CSE 599: Sparsification, sampling, and optimization

Autumn 2023

MW 11:30am-12:50pm in CSE 2 (Gates) 387

Instructor: James R. Lee

Office hours: By appointment

Course email list [archives]

Reference material:

Course description:

Consider a function \(f : \R^n \to \R_+\) that can be written as a sum of a large number of functions \(f = f_1 + f_2 + \cdots\) from some class. When can this sum be sparsified in the sense that f can be approximated by a (nonnegative) weighted combination of a small number of the \(\{f_i\}\)? For many classes of functions, the answer is that, surprisingly, only about \(O(n)\) summands suffice (up to logarithmic factors). When this is possible, one can address the algorithmic questions: How to efficiently construct the sparsifier, and how to use sparse representations to optimize \(f\) efficiently.

This simple question has a wealth of applications in CS (especially in big data analysis), as well as in many areas of mathematics (especially in functional analysis and convex geometry). The course will focus on the mathematical structures underlying sparsification problems, covering things like: Leverage scores, Lewis weights, generalized linear regression, spectral sparsification of graphs, matrix concentration, generic chaining theory, subspace embeddings and dimension reduction, submodularity, and low-rank approximation in numerical linear algebra.

Lectures [full notes]

$\ell_2$ sparsification $\ell_p$ sparsification Norm sparsification General linear models Active regression
  • Nov 27: Grounded approximations
  • Nov 29: Analysis of $\gamma_2$
Operator-valued sparsification


  • Homework #1 [Wed, Oct 18 @ 11:59pm]
  • Homework #2 [Wed, Nov 8 @ 11:59pm]
  • Homework #3 [Fri, Dec 15 @ 11:59pm]
  • $\ell_2$ sparsification Sparsifying $F(x) = |\langle a_1,x\rangle|^2 + |\langle a_2,x\rangle|^2 + \cdots + |\langle a_m,x\rangle|^2$
    Mon, Oct 02
    Wed, Oct 04

    Importance sampling and leverage scores
    • Least squares linear regression
    • General sparsification setting
    • Examples
    • Importance sampling
    • Leverage scores

        or     Notes as a full page

    Intro to sparsification

    • Linear regression

      Consider the prototypical problem of least-squares linear regression: Suppose we are given input pairs \((a_1,b_1), (a_2,b_2) \ldots, (a_m,b_m)\) with \(a_1,\ldots,a_m \in \R^n\) and \(b_1,\ldots,b_m \in \R\), and we want to find \(x \in \R^n\) such that \(\langle a_i,x\rangle \approx b_i\) for all \(i=1,\ldots,m\).

      Let \(A\) be the \(m \times n\) matrix whose rows are \(a_1,\ldots,a_m\). We will assume that the data is noisy and the system is overdetermined (\(m \gg n\)) so that there is no actual solution to \(Ax=b\).

      In this case, a natural thing to try is to minimize the least-squares error:

      \[\textrm{minimize}\ \left\{ \|Ax-b\|_2^2 : x \in \R^n \right\}.\]

      One might now ask the question of whether we really need to work with all \(m\) examples given that the dimensionality is only \(n \ll m\). Here’s one possible formulation of this problem: Is there a matrix \(\tilde{A}\) with \(\tilde{m} \approx n\) rows and such that the solution to any least-squares regression problem with \((\tilde{A},b)\) is close to the solution with \((A,b)\)?

      Even more ambitiously, we could ask that every row of \(\tilde{A}\) is a weighted scaling of some row of \(A\). Thus \(\tilde{A}\) would correspond to the weighted input \(c_{i_1} (a_{i_1}, b_{i_1}), c_{i_2} (a_{i_2}, b_{i_2}), \ldots, c_{i_M} (a_{i_M}, b_{i_M})\), with \(c_{i_1}, c_{i_2}, \ldots \geq 0\), and the property we’d like to have is that \(\|A x - b\|_2 \approx \|\tilde{A} x - b\|_2\) for every \(x \in \R^n\).

    • Sparsification

      This is a special case of a general type of sparsification problem: Suppose that $F : \R^n \to \R_+$ is given by

      \[\begin{equation}\label{eq:F} F(x) = f_1(x) + f_2(x) + \cdots + f_m(x) \end{equation}\]

      for some functions \(f_1,\ldots,f_m : \R^n \to \R_+\). Consider a function \(\tilde{F} : \R^n \to \R\) given by

      \[\tilde{F}(x) = c_1 f_1(x) + c_2 f_2(x) + \cdots + c_m f_m(x)\]

      for some nonnegative weights \(c_1,c_2,\ldots,c_m \geq 0\). Say that the representation is \(s\)-sparse if at most \(s\) of the weights are nonzero. Say that \(\tilde{F}\) is an \(\e\)-approximation to \(F\) if it holds that

      \[|F(x)-\tilde{F}(x)| \leq \e F(x),\quad \forall x \in \R^n\,.\]
    • General linear models

      Our least-squares regression problem corresponds to the case where \(f_i(x) = \abs{\langle a_i,x\rangle - b_i}^2\) for \(i=1,\ldots,m\).

      • $\ell_p$ loss. But one can imagine many other loss functions. If, instead, we took \(f_i(x) = \abs{\langle a_i,x\rangle - b_i}^p\), the corresponding problem is called $\ell_p$ regression. Jumping ahead:

        • It is known that for \(p=1\), there are \(s\)-sparse \(\e\)-approximations with \(s \leq O(\frac{n}{\e^2} \log n)\). [Tal90]
        • For \(1 < p < 2\), there are \(s\)-sparse \(\e\)-approximations with \(s \leq O(\frac{n}{\e^2} \log n (\log \log n)^2)\). [Tal95]
        • For \(0 < p < 1\), there are \(s\)-sparse \(\e\)-approximations with \(s \leq O(\frac{n}{\e^2} (\log n)^{3})\). [SZ01]
        • For \(p > 2\), it must be that \(s\) grows like \(n^{p/2}\) (and this is tight). [BLM89]
        • Moreover, for \(p = 2\), a remarkable fact is that one can do even better: \(s \leq O(n/\e^2)\). [BSS14]

        In general, one can ask the question: Suppose that \(f_i(x) \seteq \varphi(\langle a_i,x\rangle - b_i)\). For what loss functions \(\varphi : \R \to \R_+\) is such sparsification possible?

      • Huber and \(\gamma_p\) losses. An interesting example is the Huber loss where \(\varphi(y) = \min(y^2, |y|)\). (The Huber loss is more general, but this special case captures its essence.) The loss function grows quadratically near \(0\), but is much less sensitive to outliers than the \(\ell_2\) loss. In general, functionals that grow like \(\varphi(y) = \min(y^2, |y|^p)\) for \(0 < p \leq 2\) have been studied, especially in the context of fast algorithms for solving \(\ell_p\) regression to high accuracy (see, e.g., [BCLL17]).

        The best published work achieves \(s \leq O(n^{4-2\sqrt{2}})\) [MMWY21], but we will see, using methods in this course (from upcoming joint work with Jambulapati, Liu, and Sidford) that \(s \leq O(\frac{n}{\e^2} (\log n)^{O(1)})\) is possible.

        What about the function \(\varphi(y) = \min(y^2, 1)\)? This is sometimes called the Tukey loss, where we know only partial answers to the sparsification question.

      • ReLUs. For an example of a loss function that doesn’t allow sparsification, consider \(\varphi(y) = \max(0, y - 0.5)\). The functional \(f_i(x) = \max(0, \langle a_i,x\rangle - 0.5)\) “lights up” only when \(\langle a_i,x\rangle\) is substantial. But one can easily choose exponentially many unit vectors \(\{a_i\}\) in \(\R^n\) where these functionals have disjoint support, meaning that there is no way to sparsify down to fewer than a number of terms that’s exponential in the dimension.

        If we choose \(f_i(x) = \max(0, |\langle a_i,x\rangle| - 0.5)\), then \(f_1,\ldots,f_m\) are symmetric and convex, so those two conditions alone are not enough to give strong sparsification.

      In general, we don’t yet a complete theory of sparsification even for 1-dimensional functions.

    • Graph sparsification

      • Consider an undirected graph \(G=(V,E,w)\) with nonnegative weights \(w : E \to \R_+\) on the edges. Let us enumerate the edges of \(G\) as \(u_1 v_1, u_2 v_2, \ldots, u_m v_m\), and define \(f_i(x) = w_{u_i v_i} |x_{u_i} - x_{v_i}|\). Then,

        \[F(x) = f_1(x) + \cdots + f_m(x) = \sum_{uv \in E} w_{uv} |x_u-x_v|\,,\]

        and for \(x \in \{0,1\}^V\), the latter sum is precisely the total weight of the edges crossing the cut defined by \(x\).

        Now the question of whether \(F\) admits an \(s\)-sparse \(\e\)-approximation is precisely the question of whether the weighted graph \(G\) admits a cut sparsifier in the sense of Benczur and Karger.

      • If we instead define \(f_i(x) = w_{u_i v_i} |x_{u_i}-x_{v_i}|^2\), then the question becomes one about spectral graph sparsifiers in the sense of Spielman and Teng.

    • High-dimensional sparsification

      • Hypergraph cut sparsifiers. Suppose now that \(H=(V,E,w)\) is a weighted hypergraph in the sense that each hyperedge \(e \in E\) is a subset of vertices. If we define, for \(e \in E\),

        \[f_e(x) \seteq \max_{\{u,v\} \in {e \choose 2}} |x_u-x_v|\,,\]

        and \(F(x) = \sum_{e \in E} f_e(x)\), then the corresponding notion is that of a hypergraph cut sparsifier.

        If we replaced \(\abs{x_u-x_v}\) by \(\abs{x_u-x_v}^2\), we would get the notion of a spectral hypergraph sparsifier.

        See [Lee23] and the references therein.

      • Symmetric submodular functions. More generally, we can ask about the setting where \(f_1,\ldots,f_m : \R^n \to \R_+\) are symmetric submodular functions and

        \[F(x) = f_1(x)^p + f_2(x)^p + \cdots + f_m(x)^p\,.\]

        (More precisely, one should take each \(f_i\) to be the Lovasz extension of a symmetric submodular function with \(f_i(\emptyset) = 0\).) For \(p=1\), this generalizes the setting of hypergraph cut sparsifiers, and one can achieve \(s \leq \frac{n}{\e^2} (\log n)^{O(1)}\).

        For \(p=2\), the best-known bound is \(s \leq \frac{n^{3/2}}{\e^2} (\log n)^{O(1)}\), and it’s unknown whether one can do better.

        See [JLLS23] and the references therein.

      • General norms. A further generalization considers general norms \(N_1,\ldots,N_m\) on \(\R^n\) and whether the norm \(N\) defined by

        \[N(x)^p = N_1(x)^p + N_2(x)^p + \cdots + N_m(x)^p\]

        admits an approximate sparsifier.

        See [JLLS23] and the references therein.

      • General submodular functions. For more general (non-symmetric) submodular functions, there are many classes of functions that allow varying degrees of sparsification. See, e.g., [KK23].

    Unbiased estimators and importance sampling

    • Unbiased estimators. A general method to construct sparsifiers is by independent random sampling. Suppose we are in the setting \eqref{eq:F} and let \(\rho = (\rho_1,\ldots,\rho_m)\) be a probability vector, i.e., a nonnegative vector with \(\rho_1 + \cdots + \rho_m = 1\).

      Let us sample indices \(i_1,i_2,\ldots,i_M\) i.i.d. from \(\rho\) and define the random sparsifier \(\tilde{F}\) by

      \[\tilde{F}(x) \seteq \frac{1}{M} \left(\frac{f_{i_1}(x)}{\rho_{i_1}} + \frac{f_{i_2}(x)}{\rho_{i_2}} + \cdots + \frac{f_{i_M}(x)}{\rho_{i_M}}\right).\]

      Note that, because we have rescaled by the probabilities, it holds that for every \(x \in \R^n\),

      \[\E\left[\frac{f_{i_1}(x)}{\rho_{i_1}}\right] = \sum_{i=1}^m \rho_i \frac{f_i(x)}{\rho_i} = F(x)\,,\]

      and therefore \(\E[\tilde{F}(x)] = F(x)\) as well.

    • Importance. As we just argued, \(\tilde{F}(x)\) is an unbiased estimator for any \(\rho\), but crucially in order to choose \(M\) small and still hope that \(\tilde{F}\) will be a good approximation to \(F\), we need to choose \(\rho\) carefully.

      A simple example comes from the \(\ell_2\) regression problem. Let us assume that \(b=0\) and consider \(a_1,\ldots,a_m \in \R^n\) such that \(a_1\) is linearly independent from \(a_2,\ldots,a_m\). Then \(f_1(x) = |\langle a_1,x\rangle|^2\) must appear in a sparse approximation to \(F\) to handle the input \(x = a_1\). Therefore we will need to sample at least \(O(1/\rho_1)\) terms before we expect to have \(f_1\) appear. If we are looking to have \(M \leq n (\log n)^{O(1)}\), it’s clear that choosing \(\rho\) to be uniform, for instance, will be drastically insufficient.

    • Concentration. In general, once we choose \(\rho\), we are left with the non-trivial problem of getting \(F(x) \approx \tilde{F}(x)\) for every \(x \in \R^n\). This can be a daunting task that is often framed as bounding an expected maximum:

      \[\E \sup_{x \in \R^n} \frac{|F(x)-\tilde{F}(x)|}{|F(x)|} \leq \e\,.\]

      If we focus on the case where \(F\) is sufficiently homogeneous (like the \(\ell_2\) regression problem), this simplifies a bit to

      \[\E \sup_{F(x) \leq 1} |F(x) - \tilde{F}(x)| \leq \e\,,\]

      but the fundamental difficulty remains.

    \(\ell_2\) regression and statistical leverage scores

    Considering \(F(x) = f_1(x) + \cdots + f_m(x)\), one way to choose an importance for each \(f_i\) is to obtain a uniform bound on each term. In other words, we consider the quantity

    \[\alpha_i \seteq \max_{0 \neq x \in \R^n} \frac{f_i(x)}{F(x)}\,.\]

    If we assume that \(F\) and \(f_i\) scale homogeneously (e.g., in the case of \(\ell_2\) regression, \(F(\lambda x) = |\lambda|^2 F(x)\) and similarly for each \(f_i\)), then we can write this as

    \[\begin{equation}\label{eq:uniform-con} \alpha_i = \max_{F(x) \leq 1} f_i(x)\,. \end{equation}\]

    Let’s try to derive good sampling probabilities for the \(\ell_2\) regression problem where

    \[F(x) = |\langle a_1,x\rangle|^2 + |\langle a_2,x\rangle|^2 + \cdots + |\langle a_m,x\rangle|^2\,,\]

    for \(a_1,\ldots,a_m \in \R^n\), and we define \(A\) as the matrix with \(a_1,\ldots,a_m\) as rows. We’ll derive our importance scores in a few different ways. Each of these perspectives will be useful for moving later to more general settings.

    • Uniform control.

      Consideration of \eqref{eq:uniform-con} in this setting is easy:

      \[\alpha_i = \max_{\|A x\|^2 \leq 1} |\langle a_i,x\rangle|^2\,.\]

      Observe that \(\|A x\|^2 = \langle Ax, Ax\rangle = \langle (A^{\top} A)^{1/2} x, (A^{\top} A)^{1/2} x\rangle\), so

      \[\begin{align*} \alpha_i &= \max_{\|x\|^2 \leq 1} |\langle a_i, (A^{\top} A)^{-1/2} x\rangle|^2 \\ &= \max_{\|x\|^2 \leq 1} |\langle (A^{\top} A)^{-1/2} a_i, x\rangle|^2 \\ &= \|(A^{\top} A)^{-1/2} a_i\|_2^2\,, \end{align*}\]

      where in the last line we took \(x = (A^{\top} A)^{-1/2} a_i/\|(A^{\top} A)^{-1/2} a_i\|_2\) as the maximizer. The value

      \[\sigma_i(A) \seteq \|(A^{\top} A)^{-1/2} a_i\|_2^2 = \langle a_i, (A^{\top} A)^{-1} a_i\rangle\]

      is called the \(i\)th leverage score of \(A\).

      Note that

      \[\sum_{i=1}^m \sigma_i(A) = \sum_{i=1}^m \langle a_i, (A^{\top} A)^{-1} a_i\rangle = \tr\left((A^{\top} A)^{-1} \sum_{i=1}^m a_i a_i^{\top}\right) = \mathrm{rank}(A)\,,\]

      therefore our corresponding sample probabilities would be \(\rho_i \seteq \sigma_i(A)/\mathrm{rank}(A)\). We will assume in the rest of this lecture that \(\mathrm{rank}(A) = n\), but this is only for convenience.

    • Symmetry considerations.

      We have already seen that if \(a_1\) is linearly independent from the rest of the vectors, then we should sample it with a pretty large probability (considering the case where \(a_1,\ldots,a_n\) forms an orthonormal basis, “large” will have to mean probability \(\approx 1/n\)). Another indication comes from how \(F\) scales: If we replace \(a_1\) by two copies of \(a_1/\sqrt{2}\), then the function \(F\) doesn’t change. This suggests that \(\rho_i\) should scale proportional to \(\|a_i\|_2^2\).

      But notice also that \(F\) is a sum of squares; as there are no cancellations, it is natural to consider the matrix \(a_1 a_1^{\top} + \cdots + a_m a_m^{\top} = A^{\top} A\). Let’s suppose, for simplicity, that the vectors form a decomposition of the identity:

      \[\begin{equation}\label{eq:isotropic} a_1 a_1^{\top} + \cdots + a_m a_m^{\top} = I\,. \end{equation}\]

      Then we have \(\|a_1\|_2^2 + \cdots + \|a_m\|_2^2 = \tr\left(a_1 a_1^{\top} + \cdots + a_m a_m^{\top}\right) = \tr(I) = n\), so symmetry suggests we take \(\rho_i \seteq \|a_i\|_2^2/n\).

      For the general case, let us assume that \(A\) has full rank, i.e., \(\mathrm{rank}(A) = n\). We can left-multiply the vectors by \((A^{\top} A)^{-1/2}\) to achieve \eqref{eq:isotropic}, again suggesting the probabilities \(\rho_i = \sigma_i(A)/n\).

      We can think about leverage scores in a couple of other ways.

    • Expected contribution to \(F(x)\).

