI} on matrices is unitarily invariant if

    \[\left\|UBV\right\|_{\rm UI} = \left\|B\right\|_{\rm UI} \quad \text{for all unitary matrices $U$, $V$ and any matrix $B$}.\]

A unitarily invariant norm \left\|\cdot\right\|_{\rm Q} is said to be quadratic if there exists another unitarily invariant norm \left\|\cdot\right\|_{\rm UI} such that

(1)   \[\left\|B\right\|_{\rm Q}^2 = \left\|B^*B\right\|_{\rm UI} = \left\|BB^*\right\|_{\rm UI} \quad \text{for every matrix $B$.} \]

Many examples of quadratic unitarily invariant norms are found among the Schatten p-norms, defined as

    \[\left\|B\right\|_{S_p}^p \coloneqq \sum_i \sigma_i(B)^p.\]

Here, \sigma_1(B) \ge \sigma_2(B) \ge \cdots denote the decreasingly order singular values of B. The Schatten 2-norm \left\|\cdot\right\|_{S_2} is the Frobenius norm of a matrix. The spectral norm (i.e., operator 2-norm) is the Schatten \infty-norm, defined to be

    \[\norm{B} = \left\|B\right\|_{S_\infty} \coloneqq \max_i \sigma_i(B) = \sigma_1(B).\]

The Schatten p-norms are unitarily invariant norms for every 1\le p \le \infty. However, the Schatten p-norms are quadratic unitarily invariant norms only for 2\le p \le \infty since

    \[\left\|B\right\|_{S_p}^2 = \left\|B^*B\right\|_{S_{p/2}}.\]

For the remainder of this post, we let \left\|\cdot\right\|_{\rm Q} and \left\|\cdot\right\|_{\rm UI} be a quadratic unitarily invariant norm pair satisfying (1).

Error Decomposition

The starting point of our analysis is the following decomposition of the error of the randomized SVD:

(2)   \[\left\|B - X\right\|_{\rm Q}^2 \le \left\|B - \lowrank{B\right\|_r}_{\rm Q}^2 + \left\|\lowrank{B\right\|_r - \Pi_{B\Omega} \lowrank{B}_r}_{\rm Q}^2. \]

Recall that we have defined \lowrank{B}_r to be an optimal rank-r approximation to B:

    \[\left\|B - \lowrank{B\right\|_r}_{\rm Q} \le \left\|B - C\right\|_{\rm Q} \quad \text{for any rank-$r$ matrix $C$}.\]

Thus, the error decomposition says that the excess error is bounded by

    \[\left\|B - X\right\|_{\rm Q}^2 - \left\|B - \lowrank{B\right\|_r}_{\rm Q}^2 \le \left\|\lowrank{B\right\|_r - \Pi_{B\Omega} \lowrank{B}_r}_{\rm Q}^2.\]

Using the projection formula, we can prove the error decomposition (2) directly:

    \begin{align*}\left\|B - X\right\|_{\rm Q}^2 &= \left\|B - \Pi_{B\Omega} B \right\|_{\rm Q}^2\\&= \norm{(I-\Pi_{B\Omega})BB^*(I-\Pi_{B\Omega})}_{\rm UI}  \\&= \norm{(I-\Pi_{B\Omega})(BB^* - \lowrank{B}_r\lowrank{B}_r^*)(I-\Pi_{B\Omega})}_{\rm UI} \\&\qquad + \norm{(I-\Pi_{B\Omega})\lowrank{B}_r\lowrank{B}_r^*(I-\Pi_{B\Omega})}_{\rm UI}\\&\le \left\|BB^* - \lowrank{B}_r\lowrank{B}_r^*\right|_{\rm UI} + \left\|\lowrank{B}_r - \Pi_{B\Omega} \lowrank{B}_r\right\|_{\rm Q}^2 \\&\le \left\|B - \lowrank{B}_r \right\|_{\rm Q}^2 + \left\|\lowrank{B}_r- \Pi_{B\Omega} \lowrank{B}_r\right\|_{\rm Q}^2.\end{align*}

The first line is the projection formula, the second is relation between \norm{\cdot}_{\rm Q} and \norm{\cdot}_{\rm UI}, and the third is the triangle inequality. For the fifth line, we use the fact that I - \Pi_{B\Omega} is an orthoprojector and thus has unit spectral norm \norm{I - \Pi_{B\Omega}} together with the fact that

    \[\left\|BCD\right\|_{\rm UI} \le \norm{B} \cdot \left\|C\right\|_{\rm UI}\cdot \norm{D} \quad \text{for all matrices $B,C,D$.}\]

For the final inequality, we used commutation rule

    \[(B - \lowrank{B}_r)(B - \lowrank{B}_r)^* = BB^* - \lowrank{B}_r\lowrank{B}_r^*.\]

Bounding the Error

In this section, we shall continue to refine the error decomposition (2). Consider a thin SVD of B, partitioned as

    \[B = \onebytwo{U_1}{U_2} \twobytwo{\Sigma_1}{0}{0}{\Sigma_2}\onebytwo{V_1}{V_2}^*\]


  • \onebytwo{U_1}{U_2} and \onebytwo{V_1}{V_2} both have orthonormal columns,
  • \Sigma_1 and \Sigma_2 are square diagonal matrices with nonnegative entries,
  • U_1 and V_1 have r columns, and
  • \Sigma_1 is an r\times r matrix.

Under this notation, the best rank-r approximation is \lowrank{B}_r = U_1\Sigma_1V_1^*. Define

    \[\twobyone{\Omega_1}{\Omega_2} \coloneqq \twobyone{V_1^*}{V_2^*} \Omega.\]

We assume throughout that \Omega_1 is full-rank (i.e., \rank \Omega_1 = r).

Applying the error decomposition (2), we get

(3)   \begin{align*}\left\|B - X\right\|_{\rm Q}^2&\le \norm{ \onebytwo{U_1}{U_2} \twobytwo{0}{0}{0}{\Sigma_2}\onebytwo{V_1}{V_2}^*}_{\rm Q}^2 + \norm{(I-\Pi_{B\Omega})U_1\Sigma_1V_1^*}_{\rm Q}^2 \\&= \left\| \Sigma_2 \right\|_{\rm Q}^2 + \norm{(I-\Pi_{B\Omega})U_1\Sigma_1}_{\rm Q}^2. \end{align*}

For the second line, we used the unitary invariance of \left\|\cdot\right\|_{\rm Q}. Observe that the column space of B\Omega is a subset of the column space of B\Omega\Omega_1^\dagger so

(4)   \[\norm{(I-\Pi_{B\Omega})U_1\Sigma_1}_{\rm Q}^2 \le \norm{(I-\Pi_{B\Omega\Omega_1^\dagger})U_1\Sigma_1}_{\rm Q}^2 = \norm{\Sigma_1U_1^*(I-\Pi_{B\Omega\Omega_1^\dagger})U_1\Sigma_1}_{\rm UI}. \]

Let’s work on simplifying the expression


First, observe that

    \[B\Omega\Omega_1^\dagger = B\onebytwo{V_1}{V_2}\twobyone{\Omega_1}{\Omega_2}\Omega_1^\dagger = \onebytwo{U_1}{U_2} \twobytwo{\Sigma_1}{0}{0}{\Sigma_2} \twobyone{I}{\Omega_2\Omega_1^\dagger} = U_1\Sigma_1 + U_2\Sigma_2\Omega_2\Omega_1^\dagger.\]

