Learning With Errors

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Deterministic Sparsification


Let $G$ be a dense graph. A sparse graph $H$ is a sparsifier of $G$ approximation of $G$ that preserves certain properties such as quadratic forms of its Laplacian. This post will formally define spectral sparsification, then present the intuition behind the deterministic construction of spectral sparsifiers by Baston, Spielman and Srivastava [BSS08].

Benczúr and Karger [BK96] introduced the cut sparsifier, which ensures that the value of all cuts in $H$ approximates that of all cuts in $G$:

Definition 1 (Cut Sparsification): A weighted undirected graph $H = (V, E_H)$ is an $\epsilon$-cut sparsifier of a weighted undirected graph $G = (V, E_G)$ if for all $S \subset V$,

Where $E_G$ and $E_H$ are the sums of edge weights crossing the cuts in $G$ and $H$ respectively.

Spielman and Teng [ST08] introduced another notion of graph sparsification with the quadratic form of the Laplacian:

Definition 2 (Spectral Sparsification): A weighted undirected graph $H = (V, E_H)$ is an $\epsilon$-spectral sparsifier of a weighted undirected graph $G = (V, E_G)$ if for all $x \in \mathbb{R}^{|V|}$,

Where $L_G$ and $L_H$ are the graph Laplacians of $G$ and $H$ respectively.

Cut sparsifiers can be used in approximating max-flow (via the max-flow min-cut theorem) and spectral sparsifiers are a key ingredient in solving Laplacian inear systems in near linear time.

Note that this stronger than cut sparsification, as we can fix $x$ to be the indicator vectors of cuts to obtain definition 1. Also note that this notion of sparsifiers also provides bounds on Laplacian eigenvalues, and thus spectral sparsifiers of complete graphs are also expanders. These sparsifiers can be constructed in a randomized manner by sampling edges proportional to their effective resistance [SS08], but in this post we will focus on a deterministic construction presented in [BSS08], as stated more precisely in the following theorem:

Theorem 1: For every $d > 1$, every undirected graph $G = (V, E)$ on $n$ vertices contains a weighted subgraph $H = (V, F, \tilde{w})$ with $\lceil d(n-1) \rceil$ edges that satisfies:


The Laplacian $L$ of a graph can be seen as a linear transformation relating the flow and demand in an electrical flow on the graph where each edge has unit resistance. Let $B \in \mathbb{R}^{m \times n}$ be the vertex-edge incidence matrix and $W$ is a diagonal matrix of edge weights, then $L = B^TWB$. $L$ also has a pseudoinverse which acts like an actual inverse for all vectors $x \bot \mathbb 1$, resulting from solving an electrical flow on $G$.

Let $\kappa = \frac{d+1+2\sqrt d}{d+1-2\sqrt d}$, we assume that the graph is connected thus $x \bot \mathbf{1}$, and perform a transformation on the condition in Theorem 1:

Let $b_{e = (u, v)} = \mathbf{1}_u - \mathbf{1}_v$ be a row of incidence matrix $B$ , $s_e$ be the weight of edge $e$ in $E_H$ and $A \succeq B$ when $A - B$ is a psd matrix. Then the above condition can be rewritten as:

We then define a vector $v_e = L_G^{-1/2}b_e^T$ for each $e \in E_G$. Notice that over all edges of $G$, the rank 1 matrices formed by $v_eV_e^T$ sum to the identity matrix:

Then equation 1 can be interpreted as choosing a sparse subset of the edges in $G$, as well as weights $s_e$, so that the matrix obtained by summing over the edges of $H$, $\sum_{e \in E_H} s_e v_ev_e^T$, has a low condition number (ratio between the largest and smallest eigenvalues):

If we can find such a sparse set of edges and weights, then we have proved Theorem 1. In [SS08] this was done by randomly sampling these rank-1 matrices based on their effective resistances of their corresponding edges, using a distribution that has the identity matrix as the expectation. Convergence is shown using a matrix concentration inequality. The construction in [BSS08] deterministically chooses each $v_e$ and $s_e$, bounding the increase in $\kappa$ in each step using barrier functions. One useful lemma for this procedure is:

Lemma 1 (Matrix Determinant Lemma): If $A$ is nonsingular and $v$ is a vector, then:

Main Proof

Recall from the previous section the main theorem that need to be proved is:

Theorem 2: Suppose $d > 1$ and $v_1, \cdots, v_m$ are vectors in $\mathbb{R}^n$ with Then there exist scalars $s_i > 0$ with $|{i: s_i \ne 0 }| \le dn$ so that

This is equivalent to bounding the ratio of $\lambda_{\min}$ and $\lambda_{\max}$ of the matrix $\sum_{i \le m} s_i v_iv_i^T$.