      One way to judge the importance of \(a_i\) is to switch from a “worst case” perspective (approximating \(F(x)\) for every \(x\)) to an ``average case’’ one: Suppose that \(\mathbf{X}\) is a random vector in \(\R^n\) and define

      \[\rho_i \propto \E \left[|\langle a_i, \mathbf{X}\rangle|^2\right].\]

      What distribution should we use? In the case that \eqref{eq:isotropic} holds, it makes sense for \(\mathbf{X}\) to be rotationally invariant, and indeed any such choice works. Using the Gaussian measure on $\R^n$, this would give

      \[\rho_i \propto \int |\langle a_i,x\rangle|^2 e^{-\|x\|_2^2/2}\,dx\,,\]

      and it is straightforward to check that \(\rho_i \propto \|a_i\|_2^2\). And if we apply the transformation \((A^{\top} A)^{-1/2}\) to a general set of vectors to achieve \eqref{eq:isotropic}, then the corresponding form is

      \[\rho_i \propto \int |\langle a_i,x\rangle|^2 e^{-\|(A^{\top} A)^{1/2} x\|_2^2/2}\,dx = \int |\langle a_i,x\rangle|^2 e^{-\|A x\|_2^2/2}\,dx\,,\]

      and again one has \(\rho_i \propto \|(A^{\top}A)^{-1/2} a_i\|_2^2\).

    • Singular value decomposition.

      Suppose we write \(A = U \Sigma V^{\top}\). Recall that we consider \(n \leq m\), thus \(\Sigma = \begin{bmatrix} D \\ \hline 0\end{bmatrix}\), where \(D\) is an \(n \times n\) diagonal matrix containing the singular values \(\sigma_1,\ldots,\sigma_n\). Note that \(U\) is an \(m \times m\) matrix, but \(\Sigma\) makes all but the first \(n\) columns of \(U\) meaningless (indeed, one can obtain an SVD from any completion of the first \(n\) columns of \(U\) to an orthonormal basis).

      If we consider the “compact SVD” \(A = \hat{U} \Sigma V^{\top}\) where we cut the last \(m-n\) columns off of \(U\), then \(\sigma_i(A) = \|\hat{u}_i\|_2^2\), where \(\hat{u}_1,\ldots,\hat{u}_m\) are the rows of \(\hat{U}\).


    Once we’ve chosen our sampling probabilities \(\rho_i = \sigma_i(A)/n\), we can independently sample \(M\) terms from the distribution, and we are left to analyze the random sum

    \[\tilde{A} = \frac{1}{M}\left(\frac{a_{i_1} a_{i_1}^{\top}}{\rho_{i_1}} + \cdots + \frac{a_{i_M} a_{i_M}^{\top}}{\rho_{i_M}}\right).\]

    We have already argued that \(\E[\tilde{A}] = A\), so the real question is about concentration, which we will begin to cover in the next lecture.

    There are actually two ways to approach this question. One is to think about proving concentration of the sum along every direction \(x \in \R^n\) simultaneously. This leads naturally to entropy bounds, covering numbers, and the generic chaining theory. The second approach is to think about \(\tilde{A}\) as a random operator and try to prove concentration of sums of independent random matrices. The former will be far more general, while the latter appears substantially more powerful in certain special cases.

    Mon, Oct 09
    Symmetrization and subgaussian processes
    • Symmetrization
    • Subgaussian processes

        or     Notes as a full page

    Symmetrization, take I

    Recall that we have a function \(F : \R^n \to \R\) written as \(F(x) = f_1(x) + \cdots + f_m(x)\) and we want to sparisfy it by sampling the terms in proportion to a probability vector \(\rho \in \R_+^m\) (the “importance scores”).

    One way to analyze this is to first make sure all the probabilities are uniformly small, say \(\rho_1,\ldots,\rho_m \leq 2/m\). Since we are trying to sparsify down to a number of terms that is independent of \(m\), we can always achieve this by simply splitting a term

    \[f_i(x) \to f_i(x)/s + f_i(x)/s + \cdots + f_i(x)/s\,,\]

    where there are \(s\) summands.

    Then one can sparsify in phases by deleting every term independently with probability \(1/2\). This experiment can be modeled with \(\{0,1\}\) random variables in the natural way:

    \[\E_{\e} \max_{x \in \Omega} \left|F(x) - \sum_{j=1}^m (1+\e_j) f_j(x)\right| = \E_{\e} \max_{x \in \Omega} \left|\sum_{j=1}^m \e_j f_j(x)\right|.\]

    where \(\e_1,\ldots\e_m\) are i.i.d. uniform random $\pm 1$ variables, and \(\Omega \subseteq \R^n\) is our domain of interest.

    It turns out that it is possible to do something similar and reduce the analysis of one-shot sampling to the analysis of randomly signed sums of the functions. This makes for both a simpler algorithm, and a more robust method of analysis that will be very useful later.

    Symmetrization, take II

    Let’s consider a more general situation. Consider a family of independent random functions \(\mathbf{g}_1,\ldots,\mathbf{g}_M : \R^n \to \R\) that are sampled from some distribution. (Recall that we are thinking about each one as being chosen from a finite family of functions and, in our case, it even holds that they are i.i.d.).

    Define \(\mathbf{G}(x) \seteq \mathbf{g}_1(x) + \cdots + \mathbf{g}_m(x)\) and \(F(x) \seteq \E[\mathbf{G}(x)]\). We are interested in the quantity

    \[\begin{equation}\label{eq:Esup} \mathcal{S} \seteq \E \max_{x \in \Omega} \left|F(x)-\mathbf{G}(x)\right| = \E \max_{x \in \Omega} \left|F(x) - \left(\mathbf{g}_1(x)+\cdots+\mathbf{g}_M(x)\right)\right|\,, \end{equation}\]

    where \(\Omega \subseteq \R^n\).

    For our particular case of interest, let us take \(\Omega \seteq \{ x \in \R^n : F(x) \leq 1 \}\).

    • Lemma: Suppose it holds that for every choice \(g_1,\ldots,g_M\) of functions from the support of \(\mathbf{g}_1,\ldots,\mathbf{g}_M\), we have

      \[\begin{equation}\label{eq:rad} \E_{\mathbf{\e}} \max_{x \in \Omega} \left|\sum_{j=1}^M \e_j g_j(x)\right| \leq \delta \left(\max_{x \in \Omega} G(x)\right)^{1/2}, \end{equation}\]

      where \(\delta < 1\). Then we have \(\mathcal{S} \lesssim \delta\).

    • Proof: Note that if \(\tilde{\mathbf{g}}_1, \ldots, \tilde{\mathbf{g}}_M\) is an independent family of functions from the same distribution, then clearly \(F(x) = \E\left[\tilde{\mathbf{g}}_1(x) + \cdots + \tilde{\mathbf{g}}_M(x)\right]\) as well, so we can write

      \[\begin{align*} \E \max_{x \in \Omega} & \left|F(x) - \left(\mathbf{g}_1(x)+\cdots+\mathbf{g}_M(x)\right)\right| \\ &= \E \max_{x \in \Omega} \left|\E \left[\tilde{\mathbf{g}}_1(x)+\cdots+\tilde{\mathbf{g}}_M(x)\right] - \left(\mathbf{g}_1(x)+\cdots+\mathbf{g}_M(x)\right)\right| \\ &\leq \E \max_{x \in \Omega} \left|\left(\tilde{\mathbf{g}}_1(x)+\cdots+\tilde{\mathbf{g}}_M(x)\right) - \left(\mathbf{g}_1(x)+\cdots+\mathbf{g}_M(x)\right)\right|, \end{align*}\]

      where we have pulled out the expectation using convexity of the absolute value and the maximum.

      Write the latter quantity as

      \[\begin{align*} \E \max_{x \in \Omega} \left|\left(\tilde{\mathbf{g}}_1(x)-\mathbf{g}_1(x)\right)+\cdots+\left(\tilde{\mathbf{g}}_M(x) - \mathbf{g}_M(x)\right)\right|, \end{align*}\]

      and note that since \(\mathbf{g}_i(x) - \tilde{\mathbf{g}}_i(x)\) and \(\tilde{\mathbf{g}}_i(x)-\mathbf{g}_i(x)\) have the same distribution, we can write this as

      \[\begin{align*} \E \max_{x \in \Omega} \left|\e_1 \left(\tilde{\mathbf{g}}_1(x)-\mathbf{g}_1(x)\right)+\cdots+\e_M \left(\tilde{\mathbf{g}}_M(x) - \mathbf{g}_M(x)\right)\right|, \end{align*}\]

      for any fixed choice of signs \(\e_1,\ldots,\e_M \in \{-1,1\}\).

      Now let \(\mathbf{\e}_1,\ldots,\mathbf{\e}_M\) be a uniformly random choice of signs and the latter expression equals

      \[\begin{align*} \E \E_{\mathbf{\e}} \max_{x \in \Omega} \left|\e_1 \left(\tilde{\mathbf{g}}_1(x)-\mathbf{g}_1(x)\right)+\cdots+\e_M \left(\tilde{\mathbf{g}}_M(x) - \mathbf{g}_M(x)\right)\right|, \end{align*}\]

      where we use \(\E_{\e}\) denote expectation over only the random signs.

      Now we ignore the outer expectation for a moment and use the triangle inequality to write

      \[\begin{align*} \E_{\mathbf{\e}} &\max_{x \in \Omega} \left|\e_1 \left(\tilde{g}_1(x)-{g}_1(x)\right)+\cdots+\e_M \left(\tilde{g}_M(x) - {g}_M(x)\right)\right| \\ &\leq \E_{\mathbf{\e}} \max_{x \in \Omega} \left|\e_1 {g}_1(x) + \cdots + \e_M {g}_M(x)\right| + \E_{\mathbf{\e}} \max_{x \in \Omega} \left|\e_1 \tilde{g}_1(x) + \cdots + \e_M \tilde{g}_M(x)\right| \\ &\leq 2\delta \left(\max_{x \in \Omega} G(x)\right)^{1/2}\,, \end{align*}\]

      where the last inequality uses the assumption \eqref{eq:rad}.

      Taking expectations, we have therefore proved that

      \[\mathcal{S} \leq 2 \delta \E\left[\left(\max_{x \in \Omega} \mathbf{G}(x)\right)^{1/2}\right].\]

      Since \(F(x) \leq 1\) for \(x \in \Omega\), we have

      \[\max_{x \in \Omega} G(x) \leq 1 + \max_{x \in \Omega} \left|F(x) - G(x)\right|\]

      Given the above discussion and recalling the definition in \eqref{eq:Esup}, if \eqref{eq:rad} holds for every choice of \(g_1,\ldots,g_M\), this yields the bound

      \[\begin{align*} \mathcal{S} \leq \delta\ \E \left(1 + \max_{x \in \Omega} \left|F(x)-\mathbf{G}(x)\right|\right)^{1/2} \leq \delta \left(1+\mathcal{S}\right)^{1/2}\,, \end{align*}\]

      where the last inequality uses concavity of the square root to move the expectation inside. For \(\delta < 1\), this tells us that \(\mathcal{S} \leq O(\delta)\).

    This completes the symmetrization argument, allowing us to focus on proving bounds like \eqref{eq:rad}.

    The advantage here lies in the the fact that \(\left \{ \e_1 g_1(x) + \e_2 g_2(x) + \cdots + \e_M g_M(x) : x \in \R^n \right\}\) is an example of a subgaussian process, and bounding the expected maximum of a subgaussian process has a long and rich history, along with a correspondingly powerful framework.

    The (mild) disadvantage is that we require the inequality \eqref{eq:rad} to hold for every choice of functions \(g_1,\ldots,g_M\) in the support of our distribution.

    (Note: The confusing terminology “process” is a historical artifact. Originally, one was indeed interested in \(\e_1 g_1(x)\), \(\e_1 g_1(x) + \e_2 g_2(x)\), etc. as a sequence “evolving in time.” If one switches from discrete to continuous time, e.g., a process like Brownian motion, then control of the expected maximum is closely related to almost sure continuity of the sample paths.)

    Subgaussian processes

    Consider a collection of random variables \(\{ X_t : t \in T \}\) where the index set \(T\) is equipped with a distance \(d\). The family is called subgaussian (with respect to \(d\)) if there if there is a constant \(c > 0\) such that

    \[\begin{equation}\label{eq:subgaussian} \P\left[|X_s-X_t| > \lambda\,d(s,t)\right] \leq e^{- c\lambda^2}\,,\quad \forall \lambda > 0, s,t \in T\,. \end{equation}\]
    • Gaussian processes. The canonical example occurs when \(\{X_t : t \in T\}\) is a jointly Gaussian family of random variables, in which case \eqref{eq:subgaussian} holds with \(c=1/2\) and \(d(s,t) = (\E |X_s-X_t|^2)^{1/2}\).

      Note that if the index set \(T\) is finite, then such a family can always be realized in the following way: Let \(g_1,g_2,\ldots,g_m\) be i.i.d. \(N(0,1)\) random variables and define \(X_t = t_1 g_1 + t_2 g_2 + \cdots + t_m g_m\) for \(t \in T \subseteq \R^n\).

    • Bernoulli processes. Suppose that \(\e_1,\ldots,\e_m\) are i.i.d uniform \(\pm 1\) random variables and for \(t \in T\), we define \(X_t \seteq \e_1 t_1 + \e_2 t_2 + \cdots + \e_m t_m\). Then the \(\{ X_t : t \in \R^n \}\) is a family of subgaussian random variables with the same parameters: \(c=1/2\) and \(d(s,t) = (\E |X_s-X_t|^2)^{1/2}\).

      Many proofs are known. Even proofs of significant generalizations can be made quite short. See, e.g., the proof of Theorem 1.3 here.

    The quantity we care about after our symmetrization argument is

    \[\begin{equation}\label{eq:symout} \E \max_{x \in \Omega} \left(\e_1 g_1(x) + \cdots + \e_M g_M(x)\right), \end{equation}\]

    and as we have just seen this is the expected maximum of a centered subgaussian process. (Centered means that \(\E X_t = 0\) for every \(t \in T\).) There is a rich history and theory for bounding such expected maxima, with M. Talagrand as the primary architect (see his comprehensive book on the topic).

    Wed, Oct 11
    Entropy numbers and Dudley's bound
    • Union bounds
    • Chaining
    • Dudley's entropy inequality

        or     Notes as a full page

    Dudley’s entropy inequality

    • Entropy numbers

      Suppose now that \(\{ X_t : t \in T \}\) is subgaussian with respect to the distance \(d\). Our goal is to get some control on \(\E \max_{t \in T} X_t\), and one of the central tenants of the theory is that we can do this using just the geometry of the distance \(d\). Let’s suppose that \(d\) is symmetric \(d(s,t)=d(t,s)\) for all \(s,t \in T\), and that it satisfies the triangle inequality:

      \[d(s,t) \leq d(s,u) + d(u,t)\,,\qquad \forall u,s,t \in T\,.\]

      Define the quantity \(e_h(T,d)\) as the smallest radius \(r\) such that \((T,d)\) can be covered by at most \(2^{2^h}\) balls of radius \(r\), where a ball in \((T,d)\) is given by

      \[B(t, r) \seteq \{ s \in Y : d(s,t) \leq r \}\,.\]

      It follows by a greedy construction that there exists a “net” \(N_h \subseteq T\) such that \(|N_h| \leq 2^{2^h}\) and every point of \(T\) is within distance at most \(e_h(T,d)\) of a point in \(N_h\).

      It is useful to think of \(N_h\) as the “best” uniform approximation of \((T,d)\) when one is only allowed to use \(2^{2^h}\) points. Let’s fix any such sets \(\{N_h : h=0,1,2,\ldots\}\).

      Let \(\pi_h : T \to N_h\) be a map that satisfies

      \[\begin{equation}\label{eq:pih} d(t, \pi_h(t)) \leq e_h(T,d)\,\qquad \forall t \in T\,. \end{equation}\]

      For instance, a natural choice is for \(\pi_h(t)\) to map \(t\) to the closest point of \(N_h\), but we will only need \eqref{eq:pih}.

    • A multilevel union bound

      Now define

      \[U_h \seteq \{ (t, t') : t \in N_{h}, t' \in N_{h+1}, d(t,t') \leq 2 e_h(T,d) \}\,.\]

      Think of these as the “edges” from points in \(N_{h+1}\) to nearby points in \(N_h\). Let us denote the event

      \[\mathcal{E}_h(\lambda) \seteq \left\{|X_t - X_{t'}| \leq 2 \lambda e_h(T,d),\ \forall (t,t') \in U_h \right\}.\]

      If we think about \(U_h\) as containing “edges” that gets stretched to random lengths, then this event entails no edge of \(U_h\) being stretched too much. Since every pair in \(U_h\) is within distance \(2 e_h(T,d)\), a union bound combined with \eqref{eq:subgaussian} gives

      \[\begin{align*} \P\left(\mathcal{E}_h(\lambda) \right) &\geq 1 - |U_h| e^{-c \lambda^2} \\ &\geq 1 - |N_h| |N_{h+1}| e^{-c \lambda^2} \\ &\geq 1 - 2^{2^{h+2}} e^{-c \lambda^2}\,, \end{align*}\]

      where we have used the obvious bound \(\abs{U_h} \leq \abs{N_h} \abs{N_{h+1}}\) and the fact that \(\abs{N_h} \abs{N_{h+1}} \leq 2^{2^h} 2^{2^{h+1}}\).

      A natural choice is now \(\lambda \seteq 4 \frac{\alpha}{c} 2^{h/2}\) for some number \(\alpha > 0\) we will choose later. If we define \(\mathcal{E}_h \seteq \mathcal{E}_h((\alpha/c) 2^{(h+2)/2})\), then the preceding bound gives us

      \[\begin{equation}\label{eq:Ehtail} \P\left(\mathcal{E}_h\right) \geq 1 - 2^{2^{h+2}} e^{-2 \alpha 2^{h+2}} \geq 1 - e^{-\alpha 2^{h}}\,. \end{equation}\]

      Note that the leading constant is not so important, we just wanted a clean form for the lower bound. The real point here is that \(\lambda\) is scaling like \(2^{h/2}\), and the probability of \(\mathcal{E}_h\) not occurring can be summed over all \(h \geq 0\), since it’s doubly geometric.