Thus, the projector \Pi_{B\Omega\Omega_1^\dagger} takes the form

    \begin{align*}\Pi_{B\Omega\Omega_1^\dagger} &= (B\Omega\Omega_1^\dagger) \left[(B\Omega\Omega_1^\dagger)^*(B\Omega\Omega_1^\dagger)\right]^\dagger (B\Omega\Omega_1^\dagger)^* \\&= (U_1\Sigma_1 + U_2\Sigma_2\Omega_2\Omega_1^\dagger) \left[\Sigma_1^2 + (\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)\right]^\dagger (U_1\Sigma_1 + U_2\Sigma_2\Omega_2\Omega_1^\dagger)^*.\end{align*}

Here, {}^\dagger denotes the Moore–Penrose pseudoinverse, which reduces to the ordinary matrix inverse for a square nonsingular matrix. Thus,

(5)   \[\Sigma_1U_1^*(I-\Pi_{B\Omega\Omega_1^\dagger})U_1\Sigma_1 = \Sigma_1^2 - \Sigma_1^2 \left[\Sigma_1^2 + (\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)\right]^\dagger\Sigma_1^2. \]

Remarkably, this seemingly convoluted combination of matrices actually is a well-studied operation in matrix theory, the parallel sum. The parallel sum of positive semidefinite matrices A and H is defined as

(6)   \[A : H \coloneqq A - A(A+H)^\dagger A. \]

We will have much more to say about the parallel sum soon. For now, we use this notation to rewrite (5) as

    \[\Sigma_1U_1^*(I-\Pi_{B\Omega\Omega_1^\dagger})U_1\Sigma_1 = \Sigma_1^2 : [(\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)].\]

Plugging this back into (4) and then (3), we obtain the error bound

(7)   \[\left\|B - X\right\|_{\rm Q}^2\le \left\| \Sigma_2 \right\|_{\rm Q}^2 + \left\|\Sigma_1^2 : [(\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)]\right\|_{\rm UI}. \]

Simplifying this expression further will require more knowledge about parallel sums, which we shall discuss now.

Aside: Parallel Sums

Let us put aside the randomized SVD for a moment and digress to the seemingly unrelated topic of electrical circuits. Suppose we have a battery of voltage v and we connect the ends using a wire of resistance a. The current \mathrm{curr}_a is given by Ohm’s law

    \[v = a \cdot \mathrm{curr}_a.\]

Similarly, if the wire is replaced by a different wire with resistance h, the current \mathrm{curr}_h is then

    \[v = h \cdot \mathrm{curr}_h.\]

Now comes the interesting question. What if we connect the battery by both wires (resistances a and h) simultaneously in parallel, so that current can flow through either wire? Since wires of resistances a and h still have voltage v, Ohm’s law still applies and thus the total current is

    \[\mathrm{curr}_{\rm total} = \mathrm{curr}_a + \mathrm{curr}_h = \frac{v}{a} + \frac{v}{h} = v \left(a^{-1}+h^{-1}\right).\]

The net effect of connecting resisting wires of resistances a and h is the same as a single resisting wire of resistance

(8)   \[a:h \coloneqq \frac{v}{\mathrm{curr}_{\rm total}} = \left( a^{-1}+h^{-1}\right)^{-1}. \]

We call the the operation a \mathbin{:} h the parallel sum of a and h because it describes how resistances combine when connected in parallel.

The parallel sum operation : can be extended to all nonnegative numbers a and h by continuity:

    \[a:h \coloneqq \lim_{b\downarrow a, \: k\downarrow h} b:k = \begin{cases}\left( a^{-1}+h^{-1}\right)^{-1}, & a,h > 0 ,\\0, & \textrm{otherwise}.\end{cases}\]

Electrically, this definition states that one obtains a short circuit (resistance 0) if either of the wires carries zero resistance.

Parallel sums of matrices were introduced by William N. Anderson, Jr. and Richard J. Duffin as a generalization of the parallel sum operation from electrical circuits. There are several equivalent definitions. The natural definition (8) applies for positive definite matrices

    \[A:H = (A^{-1}+H^{-1})^{-1} \quad \text{for $A,H$ positive definite.}\]

This definition can be extended to positive semidefinite matrices by continuity. It can be useful to have an explicit definition which applies even to positive semidefinite matrices. To do so, observe that the (scalar) parallel sum satisfies the identity

    \[a:h = \frac{1}{a^{-1}+h^{-1}} = \frac{ah}{a+h} = \frac{a(a+h)-a^2}{a+h} = a - a(a+h)^{-1}a.\]

To extend to matrices, we capitalize the letters and use the Moore–Penrose pseudoinverse in place of the inverse:

    \[A : H = A - A(A+H)^\dagger A.\]

This was the definition (6) of the parallel sum we gave above which we found naturally in our randomized SVD error bounds.

The parallel sum enjoys a number of beautiful properties, some of which we list here:

  1. Symmetry. A:H = H:A,
  2. Simplified formula. A:H = (A^{-1}+H^{-1})^{-1} for positive definite matrices,
  3. Bounds. A:H \preceq A and A:H \preceq H.
  4. Monotone. A\mapsto (A:H) is monotone in the Loewner order.
  5. Concavity. The map (A,H) \mapsto A:H is (jointly) concave (with respect to the Loewner order).
  6. Conjugation. For any square C, C^*(A:H)C = (C^*AC):(C^*HC).
  7. Trace. \tr(A:H) \le (\tr A):(\tr H).2In fact, this property holds with the trace replaced by any positive linear map. That is, if \Phi is a linear map from square matrices to square matrices satisfying \Phi(A) \succeq 0 if A\succeq 0, then \Phi(A:H) \preceq \Phi(A):\Phi(H).
  8. Unitarily invariant norms. \left\|A:H\right\|_{\rm UI} \le \left\|A\right\|_{\rm UI} : \left\|H\right\|_{\rm UI}.

For completionists, we include a proof of the last property for reference.

Proof of Property 8
Let \left\|\cdot\right\|_{\rm UI}' be the unitarily invariant norm dual to \left\|\cdot\right\|_{\rm UI}.3Dual with respect to the Frobenius inner product, that is. By duality, we have

    \begin{align*}\left\|A:H\right\|_{\rm UI} &= \max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \tr((A:H)M) \\&= \max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \tr(M^{1/2}(A:H)M^{1/2})\\&= \max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \tr((M^{1/2}AM^{1/2}):(M^{1/2}HM^{1/2}))  \\&\le \max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \left[\tr(M^{1/2}AM^{1/2}):\tr(M^{1/2}HM^{1/2}) \right] \\&\le \left[\max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \tr(M^{1/2}AM^{1/2})\right]:\left[\max_{M\succeq 0,\: \left\|M\right\|_{\rm UI}'\le 1} \tr(M^{1/2}HM^{1/2})\right]\\&= \left\|A\right\|_{\rm UI} : \left\|H\right\|_{\rm UI}.\end{align*}

The first line is duality, the second holds because M\succeq 0, the third line is property 6, the fourth line is property 7, the fifth line is property 4, and the sixth line is duality.