We start with a matrix $A = 0$, and build it by adding rank-1 updates $s_ev_ev_e^T$. One interesting fact is that for any vector $v$, the eigenvalues of $A$ and $A + vv^T$ interlace. Consider the characteristic polynomial of $A + vv^T$:

Which can be written in terms of the characteristic polynomial of $A$ using Lemma 1. $u_j$ are the eigenvectors of $A$. Let $\lambda$ be a zero of $p_{A + vv^T}(x)$. It can either:

  1. Be a zero of $p_A(x)$, so $\lambda$ is equal to an eigenvalue $\lambda_i$ of $A$, and the corresponding eigenvector $u_i$ is orthogonal to $v$. In this case, this eigenvalue doesn’t move.
  2. Strictly interlace with the old eigenvalues. This happens when $p_A(\lambda) \ne 0$ and This can be interpreted with a physical model. Consider $n$ positive charges arranged vertically with the $j$-th charge’s position corresponding to the $j$-th eigenvalue of $A$, and its charge is $\dotp{v, u_j}^2$. The points where the electric potential is 1 are the new eigenvalues. Since between any two charges the potential changes direction from $+ \infty$ to $- \infty$, there has to be a point between every two charges where the potential is 1, thus the new eigenvalues strictly interlace the old ones.

To get some intuition, we see what happens when we sample $v_i$ uniformly randomly. Since $\sum_j v_jv_j^T = I$, $\mathbb{E}_v[\dotp{v, u}^2]$ is constant for any normalized vector $u$. Therefore adding the average $v$ increases the charges by the same amount in the physical model, causing the new eigenvalues to all increase by the same amount. Informally, we expect all the eigenvalues to “march forward” at similar rates with each $vv^T$ added, so $\lambda_{\max} / \lambda_{\min}$ is bounded.

We construct a sequence of matrices $A^{(0)}, \cdots, A^{(q)}$ by adding rank-1 updates $t vv^T$. To bound the condition number after each update, we create two barriers $l < \lambda_{\min}(A) < \lambda_{\max}(A) < u$ so that the eigenvalues of $A$ lie between them. $\Phi_l(A)$ and $\Phi^u(A)$ are defined as the potentials at the barriers respectively:

The crucial step is to show that there exists a $v_i$ and $t$ so that we can add $t v_i v_i^T$ to $A$, so that each barrier is shifted by a constant, and the potentials at each barrier doesn’t change. We will sketch out the proof briefly, readers can pursue the details in [BSS08].

Let constants $\delta_U$ and $\delta_L$ be the maximum amount each barrier can increase each round, and constants $\epsilon_U = \Phi^{u_0}(A^{(0)})$ and $\epsilon_L = \Phi_{l_0}(A^{(0)})$ be the initial potentials at each barrier. The first lemma shows that if $t$ is not too large, adding $t vv^T$ to $A$ and shifting the upper barrier by $\delta_U$ will not increase the upper potential $\Phi^u$.

Lemma 2 (Upper Barrier Shift): Suppose $\lambda_{\max}(A) < u$, and $v$ is any vector. If Then:

The second lemma shows that if $t$ is not too small, adding $t vv^T$ to $A$ and shifting the lower barrier by $\delta_L$ will not increase the lower potential $\Phi^u$.

Lemma 3 (Lower Barrier Shift): Suppose $\lambda_{\min}(A) > l$, $\Phi_l(A) \le 1/\delta_L$ and $v$ is any vector. If Then:

Finally, it can be shown that there exists a $t$ and $v_i$ that satisfy the conditions of the above lemmas.

Lemma 3 (Both Barriers): If $\lambda_{\max}(A) < u$, $\lambda_{\min}(A) > l$, $\Phi^u(A) \le \epsilon_U$, $\Phi_l(A) \le \epsilon_L$, and $\epsilon_U$ , $\epsilon_L$, $\delta_U$, $\delta_L$ satisfy: then there exists a $v_i$ and positive $t$ for which

This is proved by an averaging argument relating the behavior of vector $v$ to the behavior of the expected vector, showing that therefore there exists a $i$ for which there is a gap between $L_A(v_i) - U_A(v_i)$. Choosing the constants carefully, we can get the required bound on the condition number.


There is a similarity between Theorem 2 and the Kadison-Singer conjecture. One formulation of it is stated below:

Proposition 1: There are universal constants $\epsilon, \delta > 0$ and $r \in \mathbb N$ for which the following statement holds. If $v_1, \cdots, v_m \in \mathbb{R}^n$ satisfy $||v_i|| \le \delta$ for all $i$ and then there is a partition $X_1, \cdots X_r$ of ${1, \cdots, m }$ for which for every $j = 1, \cdots, r$.

This conjecture was positively resolved in [MSS13], using techniques arising from generalizing the barrier function argument used to prove Theorem 2 to a multivariate version.


[BK96] A. A. Benczúr and D. R. Karger. Approximating s-t minimum cuts in $\tilde{O}(n^2)$ time. In STOC ‘96, pages 47-55, 1996.

[BSS08] J. Baston, D. A. Spielman and N. Srivastava. Twice-Ramanujan Sparsifiers. Available at http://arxiv.org/abs/0808.0163, 2008.

[MSS13] A. W. Marcus, D. A. Spielman and N. Srivastava. Interlacing Families II: Mixed Characteristic Polynomials and The Kadison-Singer Problem. Available at http://arxiv.org/abs/1306.3969, 2013.

[SS08] D. A. Spielman and N. Srivastava. Graph Sparsification by Effective Resistances. In STOC ‘08, pages 563-568, 2008.

[ST08] D. A. Spielman and S.-H. Teng. Spectral Sparsification of Graphs. Available at http://arxiv.org/abs/0808.4134, 2008.