      If we now simply take a union bound over \(h=0,1,2,\ldots,\), we get

      \[\begin{equation}\label{eq:Ehall} \Pr\left(\mathcal{E}_0 \wedge \mathcal{E}_1 \wedge \mathcal{E}_2 \wedge \cdots \right) \geq 1 - \sum_{h \geq 0} e^{-\alpha 2^h} \geq 1 - 2 e^{-\alpha}\,. \end{equation}\]
    • The chaining argument

      We’ve gone through all this effort because it turns out that if \(\mathcal{E}_h\) holds for every \(h \geq 0\), it gives us a bound on \(\E \max_{t \in T} (X_t - X_{t_0})\), where \(\{t_0\} = N_0\) (note that \(N_0\) could be any fixed point of \(T\)). The argument goes via chaining. Consider first the infinite telescoping sum

      \[X_t - X_{t_0} = \cdots + \left(X_{\pi_{h+2}(t)} - X_{\pi_{h+1}(t)}\right) + \left(X_{\pi_{h+1}(t)} - X_{\pi_h(t)}\right) + \cdots + \left(X_{\pi_1(t)} - X_{\pi_0(t)}\right)\]

      If we think of this as a chain, then every link in the chain is going to lie in some set \(U_h\), and thus having control on the stretch of all the “edges” we considered previously will give us control on every term in this chain. You can assume the sum is finite if you like, because

      \[\E \max_{t \in T} (X_t - X_{t_0}) = \max_{S \subseteq T : |S| < \infty} \E\max_{t \in S} (X_t - X_{t_0})\,,\]

      Now, note that \(\pi_h(t) \in N_h\) and \(\pi_{h+1}(t) \in N_{h+1}\), and by the triangle inequality

      \[d(\pi_h(t), \pi_{h+1}(t)) \leq d(\pi_h(t), t) + d(\pi_{h+1}(t), t) \leq e_h(T,d) + e_{h+1}(T,d) \leq 2 e_{h}(T,d)\,.\]

      In other words, \((\pi_h(t), \pi_{h+1}(t)) \in U_h\), and therefore

      \[\mathcal{E}_h \implies |X_{\pi_{h+1}(t)} - X_{\pi_h(t)}| \leq 8 \frac{\alpha}{c} 2^{h/2} e_h(T,d)\,.\]

      And therefore the telescoping representation of \(X_t - X_{t_0}\) gives us

      \[\mathcal{E}_0 \wedge \mathcal{E}_1 \wedge \mathcal{E}_2 \wedge \cdots \implies |X_t - X_{t_0}| \leq 8 \frac{\alpha}{c} \sum_{h \geq 0} 2^{h/2} e_h(T,d)\,.\]

      Combining this with \eqref{eq:Ehall} gives

      \[\P\left(|X_t - X_{t_0}| \leq 8 \frac{\alpha}{c} \sum_{h \geq 0} 2^{h/2} e_h(T,d),\quad \forall t \in T\right) \geq 1 - 2 e^{-\alpha}\,.\]

      By integrating over \(\alpha\), one immediately concludes that

      \[\E \max_{t \in T} X_t \leq \E \max_{t \in T} |X_t - X_{t_0}| \lesssim \frac{1}{c} \sum_{h \geq 0} 2^{h/2} e_h(T,d)\,.\]

      This is known as Dudley’s entropy bound.

    Mon, Oct 16
    Wed, Oct 18

    Slepian's lemma and dual-Sudakov bounds
    • Entropy numbers and covering numbers
    • Slepian's Shift Lemma
    • Dual-Sudakov bounds for covering numbers

        or     Notes as a full page

    Sparsification and entropy numbers

    Suppose that \(F(x) = f_1(x) + \cdots + f_m(x)\), \(\rho \in \R_+^m\) is a probability vector, and define our potential sparsifier as

    \[\tilde{F}_{\nu}(x) \seteq \sum_{j=1}^M \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}}\,.\]

    Define the related distance

    \[d_{\nu}(x,y) \seteq \left(\sum_{j=1}^M \left(\frac{f_{\nu_j}(x)-f_{\nu_j}(y)}{M \rho_{\nu_j}}\right)^2 \right)^{1/2}.\]

    We have seen that if we can bound

    \[\begin{equation}\label{eq:delta-bound} \E_{\e} \max_{F(x) \leq 1} \sum_{j=1}^m \e_j \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}} \leq \delta \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}\quad \forall \nu \in [m]^M\,, \end{equation}\]

    where \(\e_1,\ldots,\e_M \in \{-1,1\}\) are uniformly random signs, then

    \[\begin{equation}\label{eq:Fapprox} \E_{\nu} \max_{F(x) \leq 1} \left|F(x)-\tilde{F}_{\nu}(x)\right| \lesssim \delta\,, \end{equation}\]

    where \(\nu_1,\ldots,\nu_M\) are indicies sampled i.i.d. from \(\rho\).

    Finally, we have seen, using Dudley’s entropy bound, that if \(B_F \seteq \{ x \in \R^n : F(x) \leq 1 \}\), then

    \[\begin{equation}\label{eq:dudley} \E_{\e} \max_{F(x) \leq 1} \sum_{j=1}^m \e_j \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}} \lesssim \sum_{h \geq 0} 2^{h/2} e_h(B_F, d_{\nu})\,. \end{equation}\]

    \(\ell_2\) regression

    Recall the setting for \(\ell_2\) regression: \(a_1,\ldots,a_m \in \R^n\) and \(f_i(x) \seteq \abs{\langle a_i,x\rangle}^2\).

    • Control by \(\ell_{\infty}\). In this case, for \(F(x),F(y) \leq 1\), we can write

      \[\begin{align*} d_{\nu}(x,y) &= \left(\sum_{j=1}^M \left(\frac{|\langle a_{\nu_j},x\rangle|^2 - |\langle a_{\nu_j},y\rangle|^2}{M \rho_{\nu_j}}\right)^2\right)^{1/2} \\ &= \left(\sum_{j=1}^M \frac{\left(|\langle a_{\nu_j},x\rangle| - |\langle a_{\nu_j},y\rangle|\right)^2\left(|\langle a_{\nu_j},x\rangle| + |\langle a_{\nu_j},y\rangle|\right)^2}{M \rho_{\nu_j}}\right)^{1/2} \\ &\leq 2 \max_{j \in [M]} \frac{\left||\langle a_{\nu_j},x\rangle| - |\langle a_{\nu_j},y\rangle|\right|}{\sqrt{M \rho_{\nu_j}}} \left(\max_{F(x) \leq 1} \sum_{j=1}^M \frac{|\langle a_{\nu_j},x\rangle|^2}{M \rho_{\nu_j}}\right)^{1/2} \\ &\leq 2 \max_{j \in [M]} \frac{|\langle a_{\nu_j},x-y\rangle|}{\sqrt{M \rho_{\nu_j}}} \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}. \end{align*}\]

      If we have two distances \(d_1\) and \(d_2\) such that \(d_1 \leq C d_2\), then \(e_h(B_F, d_1) \leq C e_h(B_F, d_2)\). Thus if we define

      \[d_{\nu,\infty}(x,y) \seteq \max_{j \in [M]} \frac{|\langle a_{\nu_j},x-y\rangle|}{\sqrt{M \rho_{\nu_j}}}\,,\]

      then we have

      \[\begin{equation}\label{eq:nucomp} e_h(B_F, d_{\nu}) \leq e_h(B_F, d_{\nu,\infty}) \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}, \end{equation}\]

      which goes nicely with our goal of bounding \eqref{eq:delta-bound} via \eqref{eq:dudley}.

    • Bounding the entropy numbers.

      For a metric space \((T,d)\), define \(\mathcal{N}(T,d,\e)\) as the minimal number of \(\e\)-balls in \((T,d)\) needed to cover \(T\). It will help to check one’s understanding of the definitions to verify that if

      \[\log \mathcal{N}(T,d,\e) \leq \left(\frac{L}{\e}\right)^p\,, \quad \forall \e > 0\,,\]

      then \(e_h(T,d) \leq 2^{-h/p} L\) for every \(h \geq 0\).

      • Covering Lemma: Suppose that \(u_1,\ldots,u_M \in \R^n\), let \(U\) be the matrix with \(u_1,\ldots,u_M\) as rows, and define

        \[d_{U}(x,y) \seteq \|U(x-y)\|_{\infty} = \max_{j \in [M]} |\langle u_j,x-y\rangle|\,.\]


        \[\left(\log \mathcal{N}(B_2^n, d_{U}, \e)\right)^{1/2} \lesssim \frac{1}{\e} \max(\|u_1\|_2,\ldots,\|u_M\|_2) \sqrt{\log M}\,,\]

        where \(B_2^n\) is the unit ball in the Euclidean norm.

    • Sparsifiers for \(\ell_2\) regression.

      Let us first use the Covering Lemma to finish our first construction of sparsifiers for \(\ell_2\) regression.

      Recall that we choose \(\rho_{i} \seteq \|(A^{\top} A)^{-1/2} a_i\|_2^2/n\), where \(A\) is the matrix with rows \(a_1,\ldots,a_m\) and, in this case, \(F(x) = \|Ax\|_2^2\).

      Using the linear transformation \(x \mapsto (A^{\top} A)^{-1/2} x\), we can write

      \[\mathcal{N}(B_F, d_{\nu,\infty}, \e) = \mathcal{N}(B_2^n, d_{U}, \e)\,,\]

      where \(U\) is the matrix with rows \(u_j \seteq \frac{(A^{\top} A)^{-1/2} a_{\nu_j}}{\sqrt{M \rho_{\nu_j}}}\). Note that, by our choice of \(\rho_1,\ldots,\rho_M\), we have

      \[\|u_j\|_2 = \frac{\|(A^{\top} A)^{-1/2} a_{\nu_j}\|_2}{\sqrt{M \rho_{\nu_j}}} = \sqrt{\frac{n}{M}} \frac{(A^{\top} A)^{-1/2} a_{\nu_j}}{\|(A^{\top} A)^{-1/2} a_{\nu_j}\|_2} = \sqrt{\frac{n}{M}}\,.\]
      • Terms with \(h \lesssim \log n\). So the Covering Lemma gives us

        \[\left(\log \mathcal{N}(B_F, d_{\nu,\infty}, \e)\right)^{1/2} \lesssim \frac{1}{\e} \sqrt{n \frac{\log M}M}.\]

        By our preceding remarks, this yields

        \[\begin{equation}\label{eq:ent1} e_h(B_F, d_{\nu,\infty}) \lesssim 2^{-h/2} \sqrt{n \frac{\log M}{M}} \end{equation}\]
      • Terms with \(h \gg \log n\). If we denote \(B_{\nu,\infty} \seteq \{ x \in \R^n : d_{\nu,\infty}(x,0) \leq 1 \}\), then we have

        \[B_F \subseteq \mathrm{diam}(B_F, d_{\nu,\infty}) B_{\nu,\infty}\,.\]

        A simple calculation gives us

        \[d_{\nu,\infty}(x,y) \leq \|A (x-y)\|_{\infty} \leq \sqrt{\frac{n}{M}} \|A (x-y)\|_2\,,\]

        therefore \(\mathrm{diam}(B_F, d_{\nu,\infty}) \leq \sqrt{n/M}\), therefore

        \[\begin{equation}\label{eq:ent2} e_h(B_F, d_{\nu,\infty}) \leq \sqrt{\frac{n}{M}} e_h(B_{\nu,\infty}, d_{\nu,\infty}) \leq \sqrt{\frac{n}{M}} 2^{-2^h/n}\,, \end{equation}\]

        where the last inequality holds for the unit ball of any norm on \(\R^n\) (and, indeed, \(d_{\nu,\infty}\) is a distance induced by a norm on \(\R^n\)), as you show in Homework #1.

      • Putting everything together. Now \eqref{eq:ent1} and \eqref{eq:ent2} in conjunction give

        \[\sum_{h \geq 0} 2^{h/2} e_h(B_F, d_{\nu,\infty}) \lesssim \log(n) \sqrt{n \frac{\log M}{M}} + \sum_{h > 4 \log_2 n} 2^{-2^h/n} \sqrt{\frac{n}{M}} \lesssim \sqrt{n (\log n)^2 \frac{\log M}{M}}\,.\]

        Combining this with \eqref{eq:nucomp} and \eqref{eq:dudley} gives

        \[\E_{\e} \max_{F(x) \leq 1} \sum_{j=1}^m \e_j \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}} \lesssim \sqrt{n (\log n)^2 \frac{\log M}{M}} \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}.\]

        Therefore by setting \(M \seteq C \frac{n}{\e^2} (\log n)^2 \log(n/\e)\) for an appropriate constant \(C > 1\), \eqref{eq:Fapprox} gives us our desired \(\e\)-approximation.

    The covering lemma

    Before proving the lemma, it helps to consider the more basic problem of covering the Euclidean ball \(B_2^n\) by translates of \(\e B_{\infty}^n\), i.e., by translates of small cubes.

    Suppose \(\|x\|_2^2 = x_1^2 + x_2^2 + \cdots + x_n^2 = 1\). Since we only care about approximation up to \(\e\) in the \(\ell_{\infty}\) distance, we could discretize this vector to lie in, say, \(\e \mathbb{Z}^n \cap B_2^n\).

    The most basic kind of vector we need to cover is of the form \((0, \pm \e, 0, 0, \pm \e, 0, \pm \e, 0, \ldots, 0)\). Because \(\|x\|_2^2 = 1\), there are only \(n^{O(1/\e^2)}\) choices for such a vector. But we also need to handle vectors of the form \((0, \pm 2\e, 0, 0, \pm \e, 0, \pm 2\e, 0, \ldots, 0)\), and so on.

    It is not hard to convince one’s self that there are asymptotically fewer vectors of this form. Ineed, if some entry is \(2\e\) then there are \(n\) choices for where it goes, but there are \(n(n-1)/2\) choics for where two copies of \(\e\) go. Thus the total number of centers one needs is only \(n^{O(1/\e^2)}\).

    In other words,

    \[\left(\log \mathcal{N}(B_2^n, \|\cdot\|_{\infty}, \e)\right)^{1/2} \lesssim \frac{1}{\e} \sqrt{\log n}\,.\]

    Now suppose we wanted to cover \(B_2^n\) instead with cubes of different side lengths, or with parallelpipeds (where the sides are no longer perpendicular), etc. There is a beautiful approach that gives surprisingly good bounds for cover \(B_2^n\) by translations of an arbitrary symmetric convex body. (I have heard it credited to Talagrand, or to Pajor and Talagrand.)

    • The dual-Sudakov approach.

      We will prove the following substantial strengthening of this bound. Suppose that \(d(x,y) = \|x-y\|\) is a distance induced by some norm $\norm{\cdot}$ on \(\R^n\). Then for every \(\e > 0\),

      \[\begin{equation}\label{eq:dual-sudakov} \left(\log \mathcal{N}(B_2^n, d, \e)\right)^{1/2} \lesssim \frac{1}{\e} \E \|\mathbf{g}\|\,, \end{equation}\]

      where \(\mathbf{g}\) is a standard \(n\)-dimensional gaussian.

      Note that this proves the Covering Lemma since

      \[\E \|U \mathbf{g}\|_{\infty} = \E \max_{j \in [M]} |\langle u_j, \mathbf{g}\rangle| \lesssim \max(\|u_1\|_2,\ldots,\|u_M\|_2) \sqrt{\log M}\,.\]

      The last bound is a basic exercise with the gaussian tail, taking a union bound over the \(M\) gaussian variables \(\{ \langle u_j,\mathbf{g} \rangle : j \in [M] \}\).

      • Shift Lemma: Suppose that \(\gamma_n\) is the standard gaussian measure on \(\R^n\) and \(W \subseteq \R^n\) is any symmetric set (\(W = -W\)) and \(z \in \R^n\). Then,

        \[\gamma_n(W+z) \geq e^{-\|z\|_2^2} \gamma_n(W)\,.\]
      • Proof: Write

        \[\begin{align*} \gamma_n(W+z) &\propto \int_W e^{-\|x+z\|_2^2/2}\,dx \\ &= \E_{\sigma \in \{-1,1\}} \int_W e^{-\|\sigma x + z\|_2^2}\,dx \\ &\geq \int_W e^{-\E_{\sigma \in \{-1,1\}} \|\sigma x + z\|_2^2}\,dx\,, \end{align*}\]

        where the first equality uses symmetry of \(W\) and the second uses convexity of \(e^{-y}\).

        Now note that from Pythagoras, \(\E_{\sigma \in \{-1,1\}} \|\sigma x + z\|_2^2 = \|x\|_2^2 + \|z\|_2^2\), therefore

        \[\int_W e^{-\E_{\sigma \in \{-1,1\}} \|\sigma x + z\|_2^2}\,dx = e^{-\|z\|_2^2} \int_W e^{-\|x\|_2/2} \propto e^{-\|z\|_2^2} \gamma_n(W)\,.\]
    • Proof of \eqref{eq:dual-sudakov}:

      Let \(B \seteq \{ x \in \R^n : \|x\| \leq 1 \}\) denote the unit ball in our given norm. By scaling, we can assume that \(\e = 2\).

      If we cannot cover \(B_2^n\) by \(N\) translates of \(2 B\), then there must exist \(x_1,\ldots,x_N \in B_2^n\) such that \(x_1 + B, \ldots, x_N + B\) are all pairwise disjoint. Let \(\lambda > 0\) be a parameter we will choose momentarily, and note that \(\lambda (x_1+B),\ldots, \lambda(x_N+B)\) are pairwise disjoint as well, therefore

      \[\begin{align*} 1 \geq \gamma_n\left(\bigcup_{i \leq N} \lambda (x_i+B)\right) &= \sum_{i \leq N} \gamma_n\left(\lambda (x_i+B)\right) \\ &\geq \gamma_n(\lambda B) \sum_{i \leq N} e^{-\lambda^2 \|x_i\|_2^2/2} \\ &\geq \gamma_n(\lambda B) N e^{-\lambda^2/2}\,, \end{align*}\]

      where the first inequality uses the Shift Lemma and the second inequality uses \(x_1,\ldots,x_N \in B_2^n\).

      Now note that, by Markov’s inequality, \(\E \|\mathbf{g}\| \geq \lambda \gamma_n(\R^n \setminus (\lambda B))\), thus taking \(\lambda \seteq 2 \E \|\mathbf{g}\|\) gives \(\gamma_n(\lambda B) \geq 1/2\), and we conclude that

      \[N \leq 2 e^{-2 (\E \|\mathbf{g}\|)^2}\,,\]

      completing the proof.