Nearing the Finish: Enter the Randomness

Equipped with knowledge about parallel sums, we are now prepared to return to bounding the error of the randomized SVD. Apply property 8 of the parallel sum to the randomized SVD bound (7) to obtain

    \begin{align*}\left\|B - X\right\|_{\rm Q}^2&\le \left\| \Sigma_2 \right\|_{\rm Q}^2 + \left\|\Sigma_1^2 : [(\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)]\right\|_{\rm UI} \\&\le \left\| \Sigma_2 \right\|_{\rm Q}^2 + \left\|\Sigma_1^2\right\|_{\rm UI} : \left\|[(\Sigma_2\Omega_2\Omega_1^\dagger)^*(\Sigma_2\Omega_2\Omega_1^\dagger)]\right\|_{\rm UI} \\&= \left\| \Sigma_2 \right\|_{\rm Q}^2 + \left\|\Sigma_1\right\|_{\rm Q}^2 : \left\|\Sigma_2\Omega_2\Omega_1^\dagger\right\|^2_{\rm Q}.\end{align*}

This bound is totally deterministic: It holds for any choice of test matrix \Omega, random or otherwise. We can use this bound to analyze the randomized SVD in many different ways for different kinds of random (or non-random) matrices \Omega. Tropp and Webber provide a number of examples instantiating this general bound in different ways.

We shall present only the simplest error bound for randomized SVD. We make the following assumptions:

  • The test matrix \Omega is standard Gaussian.4By which, we mean the entries of \Omega are independent and standard normal.
  • The norm \left\|\cdot\right\|_{\rm Q} is the Frobenius norm \left\|\cdot\right\|_{\rm F}.
  • The randomized SVD rank k is \ge r-2.

Under these assumptions and using the property a : h \le h, we obtain the following expression for the expected error of the randomized SVD

(9)   \[\mathbb{E} \left\|B - X\right\|_{\rm F}^2\le \left\| B - \lowrank{B\right\|_r }_{\rm F}^2 + \mathbb{E}\left\|\Sigma_2\Omega_2\Omega_1^\dagger\right\|^2_{\rm F}. \]

All that is left to is compute the expectation of \left\|\Sigma_2\Omega_2\Omega_1^\dagger\right\|_{\rm F}^2. Remarkably, this can be done in closed form.

Aside: Expectation of Gaussian–Inverse Gaussian Matrix Product

The final ingredient in our randomized SVD analysis is the following computation involving Gaussian random matrices. Let G and \Gamma be independent standard Gaussian random matrices with \Gamma of size r\times k, k\ge r-2. Then

    \[\mathbb{E} \left\|SG\Gamma^\dagger R\right\|_{\rm F}^2 = \frac{1}{k-(r+1)}\left\|S\right\|_{\rm F}^2\left\|R\right\|_{\rm F}^2.\]

For the probability lovers, we will now go on to prove this. For those who are less probabilistically inclined, this result can be treated as a black box.

Proof of Random Matrix Formula
To prove this, first consider a simpler case where \Gamma^\dagger does not appear:

    \[\mathbb{E} \left\|SGW\right\|_{\rm F}^2 = \mathbb{E} \sum_{ij} \left(\sum_{k\ell} s_{ik}g_{k\ell}w_{\ell j} \right)^2 = \mathbb{E} \sum_{ijk\ell} s_{ik}^2 w_{\ell j}^2 = \left\|S\right\|_{\rm F}^2\left\|W\right\|_{\rm F}^2.\]

Here, we used the fact that the entries g_{k\ell} are independent, mean-zero, and variance-one. Thus, applying this result conditionally on \Gamma with W = \Gamma^\dagger R, we get

(10)   \[\mathbb{E} \left\|SG\Gamma^\dagger R\right\|_{\rm F}^2 =\mathbb{E}\left[ \mathbb{E}\left[ \left\|SG\Gamma^\dagger R\right\|_{\rm F}^2 \,\middle|\, \Gamma\right]\right] =\left\|S\right\|_{\rm F}^2\cdot \mathbb{E} \left\|\Gamma^\dagger R\right\|_{\rm F}^2. \]

To compute \mathbb{E} \left\|\Gamma^\dagger R\right\|_{\rm F}^2, we rewrite using the trace

    \[\mathbb{E} \left\|\Gamma^\dagger R\right\|_{\rm F}^2 = \mathbb{E} \tr \left(\Gamma^\dagger RR^* \Gamma^{*\dagger} \right)= \tr \left(RR^* \mathbb{E}[\Gamma^{*\dagger}\Gamma^\dagger] \right) = \tr \left(RR^* \mathbb{E}[(\Gamma\Gamma^*)^{-1}] \right).\]

The matrix (\Gamma\Gamma^*)^{-1} is known as an inverse-Wishart matrix and is a well-studied random matrix model. In particular, its expectation is known to be (k-(r+1))^{-1}I. Thus, we obtain

    \[\mathbb{E} \left\|\Gamma^\dagger R\right\|_{\rm F}^2 = \tr \left(RR^* \mathbb{E}[(\Gamma\Gamma^*)^{-1}] \right) = \frac{\tr \left(RR^* \right)}{k-(r+1)} = \frac{\left\|R\right\|_{\rm F}^2}{k-(r+1)}.\]

Plugging into (10), we obtain the desired conclusion

    \[\mathbb{E} \left\|SG\Gamma^\dagger R\right\|_{\rm F}^2 =\left\|S\right\|_{\rm F}^2\cdot \mathbb{E} \left\|\Gamma^\dagger R\right\|_{\rm F}^2 = \frac{1}{k-(r+1)}\left\|S\right\|_{\rm F}^2\left\|R\right\|_{\rm F}^2.\]

This completes the proof.

Wrapping it All Up

We’re now within striking distance. All that is left is to assemble the pieces we have been working with to obtain a final bound for the randomized SVD.

Recall we have chosen the random matrix \Omega to be standard Gaussian. By the rotational invariance of the Gaussian distribution, \Omega_1 and \Omega_2 are independent and standard Gaussian as well. Plugging the matrix expectation bound to (9) then completes the analysis

    \begin{align*}\mathbb{E} \left\|B - X\right\|_{\rm F}^2&\le \left\| B - \lowrank{B\right\|_r }_{\rm F}^2 + \mathbb{E}\norm{\Sigma_2\Omega_2\Omega_1^\dagger I_{k\times k}}^2_{\rm F} \\&= \left\| B - \lowrank{B\right\|_r }_{\rm F}^2 + \frac{\left\|\Sigma_2\right\|_{\rm F}^2\norm{I_{k\times k}}_{\rm F}^2}{k-(r+1)} \\&= \left(1 + \frac{k}{k-(r+1)}\right) \left\| B - \lowrank{B\right\|_r }_{\rm F}^2.\end{align*}

Low-Rank Approximation Toolbox: Randomized SVD

The randomized SVD and its relatives are workhorse algorithms for low-rank approximation. In this post, I hope to provide a fairly brief introduction to this useful method. In the following post, we’ll look into its analysis.

The randomized SVD is dead-simple to state: To approximate an m\times n matrix B by a rank-k matrix, perform the following steps:

  1. Collect information. Form Y = B\Omega where \Omega is an appropriately chosen n\times k random matrix.
  2. Orthogonalize. Compute an orthonormal basis Q for the column space of Y by, e.g., thin QR factorization.
  3. Project. Form C \coloneqq Q^*B. (Here, {}^* denotes the conjugate transpose of a complex matrix, which reduces to the ordinary transpose if the matrix is real.)

If all one cares about is a low-rank approximation to the matrix B, one can stop the randomized SVD here, having obtained the approximation

    \[B \approx X \coloneqq Q C.\]

As the name “randomized SVD” suggests, one can easily “upgrade” the approximation X = QC to a compact SVD format:

  1. SVD. Compute a compact SVD of the matrix C: C = \hat{U}\Sigma V^* where \hat{U} and \Sigma and k\times k and V is n\times k.
  2. Clean up. Set U \coloneqq Q\hat{U}.

We now have approximated B by a rank-k matrix X in compact SVD form:

    \[B \approx X = QC = U\Sigma V^*.\]

One can use the factors U, \Sigma, and V of the approximation X \approx B as estimates of the singular vectors and values of the matrix B.

What Can You Do with the Randomized SVD?

The randomized SVD has many uses. Pretty much anywhere you would ordinarily use an SVD is a candidate for the randomized SVD. Applications include model reduction, fast direct solvers, computational biology, uncertainty quantification, among numerous others.

To speak in broad generalities, there are two ways to use the randomized SVD:

  • As an approximation. First, we could use X as a compressed approximation to B. Using the format X = QC, X requires just (m+n)k numbers of storage, whereas B requires a much larger mn numbers of storage. As I detailed at length in my blog post on low-rank matrices, many operations are cheap for the low-rank matrix X. For instance, we can compute a matrix–vector product Xz in roughly (m+n)k operations rather than the mn operations to compute Bz. For these use cases, we don’t need the “SVD” part of the randomized SVD.
  • As an approximate SVD. Second, we can actually use the U, \Sigma, and V produced by the randomized SVD as approximations to the singular vectors and values of B. In these applications, we should be careful. Just because X is a good approximation to B, it is not necessarily the case that U, \Sigma, and V are close to the singular vectors and values of B. To use the randomized SVD in this context, it is safest to use posterior diagnostics (such as the ones developed in this paper of mine) to ensure that the singular values/vectors of interest are computed to a high enough accuracy. A useful rule of thumb is the smallest two to five singular values and vectors computed by the randomized SVD are suspect and should be used in applications only with extreme caution. When the appropriate care is taken and for certain problems, the randomized SVD can provide accurate singular vectors far faster than direct methods.

Accuracy of the Randomized SVD

How accurate is the approximation X produced by the randomized SVD? No rank-k approximation can be more accurate than the best rank-k approximation \lowrank{B}_k to the matrix B, furnished by an exact SVD of B truncated to rank k. Thus, we’re interested in how much larger B - X is than B - \lowrank{B}_k.

Frobenius Norm Error

A representative result describing the error of the randomized SVD is the following:

(1)   \[\mathbb{E} \left\|B - X\right\|_{\rm F}^2 \le \min_{r \le k-2} \left( 1 + \frac{r}{k-(r+1)} \right) \left\|B - \lowrank{B}_r\right\|^2_{\rm F}. \]

This result states that the average squared Frobenius-norm error of the randomized SVD is comparable with the best rank-r approximation error for any r\le k-2. In particular, choosing k = r+2, we see that the randomized SVD error is at most (1+r) times the best rank-r approximation

(2)   \[\mathbb{E} \left\|B - X\right\|_{\rm F}^2 \le (1+r) \left\|B - \lowrank{B}_r \right\|^2_{\rm F} \quad \text{for $k=r+2$}. \]

Choosing k = 2r+1, we see that the randomized SVD has at most twice the error of the best rank-r approximation

(3)   \[\mathbb{E} \left\|B - X\right\|_{\rm F}^2 \le 2 \left\|B - \lowrank{B}_r\right\|^2_{\rm F} \quad \text{for $k=2r+1$}. \]

In effect, these results tell us that if we want an approximation that is nearly as good as the best rank-r approximation, using an approximation rank of, say, k = r+2 or k = 2r for the randomized SVD suffices. These results hold even for worst-case matrices. For nice matrices with steadily decaying singular values, the randomized SVD can perform even better than equations (2)–(3) would suggest.

Spectral Norm Error

The spectral norm is often a better error metric for matrix computations than the Frobenius norm. Is the randomized SVD also comparable with the best rank-k approximation when we use the spectral norm? In this setting, a representative error bound is

(4)   \[\mathbb{E} \left\|B - X\right\|^2 \le \min_{r \le k-2} \left( 1 + \frac{2r}{k-(r+1)} \right) \left(\left\|B - \lowrank{B}_r \right\|^2 + \frac{\mathrm{e}^2}{k-r} \left\|B - \lowrank{B}_r\right\|^2_{\rm F} \right). \]

The spectral norm error of the randomized SVD depends on the Frobenius norm error of the best rank-r approximation for r \le k-2.

Recall that the spectral and Frobenius norm can be defined in terms of the singular values of the matrix B:

    \[\norm{B} = \sigma_1(B),\quad \left\|B\right\|_{\rm F}^2 = \sum_i \sigma_i(B)^2.\]

Rewriting (4) using these formulas, we get

    \[\mathbb{E} \left\|B - X\right\|^2 \le \min_{r \le k-2} \left( 1 + \frac{2r}{k-(r+1)} \right) \left(\sigma_{r+1}^2 + \frac{\mathrm{e}^2}{k-r} \sum_{i>r} \sigma_i^2 \right).\]

Despite being interested in the largest singular value of the error matrix B-X, this bound demonstrates that the randomized SVD incurs errors based on the entire tail of B‘s singular values \sum_{i>r}\sigma_i^2. The randomized SVD is much worse than the best rank-k approximation for a matrix B with a long detail of slowly declining singular values.

Improving the Approximation: Subspace Iteration and Block Krylov Iteration

We saw that the randomized SVD produces nearly optimal low-rank approximations when we measure using the Frobenius norm. When we measure using the spectral norm, we have a split decision: If the singular values decay rapidly, the randomized SVD is near-optimal; if the singular values decay more slowly, the randomized SVD can suffer from large errors.

Fortunately, there are two fixes to improve the randomized SVD in the presence of slowly decaying singular values:

  • Subspace iteration: To improve the quality of the randomized SVD, we can combine it with the famous power method for eigenvalue computations. Instead of Y = B\Omega, we use the powered expression Y = B(B^*B)^q\Omega. Powering has the effect of amplifying the large singular values of B and dimishing the influence of the tail.
  • Block Krylov iteration: Subspace iteration is effective, but fairly wasteful. To compute B(B^*B)^q\Omega we compute the block Krylov sequence

        \[B\Omega, BB^*B\Omega, BB^*BB^*B\Omega,\ldots,B(B^*B)^q\Omega\]

    and throw away all but the last term. To make more economical use of resources, we can use the entire sequence as our information matrix Y:

        \[Y = \begin{bmatrix} B\Omega & BB^*B\Omega & BB^*BB^*B\Omega& \cdots & B(B^*B)^q\Omega\end{bmatrix}.\]

    Storing the entire block Krylov sequence is more memory-intensive but makes more efficient use of matrix products than subspace iteration.

Both subspace iteration and block Krylov iteration should be carefully implemented to produce accurate results.

Both subspace iteration and block Krylov iteration diminish the effect of a slowly decaying tail of singular values on the accuracy of the randomized SVD. The more slowly the tail decays, the larger one must take the number of iterations q to obtain accurate results.