    $\ell_p$ sparsification Sparsifying $F(x) = |\langle a_1,x\rangle|^p + |\langle a_2,x\rangle|^p + \cdots + |\langle a_m,x\rangle|^p$, $1 < p < 2$.
    Mon, Oct 23
    Wed, Oct 25
    Mon, Oct 30

    $\ell_p$ sparsification for $1 < p <2$ and uniform smoothness
    • Uniform smoothness and the Shift Lemma revisited
    • Dual-Sudakov for uniformly smooth norms
    • Exponential concentration of 1D marginals of log-concave distributions

        or     Notes as a full page

    Fix \(1 \leq p \leq 2\). We will now prove that if \(a_1,\ldots,a_m \in \R^n\) and \(f_i(x) = |\langle a_i,x\rangle|^p\), then the sum \(F(x) = f_1(x) + \cdots + f_m(x)\) admits \(s\)-sparse \(\e\)-approximations for

    $$ s \lesssim \frac{n}{\e^2} \left(\log \frac{n}{\e}\right)^{O(1)}, $$

    generalizing what we saw last lecture for \(p=2\).

    As we have already seen, this will follow from an estimate of the form

    $$\begin{equation}\label{eq:lp_ent_goal} e_h(B_F, d_{\nu}) \lesssim 2^{-h/2} \sqrt{\frac{n}{M}} \left(\log(M) \log(n)\right)^{O(1)} \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}\,, \end{equation} $$


    $$\begin{align*} B_F &= \{ x \in \R^n : \|Ax\|_p \leq 1 \} \\ \tilde{F}_{\nu}(x) &= \sum_{j=1}^M \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}} \\ d_{\nu}(x,y) &= \left(\sum_{j=1}^M \left(\frac{f_{\nu_j}(x)-f_{\nu_j}(y)}{M \rho_{\nu_j}}\right)^2\right)^{1/2}\,, \end{align*} $$

    for some choice of sampling probabilities \((\rho_1,\ldots,\rho_m) \in \R_+^m\).

    Reduction to the consideration of \(d_{\nu,\infty}\)

    • Let’s introduce a distance analogous to our argument for \(p=2\):

      \[d_{\nu,\infty}(x,y) \seteq \max_{j \in [M]} \frac{|\langle a_{\nu_j}, x-y\rangle|}{(M \rho_{\nu_j})^{1/p}}\]
    • Using the elementary inequality, valid for \(1 \leq p \leq 2\),

      \[|a^p - b^p|^2 \leq 2 |a-b|^p \left(a^p + b^p\right)\,,\quad \forall a,b \geq 0\,,\]

      it holds that

      \[d_{\nu}(x,y) \lesssim d_{\nu,\infty}(x,y)^{p/2} \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}.\]
    • Thus we need to prove a bound of the form

      \[e_h(B_F, d_{\nu,\infty}^{p/2}) \lesssim 2^{-h/2} L\,,\quad h \geq 0\,.\]

      As we discussed last time, this is equivalent to the bound

      \[\begin{equation}\label{eq:lp_cov_goal} \left(\log \mathcal{N}(B_F, d_{\nu,\infty}, \e^{2/p})\right)^{1/2} \lesssim \frac{L}{\e}\,\quad \e > 0\,. \end{equation}\]

    $\ell_p$ importances from averages

    Consider \(A : \R^n \to \R^m\) with rows \(a_1,\ldots,a_m \in \R^n\), and let us define importance scores

    $$\begin{equation}\label{eq:lp_scores} \rho_i \seteq \frac{\int e^{-\norm{Ax}_p^p} \abs{\langle a_i,x\rangle}^p\,dx}{\int e^{-\norm{Ax}_p^p} \norm{Ax}_p^p\,dx}\,. \end{equation} $$
    • Lemma: For any norm \(N\) on \(\R^n\) and \(1 \leq p \leq 2\), it holds that

      \[\int e^{-N(x)^p} N(x)^p \,dx = \frac{n}{p} \int e^{-N(x)^p}\,dx\,.\]
    • Proof:

      Let \(B_N\) denote the unit ball of \(N\). Define \(V(r) \seteq \mathrm{vol}_n(r B_N) = r^n \mathrm{vol}_n(B_N)\) so that \(\frac{d}{dr} V(r) = n r^{n-1} \mathrm{vol}_n(B_N)\). Therefore,

      \[\begin{align*} \int_{\R^n} N(x)^p\,d\mu(x) &= \frac{\int_{\R^n} N(x)^p e^{-N(x)^p}\,dx}{\int_{\R^n} e^{-N(x)^p}\,dx} \\ &= \frac{\int_{0}^{\infty} r^p e^{-r^p} d V(r)}{\int_{0}^{\infty} e^{-r^p} d V(r)} = \frac{\int_{0}^{\infty} e^{-r^p} r^{n-1+p}\,dr}{\int_{0}^{\infty} e^{-r^p} r^{n-1}\,dr}\,. \end{align*}\]

      Make the subsitution $u = r^p$, yielding

      \[\int_{\R^n} N(x)^p e^{-N(x)^p}\,dx = \frac{\int_{0}^{\infty} e^{-u} u^{n/p}\,du}{\int_{0}^{\infty} e^{-u} u^{n/p-1}\,du} = \frac{n}{p}\,,\]

      where the latter equality follows from integration by parts.

    • Let \(\mathbf{Z}\) be a random variable with law \(\mu\), where \(\mu\) has density proportional to \(e^{-\norm{Ax}_p^p}\). Then the lemma tells us that

      \[\begin{equation}\label{eq:rho-alt} \rho_i = \frac{p}{n} \E \left[\abs{\langle a_i,\mathbf{Z}\rangle}^p\right]\,, \quad i=1,\ldots,m\,. \end{equation}\]
    • Check that in the case \(p=2\), it holds that \(\rho_i = \norm{(A^{\top} A)^{-1/2} a_i}_2^2/n\), i.e., the corresponding weights are precisely the statistical leverage scores.

    The shift lemma and uniform smoothness

    In light of our choice of important scores in \eqref{eq:lp_scores}, we can think of the following setting. Let \(U\) be a matrix with rows \(u_1,\ldots,u_M \in \R^n\). Let \(\mathbf{Z}\) be a random variable whose distribution has density proportional to \(e^{-\|A x\|_p^p}\), and assume that

    $$\begin{equation}\label{eq:pth-moments} \left(\E\left[\abs{\langle u_j, \mathbf{Z}\rangle}^p\right]\right)^{1/p} \leq K\,,\quad j=1,\ldots,M\,. \end{equation} $$

    Note that if \(u_j = a_{\nu_j}/(M \rho_{\nu_j})^{1/p}\), then \(K \leq (\frac{n}{p M})^{1/p}\) using \eqref{eq:rho-alt}.

    Our goal now is to bound the covering numbers of the set \(B_A \seteq \{x \in \R^n : \|A x\|_p \leq 1 \}\) in the metric \(d_{U}(x,y) \seteq \|U(x-y)\|_{\infty}\). Recall that we did this in the last lecture in the case \(p=2\) using the Shift Lemma for the gaussian measure. There we used the inequality

    $$ \frac{\|x + z\|_2^2 +\|x-z\|_2^2}{2} \leq \|x\|_2^2 + \|z\|_2^2\,, $$

    which actually holds with equality (by the Pythagorean theorem) for the Euclidean norm. We will need a generalization of this property.

    • Uniform smoothness.

      • A norm $N$ on $\R^n$ is said to be $p$-uniformly smooth with constant $S$ if it holds that

        \[\begin{equation}\label{eq:p-smooth} \frac{N(x+y)^p + N(x-y)^p}{2} \leq N(x)^p + N(S y)^p,\qquad x,y \in \R^n\,. \end{equation}\]
      • For \(1 \leq p \leq 2\), the \(\ell_p\) norm is \(p\)-uniformly smooth with constant \(1\). This reduces to the \(1\)-dimensional inequality (due to Clarkson):

        \[\begin{equation}\label{eq:clarkson} \frac{|a+b|^p + |a-b|^p}{2} \leq |a|^p + |b|^p\,,\quad \forall a,b \in \R\,. \end{equation}\]
        • Proof: Write
        \[\begin{align*} \frac{|a+b|^p + |a-b|^p}{2} &= \frac{((a+b)^2)^{p/2} + ((a-b)^2)^{p/2}}{2} \\ &\leq \left(\frac{(a+b)^2 + (a-b)^2}{2}\right)^{p/2} \\ &=\left(a^2+b^2\right)^{p/2} \leq |a|^p + |b|^p\,, \end{align*}\]

        where the first inequality is concavity of \(y \mapsto y^{q}\) for \(0 \leq q \leq 1\), and the second inequalitiy uses $(u+v)^q \leq u^q + v^q$ for \(0 \leq q \leq 1\) and \(u,v \geq 0\).

      • Uniform smoothness is a quantative measure of how much the unit ball of a norm lacks “corners.” Consider the modulus of smoothness

        \[\rho_N(t) \seteq \sup \left\{ \frac{N(x+y) + N(x-y)}{2} : N(x)=1, N(y) = t\right\}\]

        Then \(N\) is \(p\)-uniformly smooth if \(\rho_N(t) \leq O(t^p)\) for all \(t > 0\).

        To get some intuition for this notion, see the figure to the right. The blue arrow corresponds to \(x\) and the red arrows correspond to \(\pm y\). One of the directions is bad for the \(\ell_1\) ball, and the other for the \(\ell_{\infty}\) ball.

        Note how following the red arrows takes one far outside the \(\ell_1\) (and \(\ell_{\infty}\)) ball, but almost stays inside the \(\ell_2\) ball. It turns out that \(\ell_p\) is \(p\)-uniformly smooth with constant \(1\) for \(1 \leq p \leq 2\), and \(2\)-uniformly smooth with constant \(O(\sqrt{p})\) for \(p > 2\).

    • Shift Lemma for \(p\)-uniformly smooth norms:

      Suppose $N$ is a norm on $\R^n$ that is $p$-uniformly smooth with constant $S$. Let \(\mu\) be the probability measure on \(\R^n\) with density proportional to \(e^{-N(x)^p}\). Then for any symmetric convex body \(W \subseteq \R^n\) and \(z\in \R^n\),

      \[\begin{equation}\label{eq:shift-measure} \mu(W+z) \geq e^{- S^p N (z)^p}\mu(W)\,. \end{equation}\]
      • Proof:

        For any $z \in \R^n$, it holds that

        \[\mu({W}+z) = \frac{\int_{W} \exp(-N(x+z)^p)\,dx}{\int_{W} \exp(-N(x)^p)\,dx} \mu({W})\,.\]

        Now we bound

        \[\begin{align*} \int_{W} \exp(-N(x+z)^p)\,dx &= \int_{W} \E_{\sigma \in \{-1,1\}} \exp(-N(\sigma x+z)^p)\,dx \\ &\geq \int_{W} \exp\left(-\E_{\sigma \in \{-1,1\}} N(\sigma x+z)^p\right)\,dx \\ &\geq \int_{W} \exp\left(-(N(x)^p + S^p N(z)^p)\right)\,dx \\ &= \exp(-S^p N(z)^p) \int_{W} \exp(-N(x)^p)\,dx\,, \end{align*}\]

        where the equality uses symmetry of ${W}$, the first inequality uses convexity of $e^y$, and the second inequality uses $p$-uniform smoothness of $N$ (recall \eqref{eq:p-smooth}).

    • Dual-Sudakov Lemma for \(p\)-smooth norms.

      Let \(N\) and \(\hat{N}\) be norms on \(\R^n\). Suppose that $N$ is $p$-uniformly smooth with constant $S$, and let \(\mu\) be the probability measure on $\R^n$ with density propertional to \(e^{-N(x)^p}\). Then for any $\e > 0$,

      \[\left(\log \mathcal{N}(B_{N}, \hat{N}, \e)\right)^{1/p} \lesssim \frac{S}{\e} \int \hat{N}(x)\,d\mu(x)\,.\]
      • Proof: By scaling $\hat{N}$, we may assume that $\e = 1$. Suppose now that $x_1,\ldots,x_M \in B_{N}$ and $x_1+B_{\hat{N}},\ldots,x_M+B_{\hat{N}}$ are pairwise disjoint. To establish an upper bound on $M$, let $\lambda > 0$ be a number we will choose later and write

        \[\begin{align*} 1 \geq \mu\left(\bigcup_{j=1}^M \lambda (x_j + B_{\hat{N}})\right) &= \sum_{j=1}^M \mu\left(\lambda x_j + \lambda B_{\hat{N}}\right) \\ &\geq \sum_{j=1}^M e^{-\lambda^p S^p N(x_j)^p} \mu(\lambda B_{\hat{N}}) \geq M e^{-S^p \lambda^p} \mu(\lambda B_{\hat{N}})\,, \end{align*}\]

        where \eqref{eq:shift-measure} used the Shift Lemma and the last inequality used $x_1,\ldots,x_M \in B_N$.

        Now choose $\lambda \seteq 2 \int \hat{N}(x)\, d\mu(x)$ so that Markov’s inequality gives

        \[\mu(\lambda B_{\hat{N}}) = \mu\left(\{x : \hat{N}(x) \leq \lambda \} \right) \geq 1/2\,.\]

        Combining with the preceding inequality yields the upper bound

        \[\left(\log (M/2)\right)^{1/p} \leq S \lambda\,.\]
    • Control on the covering numbers. Using the dual-Sudakov Lemma, we have

      \[\left(\log \mathcal{N}(B_A, d_U, \e)\right)^{1/p} \lesssim \frac{1}{\e} \E \|U \mathbf{Z}\|_{\infty} = \frac{1}{\e} \max_{j \in [M]} \abs{\langle u_j, \mathbf{Z}\rangle}\,.\]

      Since it holds that \(\E[\abs{\langle u_j,\mathbf{Z}\rangle}] \leq \left(\E \abs{\langle u_j,\mathbf{Z}\rangle}^p\right)^{1/p} \leq L^{1/p}\) by \eqref{eq:pth-moments}, we are left with the final piece, a concentration estimate for the random variables \(\{ \langle u_j, \mathbf{Z} \rangle \}\).

    \(\psi_1\) concentration for one-dimensional marginals of log-concave measures

    • Exponential concentration. Let’s first summarize the final piece: The function \(e^{-\|Ax\|_p^p}\) is log-concave, meaning that the random variable \(\mathbf{Z}\) is log-concave. You will prove in HW#2 that the \(1\)-dimensional marginals of such a random variable always satisfy an exponential concentration inequality: For some constant \(c > 0\),

      \[\P\left[\abs{\langle u_j, \mathbf{Z}\rangle} \geq t \E[\abs{\langle u_j, \mathbf{Z}\rangle} \right] \leq e^{-c t}\,,\quad t \geq 1\,.\]
    • Finishing the argument. In particular, this implies that

      \[\E \max_{j \in [M]} \abs{\langle u_j, \mathbf{Z}\rangle} \lesssim \left(\max_{j \in [M]} \E \abs{\langle u_j, \mathbf{Z}\rangle}\right) \log M\,,\]

      which gives

      \[\left(\log \mathcal{N}(B_A, d_U, \e)\right)^{1/p} \lesssim \frac{1}{\e} K \log M\,.\]

      Now for

      \[u_j \seteq \frac{a_{\nu_j}}{M^{1/p} \rho_{\nu_j}^{1/p}}\,,\]

      we satisfy this with \(K \leq (n/M)^{1/p}\). Recalling \eqref{eq:lp_cov_goal}, let us write this as

      \[\left(\log \mathcal{N}(B_A, d_U, \e^{2/p})\right)^{1/2} \lesssim \frac{1}{\e} \sqrt{\frac{n (\log M)^p}{M}}\,.\]

      Translating all the way back to our initial \eqref{eq:lp_ent_goal} gives

      \[e_h(B_F, d_{\nu}) \lesssim 2^{-h/2} \sqrt{\frac{n (\log M)^p}{M}} \left(\max_{F(x) \leq 1} \tilde{F}_{\nu}(x)\right)^{1/2}\,,\]

      Now applying Dudley’s entropy bound as in the previous lecture gives

      \[\E_{\e} \max_{F(x) \leq 1} \mag{\sum_{j=1}^M \e_j \frac{f_{\nu_j}(x)}{M \rho_{\nu_j}}} \lesssim \sqrt{\frac{n (\log M)^p}{M}} \log n\,.\]

      And using our symmetrization reduction, we should take \(M \asymp \frac{n}{\e^2} (\log (n/\e))^p (\log n)^2\) to obtain an \(\e\)-approximation with precisely \(M\) terms.

    Wed, Nov 01
    $\ell_p$ Lewis weights
    • Variational formulation
    • Properties of Lewis weights
    • Iterative algorithm for $p < 4$

        or     Notes as a full page

    A variational formulation

    • Fix $p \geqslant 1$ and vectors $a_1,\ldots,a_m \in \R^n$. Consider the optimization problem, over all $n \times n$ matrices,

      \[\begin{equation}\label{eq:lewis-opt} \textrm{maximize} \quad \left\{ \det(U) : \|U a_1\|_2^p + \cdots + \|U a_m\|_2^p = n \right \}. \end{equation}\]
    • Claim: Suppose that $U_0$ is a maximizer of \eqref{eq:lewis-opt}. Then, \((U_0^{\top} U_0)^{-1} = A^{\top} W A\,,\) where $W$ is a nonnegative diagonal matrix with $W_{ii} = \norm{U_0 a_i}_2^{p-2}$ for $i=1,\ldots,m$.

    • Proof: The method of Langrange multipliers tells us that there is a nonnegative $\lambda > 0$ such that the maximum is given by a critical point of the functional

      \[G(U) \mathrel{\mathop:}=\det(U) + \lambda \left(n - \sum_{i=1}^m \|U a_i\|_2^p\right).\]

      Assuming that $\mathrm{span}(a_1,\ldots,a_m) = \R^n$, it is straightforward to check that the optimization has a finite, non-zero maximum, and therefore any maximizer $U_0$ is invertible.

      Let us compute the gradient of $G(U)$ with respect to the matrix entries ${ U_{ij} : 1 \leqslant i,j \leqslant n }$. The rank-one update rule for the determinant says that for invertible $U$,

      \[\det(U + u v^{\top}) = \det(U) (1+\langle v, U^{-1} u\rangle)\,,\]

      This follows from $U + uv^{\top} = U (I + U^{-1} uv^{\top})$, $\det(AB)=\det(A)\det(B)$, and $\det(I + uv^{\top}) = 1 + \langle u,v\rangle$.


      \[\partial_{U_{ij}} \det(U) = \left.\frac{d}{dt}\right|_{t=0} \det(U + t e_i e_j^{\top}) = \det(U) \langle e_j, U^{-1} e_i\rangle = \det(U) U^{-1}_{ji}\,.\]

      (From this one can see the more general formula: $\mathsf{d}\det(U) = \det(U) \mathop{\mathrm{tr}}(U^{-1} \mathsf{d} U)$.)