Low-Rank Approximation Toolbox: Nyström, Cholesky, and Schur

In the last post, we looked at the Nyström approximation to an N\times N positive semidefinite (psd) matrix A. A special case was the column Nyström approximation, defined to be1We use Matlab index notation to indicate submatrices of A.

(Nys)   \[\hat{A} = A(:,S) \, A(S,S)^{-1} \, A(S,:), \]

where S = \{s_1,\ldots,s_k\} \subseteq \{1,2,\ldots,N\} identifies a subset of k columns of A. Provided k\ll N, this allowed us to approximate all N^2 entries of the matrix A using only the kN entries in columns s_1,\ldots,s_k of A, a huge savings of computational effort!

With the column Nyström approximation presented just as such, many questions remain:

  • Why this formula?
  • Where did it come from?
  • How do we pick the columns s_1,\ldots,s_k?
  • What is the residual A - \hat{A} of the approximation?

In this post, we will answer all of these questions by drawing a connection between low-rank approximation by Nyström approximation and solving linear systems of equations by Gaussian elimination. The connection between these two seemingly unrelated areas of matrix computations will pay dividends, leading to effective algorithms to compute Nyström approximations by the (partial) Cholesky factorization of a positive (semi)definite matrix and an elegant description of the residual of the Nyström approximation as the Schur complement.

Cholesky: Solving Linear Systems

Suppose we want solve the system of linear equations Ax = b, where A is a real N\times N invertible matrix and b is a vector of length N. The standard way of doing this in modern practice (at least for non-huge matrices A) is by means of Gaussian elimination/LU factorization. We factor the matrix A as a product A = LU of a lower triangular matrix L and an upper triangular matrix U.2To make this accurate, we usually have to reorder the rows of the matrix A as well. Thus, we actually compute a factorization PA = LU where P is a permutation matrix and L and U are triangular. The system Ax = b is solved by first solving Ly = b for y and then Ux = y for x; the triangularity of L and U make solving the associated systems of linear equations easy.

For real symmetric positive definite matrix A, a simplification is possible. In this case, one can compute an LU factorization where the matrices L and U are transposes of each other, U = L^\top. This factorization A = LL^\top is known as a Cholesky factorization of the matrix A.

The Cholesky factorization can be easily generalized to allow the matrix A to be complex-valued. For a complex-valued positive definite matrix A, its Cholesky decomposition takes the form A = LL^*, where L is again a lower triangular matrix. All that has changed is that the transpose {}^\top has been replaced by the conjugate transpose {}^*. We shall work with the more general complex case going forward, though the reader is free to imagine all matrices as real and interpret the operation {}^* as ordinary transposition if they so choose.

Schur: Computing the Cholesky Factorization

Here’s one way of computing the Cholesky factorization using recursion. Write the matrix A in block form as

    \[A = \twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{22}}. \]

Our first step will be “block Cholesky factorize” the matrix A, factoring A as a product of matrices which are only block triangular. Then, we’ll “upgrade” this block factorization into a full Cholesky factorization.

The core idea of Gaussian elimination is to combine rows of a matrix to introduce zero entries. For our case, observe that multiplying the first block row of A by A_{21}A_{11}^{-1} and subtracting this from the second block row introduces a matrix of zeros into the bottom left block of A. (The matrix A_{11} is a principal submatrix of A and is therefore guaranteed to be positive definite and thus invertible.3To directly see A_{11} is positive definite, for instance, observe that since A is positive definite, x^* A_{11}x = \twobyone{x}{0}^* A\twobyone{x}{0} > 0 for every nonzero vector x.) In matrix language,

    \[\twobytwo{I}{0}{-A_{21}A_{11}^{-1}}{I}\twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{22}} = \twobytwo{A_{11}}{A_{12}}{0}{A_{22} - A_{21}A_{11}^{-1}A_{12}}.\]

Isolating A on the left-hand side of this equation by multiplying by

    \[\twobytwo{I}{0}{-A_{21}A_{11}^{-1}}{I}^{-1} = \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I}\]

yields the block triangular factorization

    \[A = \twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{22}} = \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I} \twobytwo{A_{11}}{A_{12}}{0}{A_{22} - A_{21}A_{11}^{-1}A_{12}}.\]

We’ve factored A into block triangular pieces, but these pieces are not (conjugate) transposes of each other. Thus, to make this equation more symmetrical, we can further decompose

(1)   \[A = \twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{22}} = \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I} \twobytwo{A_{11}}{0}{0}{A_{22} - A_{21}A_{11}^{-1}A_{12}} \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I}^*. \]

This is a block version of the Cholesky decomposition of the matrix A taking the form A = LDL^*, where L is a block lower triangular matrix and D is a block diagonal matrix.

We’ve met the second of our main characters, the Schur complement

(Sch)   \[S = A_{22} - A_{21}A_{11}^{-1}A_{12}. \]

This seemingly innocuous combination of matrices has a tendency to show up in surprising places when one works with matrices.4See my post on the Schur complement for more examples. It’s appearance in any one place is unremarkable, but the shear ubiquity of A_{22} - A_{21}A_{11}^{-1}A_{12} in matrix theory makes it deserving of its special name, the Schur complement. To us for now, the Schur complement is just the matrix appearing in the bottom right corner of our block Cholesky factorization.

The Schur complement enjoys the following property:5This property is a consequence of equation (1) together with the conjugation rule for positive (semi)definiteness, which I discussed in this previous post.

Positivity of the Schur complement: If A=\twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{22}} is positive (semi)definite, then the Schur complement S = A_{22} - A_{21}A_{11}^{-1}A_{12} is positive (semi)definite.

As a consequence of this property, we conclude that both A_{11} and S are positive definite.

With the positive definiteness of the Schur complement in hand, we now return to our Cholesky factorization algorithm. Continue by recursively6As always with recursion, one needs to specify the base case. For us, the base case is just that Cholesky decomposition of a 1\times 1 matrix A is A = A^{1/2} \cdot A^{1/2}. computing Cholesky factorizations of the diagonal blocks

    \[A_{11} = L_{11}^{\vphantom{*}}L_{11}^*, \quad S = L_{22}^{\vphantom{*}}L_{22}^*.\]

Inserting these into the block LDL^* factorization (1) and simplifying gives a Cholesky factorization, as desired:

    \[A = \twobytwo{L_{11}}{0}{A_{21}^{\vphantom{*}}(L_{11}^{*})^{-1}}{L_{22}}\twobytwo{L_{11}}{0}{A_{21}^{\vphantom{*}}(L_{11}^{*})^{-1}}{L_{22}}^* =: LL^*.\]

Voilà, we have obtained a Cholesky factorization of a positive definite matrix A!

By unwinding the recursions (and always choosing the top left block A_{11} to be of size 1\times 1), our recursive Cholesky algorithm becomes the following iterative algorithm: Initialize L to be the zero matrix. For j = 1,2,3,\ldots,N, perform the following steps:

  1. Update L. Set the jth column of L:

        \[L(j:N,j) \leftarrow A(j:N,j)/\sqrt{a_{jj}}.\]

  2. Update A. Update the bottom right portion of A to be the Schur complement:

        \[A(j+1:N,j+1:N)\leftarrow A(j+1:N,j+1:N) - \frac{A(j+1:N,j)A(j,j+1:N)}{a_{jj}}.\]

This iterative algorithm is how Cholesky factorization is typically presented in textbooks.

Nyström: Using Cholesky Factorization for Low-Rank Approximation

Our motivating interest in studying the Cholesky factorization was the solution of linear systems of equations Ax = b for a positive definite matrix A. We can also use the Cholesky factorization for a very different task, low-rank approximation.

Let’s first look at things through the lense of the recursive form of the Cholesky factorization. The first step of the factorization was to form the block Cholesky factorization

    \[A = \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I} \twobytwo{A_{11}}{0}{0}{A_{22} - A_{21}A_{11}^{-1}A_{12}} \twobytwo{I}{0}{A_{21}A_{11}^{-1}}{I}^*.\]

Suppose that we choose the top left A_{11} block to be of size k\times k, where k is much smaller than N. The most expensive part of the Cholesky factorization will be the recursive factorization of the Schur complement A_{22} - A_{21}A_{11}^{-1} A_{12}, which is a large matrix of size (N-k)\times (N-k).

To reduce computational cost, we ask the provocative question: What if we simply didn’t factorize the Schur complement? Observe that we can write the block Cholesky factorization as a sum of two terms

(2)   \[A = \twobyone{I}{A_{21}{A_{11}^{-1}}} A_{11} \twobyone{I}{A_{22}{A_{11}^{-1}}}^* + \twobytwo{0}{0}{0}{A_{22}-A_{21}A_{11}^{-1}A_{12}}. \]

We can use the first term of this sum as a rank-k approximation to the matrix A. The low-rank approximation, which we can write out more conveniently as

    \[\hat{A} = \twobyone{A_{11}}{A_{21}} A_{11}^{-1} \onebytwo{A_{11}}{A_{12}} = \twobytwo{A_{11}}{A_{12}}{A_{21}}{A_{21}A_{11}^{-1}A_{12}},\]

is the column Nyström approximation (Nys) to A associated with the index set S = \{1,2,3,\ldots,k\} and is the final of our three titular characters. The residual of the Nyström approximation is the second term in (2), which is none other than the Schur complement (Sch), padded by rows and columns of zeros:

    \[A - \hat{A} = \twobytwo{0}{0}{0}{A_{22}-A_{21}A_{11}^{-1}A_{12}}.\]

Observe that the approximation \hat{A} is obtained from the process of terminating a Cholesky factorization midway through algorithm execution, so we say that the Nyström approximation results from a partial Cholesky factorization of the matrix A.

Summing things up:

If we perform a partial Cholesky factorization on a positive (semi)definite matrix, we obtain a low-rank approximation known as the column Nyström approximation. The residual of this approximation is the Schur complement, padded by rows and columns of zeros.

The idea of obtaining a low-rank approximation from a partial matrix factorization is very common in matrix computations. Indeed, the optimal low-rank approximation to a real symmetric matrix is obtained by truncating its eigenvalue decomposition and the optimal low-rank approximation to a general matrix is obtained by truncating its singular value decomposition. While the column Nyström approximation is not the optimal rank-k approximation to A (though it does satisfy a weaker notion of optimality, as discussed in this previous post), it has a killer feature not possessed by the optimal approximation:

The column Nyström approximation is formed from only k columns from the matrix A. A column Nyström approximation approximates an N\times N matrix by only reading a fraction of its entries!

Unfortunately there’s not a free lunch here. The column Nyström is only a good low-rank approximation if the Schur complement has small entries. In general, this need not be the case. Fortunately, we can improve our situation by means of pivoting.

Our iterative Cholesky algorithm first performs elimination using the entry in position (1,1) followed by position (2,2) and so on. There’s no need to insist on this exact ordering of elimination steps. Indeed, at each step of the Cholesky algorithm, we can choose whichever diagonal position (j,j) that we want to perform elimination. The entry we choose to perform elimination with is called a pivot.

Obtaining good column Nyström approximations requires identifying the a good choice for the pivots to reduce the size of the entries of the Schur complement at each step of the algorithm. With general pivot selection, an iterative algorithm for computing a column Nyström approximation by partial Cholesky factorization proceeds as follows: Initialize an N\times k matrix F to store the column Nyström approximation \hat{A} = FF^*, in factored form. For j = 1,2,\ldots,k, perform the following steps:

  1. Select pivot. Choose a pivot s_j.
  2. Update the approximation. F(:,j) \leftarrow A(:,s_j) / \sqrt{a_{s_js_j}}.
  3. Update the residual. A \leftarrow A - \frac{A(:,s_j)A(s_j,:)}{a_{s_js_j}}.

This procedure results in the Nyström approximation (Nys) with column set S = \{s_1,\ldots,s_k\}:

    \[\hat{A} = FF^* = A(:,S) \, A(S,S)^{-1} \, A(S,:).\]

The pivoted Cholesky steps 1–3 requires updating the entire matrix A at every step. With a little more cleverness, we can optimize this procedure to only update the entries of the matrix A we need to form the approximation \hat{A} = FF^*. See Algorithm 2 in this preprint of my coauthors and I for details.

How should we choose the pivots? Two simple strategies immediately suggest themselves:

  • Uniformly random. At each step j, select s_j uniformly at random from among the un-selected pivot indices.
  • Greedy.7The greedy pivoting selection is sometimes known as diagonal pivoting or complete pivoting in the numerical analysis literature. At each step j, select s_j according to the largest diagonal entry of the current residual A:

        \[s_j = \argmax_{1\le k\le N} a_{kk}.\]

The greedy strategy often (but not always) works well, and the uniformly random approach can work surprisingly well if the matrix A is “incoherent”, with all rows and columns of the matrix possessing “similar importance”.

Despite often working fairly well, both the uniform and greedy schemes can fail significantly, producing very low-quality approximations. My research (joint with Yifan Chen, Joel A. Tropp, and Robert J. Webber) has investigated a third strategy striking a balance between these two approaches:

  • Diagonally weighted random. At each step j, select s_j at random according to the probability weights based on the current diagonal of the matrix

        \[\mathbb{P} \{ s_j = k \} = \frac{a_{kk}}{\operatorname{tr} A}.\]

Our paper provides theoretical analysis and empirical evidence showing that this diagonally-weighted random pivot selection (which we call randomly pivoted Cholesky aka RPCholesky) performs well at approximating all positive semidefinite matrices A, even those for which uniform random and greedy pivot selection fail. The success of this approach can be seen in the following examples (from Figure 1 in the paper), which shows RPCholesky can produce much smaller errors than the greedy and uniform methods.


In this post, we’ve seen that a column Nyström approximation can be obtained from a partial Cholesky decomposition. The residual of the approximation is the Schur complement. I hope you agree that this is a very nice connection between these three ideas. But beyond its mathematical beauty, why do we care about this connection? Here are my reasons for caring:

  • Analysis. Cholesky factorization and the Schur complement are very well-studied in matrix theory. We can use known facts about Cholesky factorization and Schur complements to prove things about the Nyström approximation, as we did when we invoked the positivity of the Schur complement.
  • Algorithms. Cholesky factorization-based algorithms like randomly pivoted Cholesky are effective in practice at producing high-quality column Nyström approximations.

On a broader level, our tale of Nyström, Cholesky, and Schur demonstrates that there are rich connections between low-rank approximation and (partial versions of) classical matrix factorizations like LU (with partial pivoting), Cholesky, QR, eigendecomposition, and SVD for to full-rank matrices. These connections can be vital to analyzing low-rank approximation algorithms and developing improvements.