      So, denoting the inverse transpose by $U^{-\top}$, we have

      \[\nabla_U \det(U) = \det(U) U^{-\top}\,.\]

      Let’s also compute

      \[\mathsf{d} \norm{U a_k}_2^p = p \norm{U a_k}_2^{p-1} \frac{\mathsf{d} \!\left(\|Ua_k\|_2^{2}\right)}{2 \|U a_k\|_2}\,,\]

      Now, observe that $\norm{U a_k}_2^2 = \mathop{\mathrm{tr}}\left(U^{\top} U a_k a_k^{\top}\right)$, thus using linearity of the gradient and the trace,

      \[\begin{aligned} \mathsf{d} \left(\sum_{k=1}^m \|U a_k\|_2^{p}\right) &= \mathop{\mathrm{tr}}\left(\mathsf{d} \left(U^{\top} U\right) \sum_{k=1}^m \left(p \|U a_k\|_2^{p-2}\right) a_k a_k^{\top}\right). \end{aligned}\]

      Now a simple application of the product rule gives

      \[\mathsf{d} (U^{\top} U) = (\mathsf{d} U^{\top}) U + U^{\top} (\mathsf{d} U) = (\mathsf{d} U)^{\top} U + U^{\top} \mathsf{d} U\,.\]

      Plugging this in above and using $\mathop{\mathrm{tr}}(M)=\mathop{\mathrm{tr}}(M^{\top})$ gives

      \[\mathsf{d} \left(\sum_{k=1}^m \|U a_k\|_2^{p}\right) = \mathop{\mathrm{tr}}\left((\mathsf{d} U)^{\top} U A^{\top} D_U A\right),\]

      where $D_U$ is the diagonal matrix with $(D_U)_{kk} = p \norm{U a_k}_2^{p-2}$.


      \[\nabla_U \left(\sum_{k=1}^m \norm{U a_k}_2^{p}\right) = U A^{\top} D_U A\,,\]

      which implies

      \[\nabla_U G(U) = \det(U) U^{-\top} - \lambda U A^{\top} D_U A\,.\]

      So if $U_0$ is a critical point of $G$, we have

      \[\det(U_0) U_0^{-\top} = \lambda U_0 A^{\top} D_{U_0} A\,,\]

      or equivalently \((U_0^{\top} U_0)^{-1} = \lambda_0 A^{\top} D_{U_0} A\,,\) with $\lambda_0 \mathrel{\mathop:}=\lambda/\det(U_0)$.

      To compute the value of $\lambda_0$, we can use $A^{\top} D_{U_0} A = \sum_{i=1}^m (D_{U_0})_{ii} a_i a_i^{\top}$ to calculate

      \[\begin{aligned} n = \mathop{\mathrm{tr}}\left((U_0^{\top} U_0)^{-1} (U_0^{\top} U_0)\right) &= \lambda_0 \sum_{i=1}^m \mathop{\mathrm{tr}}\left((D_{U_0})_{ii} (U_0^{\top} U_0) a_i a_i^{\top}\right) \\ &= \lambda_0 p \sum_{i=1}^m (D_{U_0})_{ii} \|U_0 a_i\|_2^2 \\ &= \lambda_0 p \sum_{i=1}^m \|U_0 a_i\|_2^p = \lambda_0 p n\,,\end{aligned}\]

      therefore $\lambda_0 = 1/p$.

    Properties of Lewis weights

    • Let $U_0$ be a maximizer of \eqref{eq:lewis-opt} and define $U \mathrel{\mathop:}=(U_0^{\top} U_0)^{1/2}$. Then,

      \[U^{-1} = (A^{\top} W A)^{1/2}\,,\]

      with $W_{ii} = \norm{U a_i}_2^{p-2}$ for $i=1,\ldots,m$, and $\norm{U a_1}_2^p + \cdots + \norm{U a_m}_2^p = n$.

      Let us define $w_i \mathrel{\mathop:}=W_{ii}$ for convenience.

    • First, note the consequence of Cauchy-Schwarz:

      \[\begin{equation}\label{eq:lewis-cs} |\langle a_i,x\rangle| \leqslant\|U a_i\|_2 \|U^{-1} x\|_2\,.\end{equation}\]
    • Fix $1 \leqslant p \leqslant 2$ and note the formula

      \[\begin{equation}\label{eq:lewis-exp} \|U^{-1} x\|_2^2 = \langle x, (A^{\top} W A) x\rangle = \sum_{i=1}^m w_i \langle a_i, x\rangle^2 =\sum_{i=1}^m w_i |\langle a_i, x\rangle|^p |\langle a_i,x\rangle|^{2-p}\,.\end{equation}\]

      Now use \eqref{eq:lewis-cs} to arrive at

      \[\begin{aligned} \|U^{-1} x\|_2^2 &\leqslant\sum_{i=1}^m |\langle a_i,x\rangle|^p w_i \|U a_i\|_2^{2-p} \|U^{-1} x\|_2^{2-p} \\ &= \|U^{-1} x\|_2^{2-p} \sum_{i=1}^m |\langle a_i,x\rangle|^p \\ &= \|U^{-1} x\|_2^{2-p} \|Ax\|_p^p\,.\end{aligned}\]

      Simplifying yields

      \[\begin{equation}\label{eq:l2-lp} \|U^{-1} x\|^p_2 \leqslant\|Ax\|_p^p\,.\end{equation}\]
    • Consulting \eqref{eq:lewis-cs} again, this yields

      \[|\langle a_i,x\rangle|^p \leqslant\|U a_i\|_2^p \|Ax\|_p^p\,.\]

      In particular, we get a bound on the sensitivity of the $i$th term:

      \[\begin{equation}\label{eq:lewis-sens} s_i \mathrel{\mathop:}=\max_{x \neq 0} \frac{|\langle a_i,x\rangle|^p}{\|Ax\|_p^p} \leqslant\|U a_i\|_2^p\,,\end{equation}\]

      and $s_1+\cdots+s_m \leqslant n (\norm{U a_1}_2^p + \cdots + \norm{U a_m}_2^p) = n$.

      Thus Lewis weights give us sampling probabilities:

      \[\rho_i \mathrel{\mathop:}=\frac{\|U a_i\|_2^p}{n}\,,\quad i=1,\ldots,m\,.\]

    Entropy bounds

    • As before, the analysis of concentration for importance sampling comes down to analyzing covering numbers of the set \(B_{A,p} \seteq \{ x \in \R^n : \|Ax\|_p \leqslant 1 \}\) in the metric

      \[d_{\rho,\infty}(x,y) \mathrel{\mathop:}=\max_{i=1,\ldots,m} \frac{|\langle a_i,x-y\rangle|}{\rho_i^{1/p}} = \max_{i=1,\ldots,m} \frac{|\langle a_i,x-y\rangle|}{\|U a_i\|_2}\,.\]

      The quantity we need to bound in this case is

      \[\begin{equation}\label{eq:ldb} \left(\log \mathcal{N}(B_{A,p}, d_{\rho,\infty}, r)\right)^{1/p} \lesssim \frac{L n^{1/2}}{r}\,, \end{equation}\]

      where $L$ should be $(\log m)^{O(1)}$.

      This metric is clearly inducd by the (semi-)norm

      \[\|x\|_{\rho,\infty} \mathrel{\mathop:}=\max_{i=1,\ldots,m} \frac{|\langle a_i,x\rangle|}{\|U a_i\|_2} = \max_{i=1,\ldots,m} \frac{|\langle U a_i, U^{-1} x\rangle|}{\|U a_i\|_2}\,.\]

      From \eqref{eq:l2-lp}, we see that the Euclidean ball \(B_{\rho,2} \seteq \{ x \in \R^n : \norm{U^{-1} x}_2 \leq 1 \}\) satisfies $B_{A,p} \subseteq B_{\rho,2}$.

    • Denote the unit ball \(B_{\rho,\infty} \seteq \{ x \in \R^n : \norm{x}_{\rho,\infty} \leqslant 1 \}\). Then we can bound

      \[\mathcal{N}(B_{A,p}, r B_{\rho,\infty}) \leqslant\mathcal{N}(B_{\rho,2}, r B_{\rho,\infty})\,.\]

      If we apply the linear transformation $U^{-1}$ that takes $B_{\rho,2}$ to the standard Euclidean ball $B_2$, then the dual-Sudakov inequality tells us how to bound the latter quantity:

      \[\begin{equation}\label{eq:lewis-ds} (\log \mathcal{N}(B_{\rho,2}, r B_{\rho,\infty}))^{1/2} \lesssim \frac{n^{1/p}}{r} \max_{i=1,\ldots,m} \frac{|\langle U a_i, \mathbf{g}\rangle|}{\|U a_i\|_2} \lesssim \frac{n^{1/p}}{r} \sqrt{\log m}\,.\end{equation}\]

      Problematically, this bound is too weak to recover \eqref{eq:ldb}. Taking both sides to the $p/2$ power gives

      \[\begin{equation}\label{eq:badcover} (\log \mathcal{N}(n^{1/p} B_{\rho,2}, r B_{\rho,\infty}))^{1/p} \lesssim \frac{n^{1/2}}{r^{2/p}} (\log m)^{p/4}\,.\end{equation}\]

      The issue is that the scaling in $r$ is bad unless $p=2$ (compare \eqref{eq:ldb}).

    • The problem is our use of the worst-case bound $B_{A,p} \subseteq n^{1/p} B_{\rho,2}$. There is a more sophisticated way to bound covering numbers in this setting using norm interpolation and duality of covering numbers (see Ledoux-Talagrand, Prop 15.19).

      But the sort of improvement one needs can be summarized as follows: \eqref{eq:badcover} is bad for $r \to 0$, but it gives a good bound for $r$ small, but constant. Having covered $B_{\rho,2}$ by translates of $B_{\rho,\infty}$, we get an improved bound on the inner products $|\langle a_i,x\rangle|^{2-p}$ in \eqref{eq:lewis-exp} This, in turn, gives us a better bound on $\norm{U^{-1} x}_2$, which means that when we want to reduce the radius from $r$ to $r/2$ (say), we get an improved estimate for the Euclidean diameter, making the application of \eqref{eq:lewis-ds} stronger.

    Computation of the Lewis weights for $p < 4$: A contraction argument

    The following algorithm is taken from $\ell_p$ row sampling by Lewis weights by Cohen and Peng.

    • For $p < 4$, it is possible to compute the Lewis weights using a simple iterative scheme, assuming that one can compute leverage scores. For a matrix $A$, define the $i$th leverage score by $\sigma_i(A) \mathrel{\mathop:}=\langle a_i, (A^{\top} A)^{-1} a_i\rangle$. In this section, if $W$ is a nonnegative $m \times m$ diagonal matrix, we will use $w \in \R_+^m$ for the vector with $w_i = W_{ii}$ for $i=1,\ldots,m$.

    • Define $\tau_i(w) \mathrel{\mathop:}=\langle a_i, (A^{\top} W A)^{-1} a_i\rangle$. The idea is to recall that for the Lewis weights, we have $w_i = \norm{U a_i}_2^{p-2}$, hence

      \[\begin{equation}\label{eq:lw-desc1} w_i^{2/(p-2)} = \|Ua_i\|_2^2 = \langle a_i, (A^{\top} W A)^{-1} a_i\rangle = \tau_i(w) = \frac{\sigma_i(W^{1/2} A)}{w_i}\,,\end{equation}\]

      since $\sigma_i(W^{1/2} A) = w_i \langle a_i, (A^{\top} W A)^{-1} a_i\rangle$.

    • This suggests the simple update rule

      \[\hat{w}_i \mathrel{\mathop:}=\tau_i(w)^{(p-2)/2} = \left(\frac{\sigma_i(W^{1/2} A)}{w_i}\right)^{(p-2)/2}\,,\]

      where we use the latter equality for computing $\tau_i(w)$ given a subroutine that computes leverage scores of $W^{1/2} A$.

    • To see that this converges quickly to the unique weights satisfying \eqref{eq:lw-desc1}, let us define the metric $d$ on weights:

      \[d(w,w') \mathrel{\mathop:}=\max \left\{\left|\log \frac{w_i}{w'_i}\right| : i =1,\ldots,m\right\}\,,\]

      as well as the iteration map $\varphi: \R_+^m \to \R_+^m$ by

      \[\varphi(w)_i \mathrel{\mathop:}=\left(\tau_i\left(w\right)\right)^{(p-2)/p}\,.\]

      Finally, define the vector $\tau(w) \in \R_+^m$ by $\tau(w) \mathrel{\mathop:}=(\tau_1(w),\ldots,\tau_m(w))$.

    • Claim: For any $w,w’ \in \R_+^m$, it holds that \(d(\tau(w),\tau(w')) \leqslant d(w,w')\,.\)

    • Proof: If $w_i \leqslant\alpha w’_i$ for every $i=1,\ldots,m$, then $W \preceq \alpha W’$ in the PSD (Loewner) ordering. Therefore $A^{\top} W A \preceq \alpha A^{\top} W’ A$, and finally $(A^{\top} W’ A)^{-1} \preceq \alpha (A^{\top} W A)^{-1}$, since matrix inversion inverts the PSD order.

      Finally, by definition this yields $\langle u, (A^{\top} W’ A)^{-1} u\rangle \leqslant\langle u, (A^{\top} W A)^{-1} u \rangle$ for every $u \in \R^m$. In particular, $\tau_i(w’) \leqslant\alpha \tau_i(w)$ for each $i=1,\ldots,m$.

    • As an immediate consequence of the lemma, we have

      \[d\!\left(\varphi(w), \varphi(w')\right) \leqslant\left|\frac{p-2}{p}\right| d(w,w')\,.\]

      Note that if $1 \leqslant p < 4$, then $|(p-2)/p| < 1$, and therefore $\varphi$ is a strict contraction on the space of weights. In particular, it holds that for $\delta > 0$ such that $1-\delta = |(p-2)/p|$, and any $w_0 \in \R_+^m$,

      \[d(\varphi^{k+1}(w_0), \varphi^{k}(w_0)) \leqslant(1-\delta)^{k} d(\varphi(w_0),w_0)\,,\]

      so that ${\varphi^k(w_0)}$ is a Cauchy sequence and therefore ${\varphi^k(w_0)}$ converges to a limit point.

      Moreover, one can see that the limit point is unique, since for any $w_0,w_1 \in \R_+^m$,

      \[d(\varphi^k(w_0), \varphi^k(w_1)) \leqslant(1-\delta)^k d(w_0, w_1)\,.\]

      Since the Lewis weights \eqref{eq:lw-desc1} exist, they must be the unique limit of this procedure starting from any initial weights.

    Norm sparsification Sparsifying $F(x) = N_1(x) + \cdots + N_m(x)$, $N_1,\ldots,N_m$ norms on $\R^n$.
    Mon, Nov 06
    Importance scores for general norms
    • Semi-norms
    • Spectral hypergraph sparsifiers
    • Symmetric submodular functions
    • The Lovasz extension
    • Importance scores and spherical averages

        or     Notes as a full page

    The material in this lecture is taken from Sparsifying sums of norms.

    Suppose that $N_1,\ldots,N_m : \R^n \to \R_+$ are semi-norms on $\R^n$, and define the semi-norm given by their $\ell_p$ sum, for $p > 1$:

    $$\begin{equation}\label{eq:ns} N(x) \mathrel{\mathop:}=\left(N_1(x)^p + \cdots + N_m(x)^p\right)^{1/p}\,.\end{equation} $$

    Our goal will be to sparification where the terms are $f_i(x) \mathrel{\mathop:}=N_i(x)^p$ in our usual notation, and $F(x) = N(x)^p$.

    A semi-norm $N$ is nonnegative and satisfies $N(\lambda x) = |\lambda| N(x)$ and $N(x+y) \leqslant N(x)+N(y)$ for all $\lambda\in \R$, $x,y \in \R^n$, though possibly $N(x)=0$ for $x \neq 0$.

    Note that, equivalently, a semi-norm is a homogeneous function: $N(\lambda x) = |\lambda| N(x)$ such that $N$ is also convex.


    • Spectral hypergraph sparsifiers. Consider subsets $S_1,\ldots,S_m \subseteq {1,\ldots,n}$ and

      \[f_i(x) \mathrel{\mathop:}=\max_{u,v \in S_i} (x_u-x_v)^2\,.\]

      Then sparsifying $F(x) = f_1(x) + \cdots + f_m(x)$ is called spectral hypergraph sparsification.

      More generally, we can consider linear operators ${ A_i \in \R^{d_i \times n} }$ and the terms $f_i(x) \mathrel{\mathop:}=\norm{A_i x}_{\infty}^2$.

    • Symmetric submodular functions. A function $f : 2^V \to \R_+$ is submodular if

      \[f(S \cup \{v\}) - f(S) \geqslant f(T \cup \{v\}) - f(T)\,,\quad \forall S \subseteq T \subseteq V, v \in V\,.\]

      If one imagines $S \subseteq V$ to be a set of items and $f(S)$ to be the total utility, then this says that the marginal utility of an additional item $v \in V$ is non-increasing as the basket of items increases. As a simple example, consider $w_1,\ldots,w_n \geqslant 0$ and $f(S) \mathrel{\mathop:}=\min\left(B, \sum_{i \in S} w_i\right)$. Here, the additional utility of an item drops to $0$ as one approaches the “budget” $B$.

      • A submodular function $f : 2^V \to \R_+$ is symmetric if $f(\emptyset)=0$ and $f(S) = f(V \setminus S)$ for all $S \subseteq V$. A canonical example is where $f(S)$ denote the set of edges leaving a cut $(S, V \setminus S)$ in an undirected graph.

      • The Lovász extension of a submodular function is the function $\tilde{f} : [0,1]^V \to \R_+$ defined by

        \[\tilde{f}(x) \mathrel{\mathop:}=\mathop{\mathrm{\E}}[f(\mathbf{S}_x)]\,,\]

        where $\mathbf{S}_x$ is a random set such that $i \in \mathbf{S}_x$ with probability $x_i$, independently for every $i \in V$.

        Fact: A function $f : 2^V \to \R_+$ is submodular if and only if $\tilde{f} : [0,1]^V \to \R_+$ is convex.

      • Because of this fact, it turns out that a function $f$ is symmetric and submodular if and only if $\tilde{f}$ is a semi-norm, where we extend $\tilde{f} : \R^V \to \R_+$ to all of $\R^V$ via the definition

        \[\begin{equation}\label{eq:lov-ext} \tilde{f}(x) \mathrel{\mathop:}=\int_{-\infty}^{\infty} f(\{ i : x_i \leqslant t \})\,dt\,.\end{equation}\]

        Note that if \(x \in \{0,1\}^V\) is such that $x_i = \mathbf{1}_S(i)$, then we have

        \[\tilde{f}(x) = \int_{0}^1 f(\{ i : x_i \leqslant t \}\,dt\,,\]

        since $f(\emptyset)=f(V)=0$. Moreover, \(\{ i : x_i \leqslant t \} = S\) for $0 < t < 1$, hence $\tilde{f}(x)=f(S)$. So $\tilde{f}$ is indeed an extension of $f$. A similar argument shows that for $x$ defined this way, $\tilde{f}(-x)=f(V \setminus S)$.

      • Lemma: A function $f : 2^V \to \R_+$ is symmetric and submodular if and only if $\tilde{f}$ is a semi-norm.

      • Proof: A semi-norm $\tilde{f}$ is convex, and therefore by the fact above, $f$ is submodular. Moreover $\tilde{f}(0)=0$ and $\tilde{f}(x)=\tilde{f}(-x)$ for a semi-norm, so if $x \in {0,1}^V$ is given by $x_i = \mathbf{1}_S(i)$, then $f(S)=\tilde{f}(x)=\tilde{f}(-x)=f(V\setminus S)$ and thus $f$ is symmetric.

        Conversely, if $f$ is symmetric and submodular, then $\tilde{f}(x)=\tilde{f}(-x)$ and $\tilde{f}$ is convex. Moreover, the defintion \eqref{eq:lov-ext} shows that $\tilde{f}(cx)=c \tilde{f}(x)$ for $c > 0$, hence $\tilde{f}$ is also homogeneous, implying that it’s a semi-norm.

      • Thus the sparsification problem for sums $F(S) = f_1(S) + \cdots + f_m(S)$ of symmetric submodular functions ${f_i}$ reduces to the sparsification problem for semi-norms: $\tilde{F}(x) \mathrel{\mathop:}=\tilde{f}_1(x) + \cdots + \tilde{f}_m(x)$.

    Importance scores

    • Let us consider now the sparsification problem \eqref{eq:ns} for $p=1$. As we discussed in previous lectures, a reasonable way of assigning sampling probabilities is to take

      \[\begin{equation}\label{eq:ns-rhoi} \rho_i \mathrel{\mathop:}=\frac{\mathop{\mathrm{\E}}[N_i(\mathbf{Z})]}{\mathop{\mathrm{\E}}[N(\mathbf{Z})]}\end{equation}\]

      for some random variable $\mathbf{Z}$ on $\R^n$, as this represents the “importance” of the $i$th term $N_i(x)$ under the distribution of $\mathbf{Z}$.

    • Let us also recall the notion of approximation we’re looking for:

      \[\max_{N(x) \leqslant 1} |N(x)-\tilde{N}(x)| \leqslant\varepsilon\,.\]

      Given the desired approximation guarantee, it’s natural to simply take $\mathbf{Z}$ to be uniform on the unit ball

      \[B_N \mathrel{\mathop:}=\{ x \in \R^n : N(x) \leqslant 1 \}\,.\]
    • For the purposes of analysis, it will be easier to take $\mathbf{Z}$ as the random variable on $\R^n$ with density proportional to $e^{-N(x)}$, but let us see that gives the same probabilities \eqref{eq:ns-rhoi} as averaging uniformly over $B_N$. To compare the density $e^{-N(x)}$ and the density that is uniform on $B_N$, one can take $\varphi_1(y)=e^{-y}$ and \(\varphi_2(y) = \mathbf{1}_{\{y \leqslant 1\}}\) in the next lemma.

    • Lemma: Suppose that the density of the random variable $\mathbf{Z}$ is $\varphi(N(x))$ for any sufficiently nice function $\varphi$. Then the probabilities defined in \eqref{eq:ns-rhoi} are independent of $\varphi$.

    • Proof: Define $V(r) \mathrel{\mathop:}=\mathrm{vol}_n(r B_N)$ and note that $V(r) = r^n \mathrm{vol}_n(B_n)$. Let $u : \R^n \to \R$ be a homogeneous function, i.e., $u(\lambda x) = |\lambda| u(x)$ for all $\lambda \in \R, x \in \R^n$. Then we have

      \[\begin{aligned} \int_{\R^n} \varphi(N(x)) u(x)\,dx &= \int \varphi(r) V'(r) \int_{x : N(x)=r} u(x)\,dx \\ &= n \mathrm{vol}_n(B_N) \int \varphi(r) r^{n-1} \int_{x : N(x)=r} u(x)\,dx \,dr \\ &= n \mathrm{vol}_n(B_N) \int \varphi(r) r^{n} \,dr \int_{x : N(x)=1} u(x)\,dx\,. \end{aligned}\]

      In other words, the integral only depends on the latter spherical integral, which is independent of $\varphi$.

    Wed, Nov 08
    The KLS conjecture, covering, and concentration
    • Concentration of Lipschitz functions
    • Conductance of isotropic convex bodies
    • Tails and covering numbers
    • The homotopy method

        or     Notes as a full page

    • Recall that \(N(x) = N_1(x) + \cdots + N_m(x)\,,\) and $B_N$ is the unit ball in the norm $N$. To apply our standard symmetrization/chaining analysis, define the metric
    $$d_{\nu}(x,y) \mathrel{\mathop:}=\left(\sum_{j=1}^M \left(\frac{N_{\nu_j}(x)-N_{\nu_j}(y)}{M \rho_{\nu_j}}\right)^2 \right)^{1/2}.$$
    • We need a bound on the covering numbers of the form

      \[(\log \mathcal{N}(B_N, d_{\nu}, r))^{1/2} \lesssim \frac{L}{r} \left(\max_{x \in B_N} \tilde{N}_{\nu}(x)\right)^{1/2}\,,\]


      \[\tilde{N}_{\nu}(x) \mathrel{\mathop:}=\sum_{j=1}^M \frac{N_{\nu_j}(x)}{M \rho_{\nu_j}}\,.\]
    • As usual, we will bound $d_{\nu}$ by an $\ell_{\infty}$ metric: For $x,y \in B_N$,

      \[d_{\nu}(x,y) \lesssim \max_i \left(\frac{|N_i(x)-N_i(y)|}{M \rho_i}\right)^{1/2} \left(\max_{x \in B_N} \tilde{N}_{\nu}(x) \right)^{1/2}.\]
    • Define $\mathcal N_{\infty}(x) \mathrel{\mathop:}=\max_{i \in [m]} \frac{N_i(x)}{M \rho_i}$ so that

      \[d_{\nu}(x,y) \lesssim \mathcal N_{\infty}(x-y)^{1/2} \left(\max_{x \in B_N} \tilde{N}_{\nu}(x) \right)^{1/2},\]

      where we used $\abs{N_i(x)-N_i(y)} \leqslant N_i(x-y)$.

    • Thus we have reduced to the simpler problem of bounding the covering numbers

      \[\begin{aligned} (\log \mathcal{N}(B_N, \mathcal N_{\infty}^{1/2}, r))^{1/2} \lesssim \frac{L}{r}\,, \ \forall r > 0 &\iff (\log \mathcal{N}(B_N, \mathcal N_{\infty}, r^2))^{1/2} \lesssim \frac{L}{r}\,, \ \forall r > 0 \\ &\iff \log \mathcal{N}(B_N, \mathcal N_{\infty}, r) \lesssim \frac{L^2}{r}\,, \ \forall r > 0\,.\end{aligned}\]

      Now the generalized dual-Sudakov Lemma (for $p=1$) gives

      \[\log \mathcal{N}(B_N, \mathcal N_{\infty}, r) \lesssim \frac{\mathbb{E}[\mathcal N_{\infty}(\mathbf{Z})]}{r}\,,\]

      where $\mathbf{Z}$ is the random variable with density $e^{-N(x)}$. Note that this applies without any assumptions on $N$ because every norm is $p$-uniformly smooth when $p=1$.

    • Recall that in this setting we take $\rho_i = \frac{1}{n} \mathbb{E}[N_i(\mathbf{Z})]$, therefore

      \[\mathcal N_{\infty}(x) = \max_i \frac{N_i(x)}{\rho_i} = \frac{n}{M} \max_i \frac{N_i(x)}{\mathbb{E}N_i(\mathbf{Z})}\,.\]

      We will momentarily prove that

      \[\begin{equation}\label{eq:max-ub} \mathbb{E}\max_{i} \frac{N_i(\mathbf{Z})}{\mathbb{E}N_i(\mathbf{Z})} \lesssim \psi_n \log M\,, \end{equation}\]

      where $\psi_n$ is the KLS constant in $n$ dimensions. In this case, we arrive at the bound

      \[\log \mathcal N(B_N, \mathcal N_{\infty}, r) \lesssim \frac{1}{r} \frac{n}{M} \psi_n \log M\,,\]

      which yields

      \[\left(\log \mathcal N(B_N, d_{\nu}, r)\right)^{1/2} \lesssim \frac{1}{r} \left(\frac{n}{M} \psi_n \log M\right)^{1/2} \left(\max_{x \in B_N} \tilde{N}_{\nu}(x)\right)^{1/2}\,,\]
    • Combined with Dudley’s entropy bound and Problem 4 on Homework #1, this yields: For every $\nu_1,\ldots,\nu_M \in [m]^M$,

      \[\mathbb{E}_{\varepsilon_1,\ldots,\varepsilon_M \in \{\pm 1\}} \max_{N(x) \leqslant 1} \left|\sum_{j=1}^M \varepsilon_j \frac{N_{\nu_j}(x)}{M \rho_{\nu_j}}\right| \lesssim \left(\frac{n}{M} \psi_n \log M\right)^{1/2} (\log n) \left(\max_{x \in B_N} \tilde{N}_{\nu}(x)\right)^{1/2}.\]

      If we choose $M \asymp \frac{n}{\delta^2} \psi_n \log(n/\delta) (\log n)^2$, then our sparsification framework yields

      \[\mathbb{E}\max_{N(x) \leqslant 1} |N(x)-\tilde{N}_{\nu}(x)| \lesssim \delta\,,\]

      completing the proof.

    Concentration of Lipschitz functions and KLS

    • We are left to prove \eqref{eq:max-ub}. First, observe that the maximum need only be taken over indicies $i$ in the set ${ \nu_1,\ldots,\nu_M }$. So we are looking at the expected maximum of (at most) $M$ random variables. The claimed upper bound follows immediately (see Problem 2(A) on Homework #2) from an exponential concentration inequality:

      \[\begin{equation}\label{eq:ni-con} \mathbb{P}\left[N_i(\mathbf{Z}) > (t+1) \mathbb{E}[N_i(\mathbf{Z})]\right] \leqslant e^{-c t/\psi_n}\,.\end{equation}\]

      Note that $N_i$ is only a small piece of the norm $N$, so there is essentially no exploitable relationship between $N_i$ and $\mathbf{Z}$. Therefore the concentration inequality will have to hold in some generality.

    • Log-concave random vector: A random variable $\mathbf{X}$ on $\mathbb{R}^n$ is log-concave if its density is $e^{-g(x)}\,dx$ for some convex function $g : \mathbb{R}^n \to \mathbb{R}$.

    • Isotropic random vector: A random variable $\mathbf{X}$ on $\mathbb{R}^n$ is isotropic if $\mathbb{E}[\mathbf{X}]=0$ and $\mathbb{E}[\mathbf{X} \mathbf{X}^{\top}] = I$.

    • Lipschitz functions: A function $\varphi: \mathbb{R}^n \to \mathbb{R}$ is $L$-Lipschitz (with respect to the Euclidean distance) if

      \[|\varphi(x)-\varphi(y)\| \leqslant L \|x-y\|_2\,, \quad \forall x,y \in \mathbb{R}^n\,.\]
    • Lemma [Gromov-Milman]: If $\varphi: \mathbb{R}^n \to \mathbb{R}$ is $L$-Lipschitz and $\mathbf{X}$ is an isotropic, log-concave random variable, then

      \[\begin{equation}\label{eq:kls-tail} \mathbb{P}\left(|\varphi(\mathbf{X})-\mathbb{E}[\varphi(\mathbf{X})]| > t L\right) \leqslant e^{-c t/\psi_n}\,,\end{equation}\]

      where $c > 0$ is an absolute constant and $\psi_n$ is the KLS constant in $n$ dimensions.

      Note that the KLS constant has many definitions that are equivalent up to a constant factor, and one could take \eqref{eq:kls-tail} as the definition. See links to related material at the end.

    • To prove \eqref{eq:ni-con}, first note that our random variable $\mathbf{Z}$ is log-concave because $N(x)$ is a convex function. To make it isotropic, we define $\mathbf{X} \mathrel{\mathop:}=A^{-1/2} \mathbf{Z}$, where $A \mathrel{\mathop:}=\mathbb{E}[\mathbf{Z} \mathbf{Z}^{\top}]$. We will apply the lemma to the map $\varphi(x) \seteq N_i(A^{1/2} x)$, and we need only evaluate the Lipschitz constant of $\varphi$.

      In Homework #2 problem 4, you show that $\varphi$ is $L$-Lipschitz with $L \lesssim \mathbb{E}[N_i(\mathbf{Z})]$, yielding \eqref{eq:ni-con}.

    Further reading on KLS

    Wed, Nov 15
    The homotopy method
    • Approximate sample probabilities
    • Sampling from a convex body
    • The homotopy method

        or     Notes as a full page

    Computing the importance scores

    One thing we haven’t addressed yet is how to actually compute the values

    $$\rho_i = \frac{\mathbb{E}N_i(\mathbf{Z})}{\mathbb{E}N(\mathbf{Z})}\,,$$

    when $\mathbf{Z}$ chosen uniformly (according to the volume measure on $\mathbb{R}^n$) from the unit ball $B_N$.

    • A first important thing to notice is that it suffices to sample according to a distribution $\tilde{\rho}$ that satisfies $\tilde{\rho}_i \geq c \rho_i$ for all $i=1,\ldots,m$ and some constant $c > 0$. This follows from the way the importances ${\rho_i}$ are used in our analyses.

      Recall the distance

      \[d_{\nu,\rho}(x,y) = \left(\sum_{j=1}^M \left(\frac{f_{\nu_j}(x)-f_{\nu_j}(y)}{M \rho_{\nu_j}}\right)^2\right)^{1/2},\]

      and that our sparsification bounds are in terms of entropy numbers $e_{h}(B_F, d_{\nu,\rho})$ with \(B_F \seteq \{ x \in \R^n : F(x) \leq 1 \}\). Note that for an approximation $\tilde{\rho}$ as above, we have

      \[d_{\nu,\tilde{\rho}}(x,y) \leq \frac{1}{c} d_{\nu,\rho}(x,y)\,,\quad \forall x,y \in \R^n\,,\]

      which implies $e_h(B_F, d_{\nu,\tilde{\rho}}) \leq \frac{1}{c} e_h(B_F, d_{\nu,\rho})$.

    • It is now well-understood how to (approximatley) sample from the uniform measure on a convex body. (See JLLS21 and CV18.)

      Theorem: There is an algorithm that, given a convex body $A \subseteq \mathbb{R}^n$ satisfying $r \cdot B_2^n \subseteq A \subseteq R \cdot B_2^n$ and $\varepsilon> 0$, samples from a distribution that is within TV distance $\varepsilon$ from the uniform measure on $A$ using $O(n^3 (\log \frac{nR}{\varepsilon r})^{O(1)})$ membership oracle queries to $A$, and $(n (\log \frac{nR}{\varepsilon r}))^{O(1)}$ additional time.

    • Say that a seminorm $N$ on $\mathbb{R}^n$ is $(r,R)$-rounded if it holds that

      \[r \|x\|_2 \leqslant N(x) \leqslant R \|x\|_2\,,\quad \forall x \in \ker(N)^{\perp}\,.\]

      If $N$ is a genuine norm (so that $\ker(N)=0$) and $N$ is $(r,R)$-rounded, then $r B_2^n \subseteq B_N \subseteq R B_2^n$, hence we can use the theorem to sample and estimate. But even in this case the running time for computing $\rho_1,\ldots,\rho_m$ would be $\tilde{O}(m n^{O(1)} \log \frac{R}{r})$ since evaluating $N$ requires $\Omega(m)$ time assuming each $N_i$ can be evalued in $O(1)$ time.

    The homotopy method

    • Sparsification from sampling from sparsification. We would like a better running time of the form $\tilde{O}(m + n^{O(1)}) \log \frac{R}{r}$.

      Note that if we could compute approximations ${\tilde{\rho}_i}$ to the importance scores, then we could sample from $\tilde{\rho}$, which would allow us to sparsify to obtain an approximation $\tilde{N}$ to $N$ with only $\tilde{O}(n)$ non-zero terms. This approximation could then be evaluated in time $n^{O(1)}$, allowing us to obtain samples in only $n^{O(1)}$ time. With these samples, we could then sparisfy.

    • To solve this “chicken and egg” problem, we use the following simple (but elegant) method. Define, for $t > 0$,

      \[N_t(x) \mathrel{\mathop:}=N(x) + t \norm{x}_2\,.\]

      Assuming $N$ is an $(r,R)$-rounded semi-norm, it follows that $N_R(x) \asymp R \norm{x}_2$.

      As far as I know, in the context of \(\ell_p\) regression and sparsification vs. sampling, this “homotopy method” originates from work of [Li-Miller-Peng 2013] . See also the work of [Bubeck-Cohen-Lee-Li 2017].

    • Thus we can compute an \(\tilde{O}(n)\)-sparse $1/2$-approximation to \(\tilde{N}_R(x)\) by sampling from the Euclidean unit ball. But now since \(\tilde{N}_t(x) \asymp \tilde{N}_{t/2}(x)\) this allows us to obtain an \(\tilde{O}(n)\)-sparse $1/2$-approximation \(\tilde{N}_{R/2}(x)\) of \(N_{R/2}(x)\).

    • Doing this $O(\log \frac{R}{\varepsilon r})$ times, we eventually obtain an $\tilde{O}(n)$-sparse $1/2$-approximation \(\tilde{N}_{\varepsilon r}(x)\) of \(N_{\varepsilon r}(x)\).

    • We can use this to obtain an $\tilde{O}(n)$ sparse $\varepsilon$-approximation to $N_{\varepsilon r}(x)$ which it self an $\varepsilon$-approximation to $N(x)$ on $\ker(N)^{\perp}$, using the assumption that $N$ is $(r,R)$-rounded.

    • Finally, we can easily remove the Euclidean shift from \(\tilde{N}_{\varepsilon r}(x)\), yielding a genuine $\varepsilon$-approximation to $N(x)$, since only the Euclidean part of $\tilde{N}_{\varepsilon r}(x)$ has support on $\ker(N)$.