Low-Rank Approximation Toolbox: Nyström Approximation

Welcome to a new series for this blog, Low-Rank Approximation Toolbox. As I discussed in a previous post, many matrices we encounter in applications are well-approximated by a matrix with a small rank. Efficiently computing low-rank approximations has been a major area of research, with applications in everything from classical problems in computational physics and signal processing to trendy topics like data science. In this series, I want to explore some broadly useful algorithms and theoretical techniques in the field of low-rank approximation.

I want to begin this series by talking about one of the fundamental types of low-rank approximation, the Nyström approximation of a N\times N (real symmetric or complex Hermitian) positive semidefinite (psd) matrix A. Given an arbitrary N\times k “test matrix” \Omega, the Nyström approximation is defined to be

(1)   \[A\langle \Omega\rangle := A\Omega \, (\Omega^*A\Omega)^{-1} \, \Omega^*A. \]

This formula is sensible whenever \Omega^*A\Omega is invertible; if \Omega^*A\Omega is not invertible, then the inverse {}^{-1} should be replaced by the Moore–Penrose pseudoinverse {}^\dagger. For simplicity, I will assume that \Omega^* A \Omega is invertible in this post, though everything we discuss will continue to work if this assumption is dropped. I use {}^* to denote the conjugate transpose of a matrix, which agrees with the ordinary transpose {}^\top for real matrices. I will use the word self-adjoint to refer to a matrix which satisfies A=A^*.

The Nyström approximation (1) answers the question

What is the “best” rank-k approximation to the psd matrx A provided only with the matrix–matrix product A\Omega, where \Omega is a known N\times k matrix (k\ll N)?

Indeed, if we let Y = A\Omega, we observe that the Nyström approximation can be written entirely using Y and \Omega:

    \[A\langle \Omega\rangle = Y \, (\Omega^* Y)^{-1}\, Y^*.\]

This is central advantage of the Nyström approximation: to compute it, the only access to the matrix A I need is the ability to multiply the matrices A and \Omega. In particular, I only need a single pass over the entries of A to compute the Nyström approximation. This allows the Nyström approximation to be used in settings when other low-rank approximations wouldn’t work, such as when A is streamed to me as a sum of matrices that must be processed as they arrive and then discarded.

Choosing the Test Matrix

Every choice of N\times k test matrix \Omega defines a rank-k Nyström approximation1Actually, A\langle \Omega\rangle is only rank at most k. For this post, we will use rank-k to mean “rank at most k“. A\langle \Omega\rangle by (1). Unfortunately, the Nyström approximation won’t be a good low-rank approximation for every choice of \Omega. For an example of what can go wrong, if we pick \Omega to have columns selected from the eigenvectors of A with small eigenvalues, the approximation A\langle \Omega\rangle will be quite poor.

The very best choice of \Omega would be the k eigenvectors associated with the largest eigenvalues. Unfortunately, computing the eigenvectors to high accuracy is computationally costly. Fortunately, we can get decent low-rank approximations out of much simpler \Omega‘s:

  1. Random: Perhaps surprisingly, we get a fairly low-rank approximation out of just choosing \Omega to be a random matrix, say, populated with statistically independent standard normal random entries. Intuitively, a random matrix is likely to have columns with meaningful overlap with the large-eigenvalue eigenvectors of A (and indeed with any k fixed orthonormal vectors). One can also pick more exotic kinds of random matrices, which can have computational benefits.
  2. Random then improve: The more similar the test matrix \Omega is to the large-eigenvalue eigenvectors of A, the better the low-rank approximation will be. Therefore, it makes sense to use the power method (usually called subspace iteration in this context) to improve a random initial test matrix \Omega_{\rm init} to be closer to the eigenvectors of A.2Even better than subspace iteration is block Krylov iteration. See section 11.6 of the following survey for details.
  3. Column selection: If \Omega consists of columns i_1,i_2,\ldots,i_k of the identity matrix, then A\Omega just consists of columns i_1,\ldots,i_k of A: In MATLAB notation,

        \[A(:,\{i_1,\ldots,i_k\}) = A\Omega \quad \text{for}\quad \Omega = I(:,\{i_1,i_2,\ldots,i_k\}).\]

    This is highly appealing as it allows us to approximate the matrix A by only reading a small fraction of its entries (provided k\ll N)! Producing a good low-rank approximation requires selecting the right column indices i_1,\ldots,i_k (usually under the constraint of reading a small number of entries from A). In my research with Yifan Chen, Joel A. Tropp, and Robert J. Webber, I’ve argued that the most well-rounded algorithm for this task is a randomly pivoted partial Cholesky decomposition.

The Projection Formula

Now that we’ve discussed the choice of test matrix, we shall explore the quality of the Nyström approximation as measured by the size of the residual A - A\langle \Omega\rangle. As a first step, we shall show that the residual is psd. This means that A\langle \Omega\rangle is an underapproximation to A.

The positive semidefiniteness of the residual follows from the following projection formula for the Nyström approximation:

    \[A\langle \Omega \rangle = A^{1/2} P_{A^{1/2}\Omega} A^{1/2}.\]

Here, P_{A^{1/2}\Omega} denotes the the orthogonal projection onto the column space of the matrix A^{1/2}\Omega. To deduce the projection formula, we break down A as A = A^{1/2}\cdot A^{1/2} in (1):

    \[A\langle \Omega\rangle = A^{1/2} \left( A^{1/2}\Omega \left[ (A^{1/2}\Omega)^* A^{1/2}\Omega \right]^{-1} (A^{1/2}\Omega)^* \right) A^{1/2}.\]

The fact that the paranthesized quantity is P_{A^{1/2}\Omega} can be verified in a variety of ways, such as by QR factorization.3Let A^{1/2} \Omega = QR, where Q has orthonormal columns and R is square and upper triangular. The orthogonal projection is P_{A^{1/2}\Omega} = QQ^*. The parenthesized expression is (QR)(R^*Q^*QR)^{-1}R^*Q^* = QRR^{-1}R^{-*}R^*Q^* = QQ^* = P_{A^{1/2}\Omega}.

With the projection formula in hand, we easily obtain the following expression for the residual:

    \[A - A\langle \Omega\rangle = A^{1/2} (I - P_{A^{1/2}\Omega}) A^{1/2}.\]

To show that this residual is psd, we make use of the conjugation rule.

Conjugation rule: For a matrix B and a self-adjoint matrix H, if H is psd then B^*HB is psd. If B is invertible, then the converse holds: if B^*HB is psd, then H is psd.

The matrix I - P_{A^{1/2}\Omega} is an orthogonal projection and therefore psd. Thus, by the conjugation rule, the residual of the is Nyström approximation is psd:

    \[A - A\langle \Omega\rangle = \left(A^{1/2}\right)^* (I-P_{A^{1/2}\Omega})A^{1/2} \quad \text{is psd}.\]

Optimality of the Nyström Approximation

There’s a question we’ve been putting off that can’t be deferred any longer:

Is the Nyström approximation actually a good low-rank approximation?

As we discussed earlier, the answer to this question depends on the test matrix \Omega. Different choices for \Omega give different approximation errors. See the following papers for Nyström approximation error bounds with different choices of \Omega. While the Nyström approximation can be better or worse depending on the choice of \Omega, what is true about Nyström approximation for every choice of \Omega is that:

The Nyström approximation A\langle \Omega\rangle is the best possible rank-k approximation to A given the information A\Omega.