    General linear models Sparsifying $F(x) = \varphi(\langle a_1,x\rangle - b_1) + \cdots + \varphi(\langle a_m, x\rangle - b_m)$, $\varphi : \R \to \R$.
    Mon, Nov 20
    Multiscale importance scores
    • Huber and $\gamma_p$ losses
    • Multiscale importance scores

        or     Notes as a full page

    Empirical risk minimization (ERM) is a widely studied problem in learning theory and statistics. A prominent special case is the problem of optimizing a generalized linear model (GLM), i.e.,

    $$\begin{equation}\label{eq:glm} \min_{x \in \mathbb R^n} F(x) \quad \textrm{ for } \quad F(x) := \sum_{i=1}^m \varphi_i(\langle a_i, x \rangle - b_i)\,, \end{equation}$$

    where the total loss $F : \mathbb R^n \to \mathbb R$, is defined by vectors $a_1,\ldots,a_m \in \mathbb R^n$, $b \in \mathbb R^m$, and loss functions $f_1, \dots, f_m: \mathbb R\to \mathbb R$. Different choices of the loss functions ${ \varphi_i }$ capture a variety of important problems, including linear regression, logistic regression, and $\ell_p$ regression.

    When $m \gg n$, a natural approach for fast algorithms is to apply sparsification techniques to reduce the value of $m$, while maintaining a good multiplicative approximation of the objective value. This corresponds to our usual sparsification model where $f_i(x) = \varphi_i(\langle a_i,x\rangle - b_i)$. As mentioned previously, we will can assume that $b_1=\cdots=b_m=0$ by lifting to one dimension higher. In previous lectures, we solved the sparsification problem when $\varphi_i(z)=|z|^p$ for some $1 \leqslant p \leqslant 2$.

    However, $\ell_p$ losses are the only class of natural loss functions for which linear-size sparsification results are known for GLMs. For instance, for the widely-studied class of Huber-type loss functions, e.g., $\varphi_i(z) = \min(|z|, |z|^2)$, the best known sparsity bounds obtained by previous methods were of the form is $\tilde{O}(n^{4-2\sqrt{2}} \varepsilon^{-2})$.

    We will often think of the case $\varphi_i(z) = h_i(z)^2$ for some $h_i : \mathbb R\to \mathbb R_+$, as the assumptions we need are stated more naturally in terms of $\sqrt{\varphi_i}$. To that end, consider a function $h : \mathbb R^n \to \mathbb R_+$ and the following two properties, where $L, c, \theta> 0$ are some positive constants.

    • Assumptions:

      1. ($L$-auto-Lipschitz) $|h(z)-h(z’)| \leqslant L\,h(z-z’)$ for all $z,z’ \in \mathbb R$.

      2. (Lower $\theta$-homogeneous) $h(\lambda z) \geqslant c \lambda^{\theta} h(z)$ for all $z \in \mathbb R$ and $\lambda \geqslant 1$.

    • Theorem [Jambulapati-Lee-Liu-Sidford 2023]: Consider $\varphi_1, \dots, \varphi_m: \mathbb R\to \mathbb R_{+}$, and suppose there are numbers $L,c,\theta> 0$ such that each $\sqrt{\varphi_i}$ is $L$-auto-Lipschitz and lower $\theta$-homogeneous (with constant $c$). Then for any $a_1,\ldots,a_m \in \mathbb R^n$, and numbers $0 < \varepsilon< \tfrac12$ and $s_{\max}> s_{\min}\geqslant 0$, there are nonnegative weights $w_1,\ldots,w_m \geqslant 0$ such that

      \[\left| F(x) - \sum_{i=1}^m w_i \varphi_i(\langle a_i,x\rangle) \right| \leqslant\varepsilon F(x)\,,\quad \forall x \in \mathbb R^n \ \textrm{ s.t. }\ s_{\min}\leqslant F(x) \leqslant s_{\max}\,,\]

      where $F(x) \mathrel{\mathop:}=\varphi_1(\langle a_1,x\rangle) + \cdots + \varphi_m(\langle a_m,x\rangle)$, and

      \[\left | i \in \{1,\ldots,m\} : w_i > 0 \right | \lesssim_{L,c,\theta} \frac{n}{\varepsilon^2} \log \left(\frac{n}{\varepsilon} \frac{s_{\max}}{s_{\min}}\right) \left(\log \frac{n}{\varepsilon}\right)^3\,.\]

      Moreover, the weights ${w_i}$ can be computed in time \(\tilde{O}_{L,c,\theta}\!\left((\mathsf{nnz}(a_1,\ldots,a_m) + n^{\omega} + m \mathcal{T}_{\mathrm{eval}}) \log(ms_{\max}/s_{\min})\right)\), with high probability.

    • Here, $\mathcal{T}_{\mathrm{eval}}$ is the maximum time needed to evaluate each $\varphi_i$, $\mathsf{nnz}(a_1,\ldots,a_m)$ is the total number of non-zero entries in the vectors $a_1,\ldots,a_m$, and $\omega$ is the matrix multiplication exponent. “High probability” means that the failure probability can be made less than $n^{-\ell}$ for any $\ell > 1$ by increasing the running time by an $O(\ell)$ factor.

    It is not difficult to see that for $0 < p \leqslant 2$, the function $\varphi_i(z) = |z|^p$ satisfies the required hypotheses of . Additionally, the $\gamma_p$ functions, defined as

    $$\begin{equation}\label{eq:gammap} \gamma_p(z) := \begin{cases} \frac{p}{2}z^2 & \enspace \text{ for } \enspace |z| \leqslant 1 \\ |z|^p - (1-\frac{p}{2}) & \enspace \text{ for } \enspace |z| \geqslant 1, \end{cases}\end{equation}$$

    for $p \in (0, 2]$, also satisfy the conditions. The special case of $\gamma_1$ is known as the Huber loss.

    To show that $\sqrt{\gamma_p}$ is $1$-auto-Lipschitz, it suffices to prove that $\sqrt{\gamma_p}$ is concave. The lower growth bound is immediate for $p > 0$.

    Importance sampling and multiscale weights

    Given $F(x) = \varphi_1(\langle a_1,x\rangle) + \cdots + \varphi_m(\langle a_m,x\rangle)$, our approach to sparsification is via importance sampling. Given a probability vector $\rho \in \mathbb R^m$ with $\rho_1,\ldots,\rho_m > 0$ and $\rho_1 + \cdots + \rho_m = 1$, we sample $M \geqslant 1$ coordinates $\nu_1,\ldots,\nu_M$ i.i.d. from $\rho$, and define our potential approximator by

    $$\tilde{F}(x) \seteq \frac{1}{M} \sum_{j=1}^M \frac{\varphi_{\nu_j}(\langle a_{\nu_j},x\rangle)}{\rho_{\nu_j}}\,.$$

    Since we want an approximation guarantee to hold simultaneously for many $x \in \mathbb R^n$, it is natural to analyze expressions of the form

    $$\mathop{\mathrm{\mathbb{E}}}\max_{F(x) \leqslant s} \left|F(x)-\tilde{F}(x)\right|.$$

    As we have seen, analysis of this expression involves the size of discretizations of the set \(B_F(s) \mathrel{\mathop:}=\{ x \in \mathbb R^n : F(x) \leqslant s \}\) at various granularities. The key consideration (via Dudley’s entropy inequality) is how well $B_F(s)$ can be covered by cells on which we have uniform control on how much the terms $\varphi_i(\langle a_i,x\rangle)/\rho_i$ vary within each cell.

    The $\ell_2$ case

    Let’s consider the case $\varphi_i(z) = |z|^2$ so that $F(x) = |\langle a_1,x\rangle|^2 + \cdots + |\langle a_m,x\rangle|^2$. Here, \(B_F(s) = \{ x \in \mathbb R^n : \norm{Ax}_2^2 \leqslant s \}\), where $A$ is the matrix with $a_1,\ldots,a_m$ as rows.

    A cell at scale $2^{j}$ looks like

    $$\mathsf K_j \mathrel{\mathop:}=\left\{ x \in \mathbb R^n : \max_{i \in [m]} \frac{|\langle a_i,x\rangle|^2}{\rho_i} \leqslant 2^{j} \right\},$$

    and the pertinent question is how many translates of $\mathsf K_j$ it takes to cover $B_F(s)$. In the $\ell_2$ case, this is the well-studied problem of covering Euclidean balls by $\ell_{\infty}$ balls.

    If $N_j$ denotes the minimum number of such cells required, then the dual-Sudakov inequality tells us that

    $$\log N_j \lesssim \frac{s}{2^j} \log (m) \max_{i \in [m]} \frac{\|(A^{\top} A)^{-1/2} a_i\|^2_2}{\rho_i}\,.$$

    Choosing $\rho_i \mathrel{\mathop:}=\frac{1}{n} |(A^\top A)^{-1/2} a_i|_2^2$ to be the normalized leverage scores yields uniform control on the size of the coverings:

    $$\log N_j \lesssim \frac{s}{2^j} n \log m\,.$$

    The $\ell_p$ case

    Now we take $\varphi_i(z) = |z|^p$ so that $F(x) = \norm{Ax}_p^p$. A cell at scale $2^j$ now looks like

    $$\mathsf K_j \mathrel{\mathop:}=\left\{ x \in \mathbb R^n : \max_{i \in [m]} \frac{|\langle a_i,x\rangle|^p}{\rho_i} \leqslant 2^j \right\},$$

    To cover $B_F(s)$ by translates of $\mathsf K_j$, we will again employ Euclidean balls, and one can use $\ell_p$ Lewis weights to relate the $\ell_p$ structure to an $\ell_2$ structure.

    A classical result of Lewis establishes that there are nonnegative weights $w_1,\ldots,w_m \geqslant 0$ such that if $W = \mathrm{diag}(w_1,\ldots,w_m)$ and $U \mathrel{\mathop:}=(A^{\top} W A)^{1/2}$, then

    $$\begin{equation}\label{eq:wis} w_i = \frac{\norm{U^{-1} a_i}_2^p}{\norm{U^{-1} a_i}_2^2} = \frac{\varphi_i(\norm{U^{-1} a_i}_2)}{\norm{U^{-1} a_i}_2^2}\,. \end{equation}$$

    Assuming that $A$ has full rank, a straightforward calculation gives $\sum_{i=1}^m w_i \norm{U^{-1} a_i}_2^2 = \mathop{\mathrm{tr}}(U^2 U^{-2}) = n$.

    Therefore we can take $\rho_i \mathrel{\mathop:}=\frac{1}{n} w_i \norm{U^{-1} a_i}_2^2$ for $i=1,\ldots,m$, and our cells become

    $$\mathsf K_j \mathrel{\mathop:}=\left\{ x \in \mathbb R^n : \max_{i \in [m]} |\langle a_i,x\rangle|^p \leqslant\frac{2^j}{n} w_i \norm{U^{-1} a_i}_2^2 \right\}.$$

    If we are trying to use $\ell_2$-$\ell_{\infty}$ covering bounds, we face an immediate problem: Unlike in the $\ell_2$ case, we don’t have prior control on $\norm{U x}_2$ for $x \in B_F(s)$. One can obtain an initial bound using the structure of $U = (A^{\top} W A)^{1/2}$:

    $$ \begin{align} \norm{U x}_2^2 = \sum_{i=1}^m w_i \langle a_i,x\rangle^2 &= \sum_{i=1}^m \norm{U^{-1} a_i}_2^{p-2} \langle a_i,x\rangle^2 \nonumber \\ &= \sum_{i=1}^m \left(\frac{|\langle a_i,x\rangle|}{\norm{U^{-1} a_i}_2}\right)^{2-p} |\langle a_i,x\rangle|^p \leqslant\norm{U x}_2^{2-p} \sum_{i=1}^m |\langle a_i,x\rangle|^p\,, \label{eq:intro-nc0} \end{align}$$

    where the last inequality is Cauchy-Schwarz: $|\langle a_i, x\rangle| = |\langle U^{-1} a_i, U x\rangle| \leqslant\norm{U^{-1} a_i}_2 \norm{U x}_2$. This gives the bound $\norm{U x}_2 \leqslant\norm{Ax}_p \leqslant s^{1/p}$ for $x \in B_F(s)$.

    Problematically, this uniform $\ell_2$ bound is too weak, but there is a straightforward solution: Suppose we cover $B_F(s)$ by translates of $\mathsf K_{j_0}$. This gives an $\ell_{\infty}$ bound on the elements of each cell, meaning that we can apply \eqref{eq:intro-nc0} and obtain a better upper bound on \(\norm{Ux}_2\) for $x \in \mathsf K_{j_0}$. Thus to cover $B_F(s)$ by translates of $\mathsf K_j$ with $j < j_0$, we will cover first by translates of $\mathsf K_{j_0}$, then cover each translate $(x + \mathsf K_{j_0}) \cap B_F(s)$ by translates of $\mathsf K_{j_0-1}$, and so on.

    The standard approach in this setting is to use interpolation inequalities and duality of covering numbers for a cleaner analytic version of such an iterated covering bound. But the iterative covering argument can be adapted to the non-homogeneous setting, as we discuss next.

    Non-homogeneous loss functions

    When we move to more general loss functions $\varphi_i : \mathbb R\to \mathbb R$, we lose the homogeneity property $\varphi_i(\lambda x) = \lambda^p \varphi_i(x)$, $\lambda > 0$ that holds for $\ell_p$ losses. Because of this, we need to replace the single Euclidean structure present in \eqref{eq:wis} (given by the linear operator $U$) with a family of structures, one for every relevant scale.

    • Definition (Approximate weight at scale $s$): Fix $a_1,\ldots,a_m \in \mathbb R^n$ and loss functions $\varphi_1,\ldots,\varphi_m~:~\mathbb R\to \mathbb R_+$. Say that a vector $w \in \mathbb R_+^m$ is an $\alpha$-approximate weight at scale $s$ if

      \[\label{eq:approx-weight} \frac{s}{\alpha} \leqslant\frac{\varphi_i(\norm{M_w^{-1/2} a_i}_2)}{w_i \norm{M_w^{-1/2} a_i}_2^2} \leqslant\alpha s\,,\quad i=1,\ldots,m\,,\text{ where }M_w \mathrel{\mathop:}=\sum_{j=1}^m w_j a_j a_j^{\top}\,.\]

    Moreover, in order to generalize the iterated covering argument for $\ell_p$ losses, we need there to be a relationship between weights at different scales.

    • Definition (Approximate weight scheme): Let $\mathcal J\subseteq \mathbb Z$ be a contiguous interval. A family ${w^{(j)} \in \mathbb R_+^m : j \in \mathcal J}$ is an $\alpha$-approximate weight scheme if each $w^{(j)}$ is an $\alpha$-approximate weight at scale $2^j$ and, furthermore, for every pair $j,j+1 \in \mathcal J$ and $i \in {1,\ldots,m}$,

      \[\label{eq:smooth-weights} w_i^{(j+1)} \leqslant\alpha w_i^{(j)}\,.\]

    Given a weight scheme, we choose sampling probabilities

    $$\rho_i \propto \max_{j \in \mathcal J} w_i^{(j)} \norm{M_{w^{(j)}}^{-1/2} a_i}_2^2 = \max_{j \in \mathcal{J}} \sigma_i(W_j^{1/2} A)\,,\quad i=1,\ldots,m\,,$$

    where \(W_j = \mathrm{diag}(w_1^{(j)},\ldots,w_m^{(j)})\).

    In our setting, $|\mathcal J| \leqslant O(\log(ms_{\max}/s_{\min}))$, which results in the sparsity increasing by a corresponding factor.

    Wed, Nov 22
    Finding multiscale weights
    • Iterative algorithm

        or     Notes as a full page

    Recall the definitions of approximate weights and weight schemes from the previous lecture.

    • Theorem 1: Suppose that $\varphi_1,\ldots,\varphi_m : \mathbb R\to \mathbb R_+$ are lower $\theta$-homogeneous and upper $u$-homogeneous with $u > \theta> 0$ and uniform constants $c, C > 0$. Then there is some $\alpha = \alpha(\theta,c,u,C) > 1$ such that for every choice of vectors $a_1,\ldots,a_m \in \mathbb R^n$ and $s > 0$, there is an $\alpha$-approximate weight at scale $s$.

    This can be proved by considering critical points of the functional $U \mapsto \det(U)$ subject to the constraint $G(U) \leqslant s$, where

    $$G(U) \mathrel{\mathop:}=\varphi_1(\|U a_1\|_2) + \cdots + \varphi_m(\|U a_m\|_2)\,,$$

    analogous to how we showed the existence of Lewis weights.

    • Theorem 2: Suppose that $\varphi_1,\ldots,\varphi_m : \mathbb R\to \mathbb R_+$ are lower $\theta$-homogeneous and upper $u$-homogeneous with $4 > u > \theta> 0$ and uniform constants $c, C > 0$. Then for every choice of vectors $a_1,\ldots,a_m \in \mathbb R^n$, there is an $\alpha$-approximate weight scheme \(\{ w_i^{(j)} : j \in \mathbb Z\}\).

    Contractive algorithm

    For a full-rank matrix $V$ with rows $v_1,\ldots,v_m \in \mathbb R^n$, define the $i$th leverage score of $V$ as \(\label{eq:ls-def} \sigma_i(V) \mathrel{\mathop:}=\langle v_i, (V^{\top} V)^{-1} v_i\rangle\,.\)

    For a weight $w \in \mathbb R_{+}^m$ and \(i \in \{1,\ldots,m\}\), define \(\tau_i(w) \mathrel{\mathop:}=\frac{\sigma_i(W^{1/2} A)}{w_i} = \langle a_i, (A^{\top} W A)^{-1} a_i\rangle\,, \quad W \mathrel{\mathop:}=\mathrm{diag}(w_1,\ldots,w_m)\,,\) and denote $\tau(w) \mathrel{\mathop:}=(\tau_1(w),\ldots,\tau_m(w))$.

    Fix a scale parameter $s > 0$ and define the iteration $\Lambda_s : \mathbb R_+^m \to \mathbb R_+^m$ by

    $$\begin{equation}\label{eq:phi-iter} (\Lambda_s(w))_i \mathrel{\mathop:}=\frac{1}{s} \frac{\varphi_i(\sqrt{\tau_i(w)})}{\tau_i(w)}\,.\end{equation}$$

    Write $\theta^k \mathrel{\mathop:}=\theta \circ \cdots \circ \theta$ for the $k$-fold composition of $\theta$. In this case where $\varphi_i(z)=|z|^p$ and $1 \leqslant p \leqslant 2$, it is known, for $s = 1$, starting from any $w_0 \in \mathbb R_{+}^m$, the sequence \(\{\Lambda_1^k(w_0) : k \geqslant 1\}\) converges to the unique fixed point of $\varphi$, which are the corresponding $\ell_p$ Lewis weights.

    Define now a metric $d$ on $\mathbb R_+^m$ by

    $$\begin{aligned} d(u,w) &\mathrel{\mathop:}=\max \left\{ \left|\log \frac{u_i}{w_i}\right| : i = 1,\ldots,m \right\}.\end{aligned}$$

    We note the following characterization.

    • Fact: A vector $w \in \mathbb R_+^m$ is an $\alpha$-approximate weight at scale $s$ if and only if \(d(w,\Lambda_s(w)) \leqslant\log \alpha\,.\)

    First, we observe that $\tau$ is $1$-Lipschitz on $(\mathbb R_+^m, d)$. In the next proof, $\preceq$ denotes the ordering of two real, symmetric matrices in the Loewner order, i.e., $A \preceq B$ if and only if $B-A$ is positive semi-definite.

    • Lemma: For any $w,w’ \in \mathbb R_{+}^m$, it holds that $d(\tau(w), \tau(w’)) \leqslant d(w,w’)$.

    • Proof: Denote $W = \mathrm{diag}(w), W’ = \mathrm{diag}(w’)$, and $\alpha \mathrel{\mathop:}=\exp(d(w,w’))$. Then $\alpha^{-1} W \preceq W’ \preceq \alpha W$, therefore $\alpha^{-1} A^\top W A \preceq A^\top W’ A \preceq \alpha A^\top W A$, and by monotonicity of the matrix inverse in the Loewner order, $\alpha^{-1} (A^\top W A)^{-1} \preceq (A^\top W’ A)^{-1} \preceq \alpha (A^\top W A)^{-1}$. This implies $d(\tau(w),\tau(w’)) \leqslant\log \alpha$, completing the proof.

    Proof of Theorem 2

    Consider the map $\psi: \mathbb R_+^m \to \mathbb R_+^m$ whose $i$-th coordinate is defined as

    $$\psi_i(x) \mathrel{\mathop:}=\frac{\varphi_i(\sqrt{x_i})}{x_i}\,.$$

    Our assumptions on lower and upper-homogeneity give, for all $y_i \geqslant x_i$,

    $$c \left(\frac{y_i}{x_i}\right)^{\theta/2-1} \leqslant\frac{\varphi_i(\sqrt{y_i})/y_i}{\varphi_i(\sqrt{x_i})/x_i} \leqslant C \left(\frac{y_i}{x_i}\right)^{u/2-1}\,,$$

    yielding, for \(C_1 \mathrel{\mathop:}=\max\{C, 1/c\}\),

    $$\begin{equation}\label{eq:psi-contract} d(\psi(x),\psi(y)) \leqslant\max \left(\left|\frac{\theta}{2}-1\right|, \left|\frac{u}{2}-1\right|\right) d(x,y) + \log(C_1)\,.\end{equation}$$

    Fix $s > 0$ and consider the mapping $\varphi: \mathbb R_+^m \to \mathbb R_+^m$ defined in \eqref{eq:phi-iter} Then for $u< 4$ and $\delta \mathrel{\mathop:}=\max \left(\left|\frac{\theta}{2}-1\right|, \left|\frac{u}{2}-1\right|\right) < 1$, in conjunction with \eqref{eq:psi-contract}, shows that

    $$\begin{aligned} d(\Lambda_s(w), \Lambda_s(w')) < \delta\,d(w,w') + \log(C_1)\,. \label{eq:onestep}\end{aligned}$$

    Applying this bound inductively, for any weight $w \in \mathbb R_{+}^m$ and $k \geqslant 1$, we have

    $$\begin{equation}\label{eq:iter-contract0} d\left(\Lambda_s^{k}(w), \Lambda_s^{k+1}(w)\right) \leqslant\frac{\delta^k d(\Lambda_s(w),w) + \log C_1}{1-\delta}\,,\end{equation}$$

    Now define \(w^{(0)} \mathrel{\mathop:}=\varphi^{k}_{1}(1,\ldots,1)\,,\) where $k \geqslant 1$ is chosen large enough so that $d(w^{(0)}, \Lambda_{1}(w^{(0)})) \leqslant\frac{2 \log C_1}{1-\delta}$. From , one sees that $w^{(0)}$ is an $\alpha$-approximate weight at scale $1$ for $\alpha = C_1^{2/(1-\delta)}$.

    Define inductively, for $j =1,2,\ldots$,

    $$\begin{aligned} w^{(j)} &\mathrel{\mathop:}=\Lambda_{2^j}(w^{(j-1)}) \\ w^{(-j)} &\mathrel{\mathop:}=\Lambda_{2^{-j}}(w^{(1-j)})\,.\end{aligned}$$

    Note that

    $$\begin{aligned} d(\Lambda_{2^j}(w^{(j)}), w^{(j)}) &= d(\Lambda_{2^j}^2(w^{(j-1)}), \Lambda_{2^j}(w^{(j-1)})) \\ &\leqslant\delta d(\Lambda_{2^j}(w^{(j-1)}), w^{(j-1)}) + \log(C_1) \\ &\leqslant\delta d(\Lambda_{2^{j-1}}(w^{(j-1)}), w^{(j-1)}) + \delta \log(2) + \log(C_1)\,,\end{aligned}$$

    where the last inequality uses $\Lambda_{2s}(w) = 2 \Lambda_s(w)$ for all $w \in \mathbb R_+^m$.

    Therefore, by induction, $d(\Lambda_{2^j}(w^{(j)}), w^{(j)}) \leqslant\frac{2 \log(C_1) + \log 2}{1-\delta}$ for all $j > 0$. To see that the family of weights \(\{ w^{(j)} : j \in \mathbb Z\}\) forms a weight scheme, note that

    $$d(w^{(j)}, w^{(j-1)}) = d(\Lambda_{2^j}(w^{(j-1)}), w^{(j-1)}) \leqslant d(\Lambda_{2^{j}}(w^{(j-1)}), w^{(j-1)}) + \log 2\,,$$

    thus \(\{w^{(j)} : j \in \mathbb Z\}\) is an $\alpha$-approximate weight scheme for $\alpha = \frac{2 \log(2C_1)}{1-\delta}$, completing the proof.

    Active regression
    Mon, Nov 27
    Grounded approximations
    Wed, Nov 29
    Analysis of $\gamma_2$
    Operator-valued sparsification
    Mon, Dec 04
    BSS vs Ito
    • Spectral sparsifiers
    • Iterative sparsification
    • The barrier potential

    Handwritten notes from video

    Wed, Dec 06
    Analyzing the potential change
    • Brownian motion
    • Ito's Lemma
    • Differentiating the barrier potential

        or     Notes as a full page

    These are preliminary notes for analyzing the Ito process.

    Reis and Rothvoss gave an algorithm for finding BSS sparsifiers that uses a a discretized random walk. It appears conceptually simpler to use a modified Brownian motion directly.

    Consider symmetric matrices $A_1,\ldots,A_m \succeq 0$ with $|A_1|+\cdots+|A_m| \preceq \mathbb{I}_n$. Define a stochastic process $X_t = (X_t^1,\ldots,X_t^m)$ by $X_0 = 0$, and

    $$dX_t = \mathbb{I}_{\mathcal J_t}\, dB_t\,,$$

    where \(\{B_t\}\) is a standard Brownian motion and \(\mathcal J_t \subseteq [m]\) is the set of good coordinates at time \(t\).

    Define the stopping times \(T_i \mathrel{\mathop:}=\inf \{ t > 0 : |X_t^i| \geqslant 1 \}\) and the set of stopped coordinates

    $$\mathcal S_t \mathrel{\mathop:}=\{ i \in [m] : T_i \leqslant t \}\,.$$

    We will ensure that \(\mathcal J_t \cap \mathcal S_t = \emptyset\) so that \(|X_t^1|,\ldots,|X_t^m| \leqslant 1\) holds for all times \(t \geqslant 0\).

    Denote \(T \mathrel{\mathop:}=\sup \{ t > 0 : |\mathcal S_t| \leqslant m/2 \}\). Our goal is to show that with good probability,

    $$\label{eq:goal} \sum_{i=1}^m X_T^i A_i \preceq C \left(\frac{n}{m}\right)^{1/2} \mathbb{I}_n\,.$$

    Since the stopping time \(T\) entails that \(m/2\) coordinates have been fixed to \(\pm 1\), this completes the argument since at least one of the two matrices \(\sum_{i=1}^m (1+X_T^i) A_i\) or \(\sum_{i=1}^m (1-X_T^i) A_i\) has at most \(m/2\) non-zero coefficients, and

    $$\sum_{i=1}^m (1 \pm X_T^i) A_i \preceq \left(1 + C \left(\frac{n}{m}\right)^{1/2}\right) \mathbb{I}_n\,.$$

    Repeatedly halving \(m\) until \(m \asymp n/\varepsilon^2\) yields a \(1 \pm \varepsilon\) approximation with \(O(n/\varepsilon^2)\) sparsity. To get a two-sided bound, replace \(A_i\) by \(\begin{pmatrix} A_i & 0 \\ 0 & - A_i\end{pmatrix}\).

    To this end, let us define, for parameters $\theta,\lambda > 0$ to be chosen later,

    $$\begin{aligned} U(x) &\mathrel{\mathop:}=\theta \mathbb{I}_n + \lambda \|x\|^2 \mathbb{I}_n - \sum_{i=1}^m x_i A_i \\ \Phi(x) &\mathrel{\mathop:}=\mathop{\mathrm{tr}}(U(x)^{-1})\,.\end{aligned}$$

    Define $U_t \mathrel{\mathop:}=U(X_t)$. Note that $\Phi(X_0)=\Phi(0) = n/\theta$ and $U_0 \succ 0$. The idea is that if ${ \Phi(X_t) : t \in [0,T] }$ remains bounded, then $U_t \succ 0$ holds for $t \in [0,T]$, and therefore at time $T$,

    $$\sum_{i=1}^m X_T^i A_i \preceq (\theta + \lambda \|X_T\|^2) \mathbb{I}_n \preceq (\theta + \lambda m)\mathbb{I}_n\,.$$

    From Ito’s lemma, we know that if $dX_t = \Sigma_t dB_t$ and $f : \mathbb R^m \to \mathbb R$, then

    $$d f(X_t) = \langle \nabla f(t,X_t), dX_t\rangle + \frac12 \mathop{\mathrm{tr}}\left(\nabla^2 f(t,X_t) \Sigma_t \Sigma_t^{\top}\right) dt\,.$$

    Since $\Sigma_t = \mathbb{I}_{\mathcal J_t}$,

    $$\frac12 \mathop{\mathrm{tr}}\left(\nabla^2 f(t,X_t) \Sigma_t \Sigma_t^{\top}\right) dt = \frac12 \sum_{i \in \mathcal J_t} \partial_{x_i}^2 f(t,X_t).$$

    Now denote $f(x) \mathrel{\mathop:}=\mathop{\mathrm{tr}}(U(x)^{-1})$ and calculate:

    $$\begin{aligned} \partial_{x_i} f(x) &= - 2 \lambda x_i \mathop{\mathrm{tr}}(U(x)^{-2}) + \mathop{\mathrm{tr}}(U(x)^{-2} A_i) \\ \partial_{x_j} \partial_{x_i} f(x) &= - 2 \lambda \mathbf{1}_{\{i=j\}} \mathop{\mathrm{tr}}(U(x)^{-2}) + 8 \lambda^2 x_i x_j \mathop{\mathrm{tr}}(U(x)^{-3}) - 4 \lambda x_i \mathop{\mathrm{tr}}(U(x)^{-3} A_j) + \partial_{x_j} \mathop{\mathrm{tr}}(U(x)^{-2} A_i) \\ &= - 2 \lambda \mathbf{1}_{\{i=j\}} \mathop{\mathrm{tr}}(U(x)^{-2}) + 8 \lambda^2 x_i x_j \mathop{\mathrm{tr}}(U(x)^{-3}) - 4 \lambda x_i \mathop{\mathrm{tr}}(U(x)^{-3} A_j) \\ &\quad - 4 \lambda x_j \mathop{\mathrm{tr}}(U(x)^{-3} A_i) + 2 \mathop{\mathrm{tr}}\left(U(x)^{-1} A_i U(x)^{-1} A_j U(x)^{-1}\right).\end{aligned}$$

    This gives

    $$\begin{aligned} d \Phi(X_t) = d f(X_t) &= {\color{Blue}{- 2 \lambda \langle X_t, d X_t\rangle \mathop{\mathrm{tr}}(U_t^{-2}) + \sum_{i=1}^m \mathop{\mathrm{tr}}(U_t^{-2} A_i) dX_t^i}} + \frac12 \mathop{\mathrm{tr}}\left(\nabla^2 f(t,X_t) \Sigma_t \Sigma_t^{\top}\right)\,dt \\ \frac12 \mathop{\mathrm{tr}}\left(\nabla^2 f(t,X_t) \Sigma_t \Sigma_t^{\top}\right) &= {\color{Maroon}{- \lambda \mathop{\mathrm{tr}}(U_t^{-2}) |\mathcal J_t|}} - {\color{ForestGreen}{4 \lambda \sum_{i \in \mathcal J_t} X_t^i \mathop{\mathrm{tr}}\left(U_t^{-3} A_i\right)}} + {\color{ForestGreen}{4 \lambda^2 \|\mathbb{I}_{\mathcal J_t} X_t\|^2 \mathop{\mathrm{tr}}(U_t^{-3})}} \\ &\quad {\color{Maroon}{+ \sum_{i \in \mathcal J_t} \mathop{\mathrm{tr}}\left(U_t^{-1} A_i U_t^{-1} A_i U_t^{-1}\right)}}\end{aligned}$$

    The blue terms are the martingales, which we ignore for the moment. The green terms are error terms. The two red terms need to be balanced by choosing $\theta,\lambda$ appropriately.

    For any $A,B$ with singular values $\sigma_1(A) \geqslant\cdots \geqslant\sigma_n(A)$ and $\sigma_1(B) \geqslant\cdots \geqslant\sigma_n(B)$, we have

    $$\mathop{\mathrm{tr}}(A B) \leqslant\sum_{i=1}^n \sigma_i(A) \sigma_i(B)\,.$$

    In particular, if $A,B \succeq 0$, then $\mathop{\mathrm{tr}}(AB) \leqslant\mathop{\mathrm{tr}}(A) \mathop{\mathrm{tr}}(B)$.

    Let us use this to bound the error terms

    $$\begin{aligned} {\color{ForestGreen}{4 \lambda^2 \|\mathbb{I}_{\mathcal J_t} X_t\|^2 \mathop{\mathrm{tr}}(U_t^{-3})}} &\leqslant 4 \lambda^2 m \mathop{\mathrm{tr}}(U_t^{-2}) \mathop{\mathrm{tr}}(U_t^{-1}) \\ {\color{ForestGreen}{4 \lambda \sum_{i \in \mathcal J_t} X_t^i \mathop{\mathrm{tr}}(U_t^{-3} A_i)}} &\leqslant 4 \lambda (\theta+\lambda m) \mathop{\mathrm{tr}}(U_t^{-2}) \mathop{\mathrm{tr}}(U_t^{-1})\,.\end{aligned}$$

    This fact also bounds, for any $U \succ 0$ and symmetric $V$,

    $$\mathop{\mathrm{tr}}\left(U^{-1} V U^{-1} V U^{-1}\right) \leqslant\mathop{\mathrm{tr}}(U^{-2} |V|) \mathop{\mathrm{tr}}(U^{-1} |V|)\,.$$

    Accordingly, we define the set of bad coordinates:

    $$\mathcal I_t \mathrel{\mathop:}=\left\{ i \in [m] : \mathop{\mathrm{tr}}(U_t^{-1} |A_i|) > \frac{\gamma}{m} \mathop{\mathrm{tr}}(U_t^{-1}) \right\}\,,$$

    and finally $\mathcal J_t \mathrel{\mathop:}=[m] \setminus (\mathcal I_t \cup \mathcal S_t)$.

    Observe that

    $$\sum_{i \in \mathcal J_t} \mathop{\mathrm{tr}}\left(U_t^{-1} A_i U_t^{-1} A_i U_t^{-1}\right) \leqslant\frac{\gamma}{m} \mathop{\mathrm{tr}}(U_t^{-1}) \sum_{i=1}^m \mathop{\mathrm{tr}}(U_t^{-2} |A_i|) \leqslant\frac{\gamma}{m} \mathop{\mathrm{tr}}(U_t^{-1}) \mathop{\mathrm{tr}}(U_t^{-2})\,,$$

    where the last inequality uses $|A_1|+\cdots+|A_m| \preceq \mathbb{I}_n$.

    The point is to maintain $\Phi(X_t) \leqslant 2 \Phi_0(X_0) = 2 n/\theta$ by proving that $d \Phi(X_t) < 0$. Calculate:

    $$\begin{aligned} d \Phi(X_t) &\leqslant\mathop{\mathrm{tr}}(U_t^{-2}) \left(- \lambda |\mathcal J_t| + \gamma \mathop{\mathrm{tr}}(U_t^{-1})\right) \\ &=\mathop{\mathrm{tr}}(U_t^{-2}) \left(- \lambda |\mathcal J_t| + \frac{\gamma}{m} \Phi(X_t)\right) \\ &\leqslant\mathop{\mathrm{tr}}(U_t^{-2}) \left(- \lambda |\mathcal J_t| + \frac{2 \gamma n}{\theta m}\right).\end{aligned}$$

    For $\gamma \mathrel{\mathop:}=8$, we have $|\mathcal J_t| \geqslant m - |\mathcal I_t| - |\mathcal S_t| \geqslant\frac{m}{2} - \frac{m}{\gamma} \geqslant\frac{m}{4}$. So let us take

    $$\theta \mathrel{\mathop:}=4 \left(\frac{n}{m}\right)^{1/2},\ \lambda \mathrel{\mathop:}=\frac{4}{m} \theta\,.$$

    If we maintain $\max_{t \in [0,T]} \Phi(X_t) \leqslant 2n/\theta$, it means that $U_T \succ 0$, so

    $$\sum_{i=1}^m X_T^i A_i \preceq \left(\theta + \lambda \|X_T\|^2\right) \mathbb{I}_n \preceq 5 \theta \mathbb{I}_n \asymp \left(\frac{n}{m}\right)^{1/2} \mathbb{I}_n\,,$$

    completing the proof.