In precise terms, I mean the following:

Theorem: Out of all self-adjoint matrices \hat{A} spanned by the columns of A\Omega with a psd residual A - \hat{A}, the Nyström approximation has the smallest error as measured by either the spectral or Frobenius norm (or indeed any unitarily invariant norm, see below).

Let’s break this statement down a bit. This result states that the Nyström approximation is the best approximation \hat{A} to A under three conditions:

  1. \hat{A} is self-adjoint.
  2. \hat{A} is spanned by the columns of A\Omega.

I find these first two requirements to be natural. Since A is self-adjoint, it makes sense to require our approximation \hat{A} to be as well. The stipulation that \hat{A} is spanned by the columns A\Omega seems like a very natural requirement given we want to think of approximations which only use the information A\Omega. Additionally, requirement 2 ensures that \hat{A} has rank at most k, so we are really only considering low-rank approximations to A.

The last requirement is less natural:

  1. The residual A - \hat{A} is psd.

This is not an obvious requirement to impose on our approximation. Indeed, it was a nontrivial calculation using the projection formula to show that the Nyström approximation itself satisfies this requirement! Nevertheless, this third stipulation is required to make the theorem true. The Nyström approximation (1) is the best “underapproximation” to the matrix A to in the span of A\Omega.

Intermezzo: Unitarily Invariant Norms and the Psd Order

To prove our theorem about the optimality of the Nyström approximation, we shall need two ideas from matrix theory: unitarily invariant norms and the psd order. We shall briefly describe each in turn.

A norm \left\|\cdot\right\|_{\rm UI} defined on the set of N\times N matrices is said to be unitarily invariant if the norm of a matrix does not change upon left- or right-multiplication by a unitary matrix:

    \[\left\|UBV\right\|_{\rm UI} = \left\|B\right\|_{\rm UI} \quad \text{for all unitary matrices $U$ and $V$.}\]

Recall that a unitary matrix U (called a real orthogonal matrix if U is real-valued) is one that obeys U^*U = UU^* = I. Unitary matrices preserve the Euclidean lengths of vectors, which makes the class of unitarily invariant norms highly natural. Important examples include the spectral, Frobenius, and nuclear matrix norms:

The unitarily invariant norm of a matrix B depends entirely on its singular values \sigma(B). For instance, the spectral, Frobenius, and nuclear norms take the forms

    \begin{align*}\left\|B\right\|_{\rm op} &= \sigma_1(B),& &\text{(spectral)} \\\left\|B\right\|_{\rm F} &= \sqrt{\sum_{j=1}^N (\sigma_j(B))^2},& &\text{(Frobenius)} \\\left\|B\right\|_{*} &=\sum_{j=1}^N \sigma_j(B)).& &\text{(nuclear)}\end{align*}

In addition to being entirely determined by the singular values, unitarily invariant norms are non-decreasing functions of the singular values: If the jth singular value of B is larger than the jth singular value of C for 1\le j\le N, then \left\|B\right\|_{\rm UI}\ge \left\|C\right\|_{\rm UI} for every unitarily invariant norm \left\|\cdot\right\|_{\rm UI}. For more on unitarily invariant norms, see this short and information-packed blog post from Nick Higham.

Our second ingredient is the psd order (also known as the Loewner order). A self-adjoint matrix A is larger than a self-adjoint matrix H according to the psd order, written A\succeq H, if the difference A-H is psd. As a consequence, A\succeq 0 if and only if A is psd, where 0 here denotes the zero matrix of the same size as A. Using the psd order, the positive semidefiniteness of the Nyström residual can be written as A - A\langle \Omega\rangle \succeq 0.

If A and H are both psd matrices and A is larger than H in the psd order, A\succeq H\succeq 0, it seems natural to expect that A is larger than H in norm. Indeed, this intuitive statement is true, at least when one restricts oneself to unitarily invariant norms.

Psd order and norms. If A\succeq H\succeq 0, then \left\|A\right\|_{\rm UI} \ge \left\|H\right\|_{\rm UI} for every unitarily invariant norm \left\|\cdot\right\|_{\rm UI}.

This fact is a consequence of the following observations:

  • If A\succeq H, then the eigenvalues of A are larger than H in the sense that the jth largest eigenvalue of A is larger than the jth largest eigenvalue of H.
  • The singular values of a psd matrix are its eigenvalues.
  • Unitarily invariant norms are non-decreasing functions of the singular values.

Optimality of the Nyström Approximation: Proof

In this section, we’ll prove our theorem showing the Nyström approximation is the best low-rank approximation satisfying properties 1, 2, and 3. To this end, let \hat{A} be any matrix satisfying properties 1, 2, and 3. Because of properties 1 (self-adjointness) and 2 (spanned by columns of A\Omega), \hat{A} can be written in the form

    \[\hat{A} = A\Omega \, T \, (A\Omega)^* = A \Omega \, T \, \Omega^*A,\]

where T is a self-adjoint matrix. To make this more similar to the projection formula, we can factor A^{1/2} on both sides to obtain

    \[\hat{A} = A^{1/2} (A^{1/2}\Omega\, T\, \Omega^*A^{1/2}) A^{1/2}.\]

To make this more comparable to the projection formula, we can reparametrize by introducing a matrix Q with orthonormal columns with the same column space as A^{1/2}\Omega. Under this parametrization, \hat{A} takes the form

    \[\hat{A} = A^{1/2} \,QMQ^*\, A^{1/2} \quad \text{where} \quad M\text{ is self-adjoint}.\]

The residual of this approximation is

(2)   \[A - \hat{A} = A^{1/2} (I - QMQ^*)A^{1/2}. \]

We now make use of the of conjugation rule again. To simplify things, we make the assumption that A is invertible. (As an exercise, see if you can adapt this argument to the case when this assumption doesn’t hold!) Since A - \hat{A}\succeq 0 is psd (property 3), the conjugation rule tells us that

    \[I - QMQ^*\succeq 0.\]

What does this observation tell us about M? We can apply the conjugation rule again to conclude

    \[Q^*(I - QMQ^*)Q = Q^*Q - (Q^*Q)M(Q^*Q) = I-M \succeq 0.\]

(Notice that Q^*Q = I since Q has orthonormal columns.)

We are now in a place to show that A - \hat{A}\succeq A - A\langle \Omega\rangle. Indeed,

    \begin{align*}A - \hat{A} - (A-A\langle \Omega\rangle) &= A\langle\Omega\rangle - \hat{A} \\&= A^{1/2}\underbrace{QQ^*}_{=P_{A^{1/2}\Omega}}A^{1/2} - A^{1/2}QMQ^*A^{1/2} \\&=A^{1/2}Q(I-M)Q^*A^{1/2}\\&\succeq 0.\end{align*}

The second line is the projection formula together with the observation that P_{A^{1/2\Omega}} = QQ^* and the last line is the conjugation rule combined with the fact that I-M is psd. Thus, having shown

    \[A - \hat{A} \succeq A - A\langle\Omega\rangle \succeq 0,\]

we conclude

    \[\|A - \hat{A}\|_{\rm UI} \ge \left\|A - A\langle \Omega\rangle\right\|_{\rm UI} \quad \text{for every unitarily invariant norm $\left\|\cdot\right\|_{\rm UI}$}.\]