# The Subspace Enumeration Method

In this post, we briefly explain the main idea of [CDKP23], which uses a novel spectral method named "** subspace enumeration**" to solve the extensively studied task of approximately counting the number of independent sets on a given bipartite graph \(G\).

The purpose of this post is only to sketch the proof (and its novel idea) in an easy-to-understand way. For more details, please refer to the original paper.

## Overview

The problem setting we will discuss about in this post is \(d\)-regular (and connected WLOG) bipartite graph \(G = (X, Y, E)\) where \(d = \delta n\).

First we describe some common approaches towards counting the number of independent sets \(i(G)\) on \(G\). For a possible independent set \(I\), enumerating its \(X\)-part \(I_X := I \cap X\) gives \[ i(G) = \sum_{A \subseteq X} 2^{|Y \backslash N(A)|} = 2^{|Y|} \sum_{A \subseteq X} 2^{-|N(A)|}. \]

Since there might be many subsets \(A \subseteq X\) having the same neighborhood, it seems a more efficient way to enumerate \(B \subseteq Y\) and compute the contribution of those having \(B\) as (exact) neighborhood together. Denote the collection of these subsets by \(\mathcal{N}_B\), then \(\left\langle \mathcal{N}_B, \subseteq \right\rangle\) is a ** join-semilattice**, implying there exists a greatest element \(A_B \in \mathcal{N}_B\) s.t. every \(A \subseteq X\) with \(N(A) = B\) is a subset of \(A_B\). We call such subsets

*closed*, and the minimum closed subset containing \(A\) the

*closure*of \(A\), denoted by \([A]\). Now we enumerate closed subsets (of \(X\)) instead of subsets of \(Y\), which may contain many "useless" ones (i.e. \(\mathcal{N}_B = \varnothing\)): \[ i(G) = 2^{|Y|} \sum_{A \subseteq X\ \text{closed}} \mathcal{D}_A \cdot 2^{-|N(A)|}, \quad \mathcal{D}_A := |\mathcal{N}_{N(A)}|. \]

Trying to proceed further, we will soon find that two vertices \(x, y \in A\) may be "**correlated**" (i.e., have common neighbors), which leads to a difference between \(|N(A)|\) and \(\sum_{u \in A} |N(u)|\). A natural attempt to resolve the term \(|N(A)|\) is to consider sets of vertices with no correlation between separately. Formally, we say two vertices \(u, v \in V\) are *2-linked* if \(\exists w \in V: u \sim w \sim v\). A 2-linked subset \(A \subseteq X\) is called a *polymer*, and polymers are *compatible* if they are not 2-linked (i.e., not correlated). Let \(\mathcal{P}(X)\) be all polymers in \(2^X\). With this characterization of correlated vertices, we can uniquely partition \(A\) into compatible polymers (also abbreviated as *components* in below, denoted by \(\mathcal{K}(A)\)) \(A_1, \ldots, A_k\) s.t. \(|N(A)| = \sum_{i \in [k]} |N(A_i)|\). Observing that \(A\) is closed iff all its components are closed, \[ \begin{aligned}
i(G) &= 2^{|Y|} \sum_{\substack{A \subseteq X\ \text{with} \\ \text{all components closed}}} \mathcal{D}_A \cdot 2^{-|N(A)|} = 2^{|Y|} \sum_{k \geq 0} \sum_{\substack{\left\lbrace A_1, \ldots, A_k \right\rbrace \subseteq \mathcal{P}(X) \\ \text{compatible and all closed}}} \prod_{i \in [k]} \mathcal{D}_{A_i} \cdot 2^{-|N(A_i)|}, \\
\mathcal{D}_A &:= |\mathcal{N}_{N(A)}| = \prod_{K \in \mathcal{K}(A)} |\mathcal{N}_{N(K)}|.
\end{aligned} \]

A standard step then is to make a discussion on the **expansion** of (the components of) \(A\). We call a subset \(A \subseteq X\) *\(t\)-expanding* if \(|N(A)| = |[A]| + t\), and correspondingly *\(t\)-contracting* if \(|N(A)| < |[A]| + t\). The subsets with stronger expansion tend to have (exponentially) smaller weights, so we can only enumerate the \(t_0\)-contracting polymers (which contributes the main part), then approximate the sum of \(t_0\)-expanding polymers' weights. With the "threshold parameter" \(t_0\) properly picked to ensure a provably reasonable deduction, \[ \begin{aligned}
i(G) &= 2^{|Y|} \sum_{\substack{A \subseteq X\ \text{with} \\ \text{all components closed} \\ \text{and $t_0$-contracting}}} \mathcal{D}_A \cdot 2^{-|N(A)|} \cdot \Xi_A, \\
\Xi_A &:= \sum_{k \geq 0} \sum_{\substack{\left\lbrace B_1, \ldots, B_k \right\rbrace \subseteq \mathcal{P}(X \backslash N^2(A)) \\ \text{compatible and all not $t_0$-contracting}}} 2^{-\sum_{i \in [k]} |N(B_i)|}.
\end{aligned} \]

With an additional notation \(\mathcal{A}(G)\) denoting the collection of all \(A \subseteq X\) with all components closed and \(t_0\)-contracting, we have the final form \[ i(G) = 2^{Y} \sum_{A \in \mathcal{A}(G)} \mathcal{D}_A \cdot 2^{-|N(A)|} \cdot \Xi_A. \]

Now the task of computing \(i(G)\) splits into three parts:

- enumerate \(A \in \mathcal{A}(G)\),
- (approximately) compute \(\mathcal{D}_A\) for all possible \(A \in \mathcal{A}(G)\),
- (approximately) compute \(\Xi_A\) for all possible \(A \in \mathcal{A}(G)\).

The last two parts are mostly identical to that in [JPP21] and do not introduce much novelty, so here we omit the details (maybe I will write notes about [JPP21] and complete these parts in the future) and focus on the first one. To enumerate \(A \in \mathcal{A}(G)\) efficiently, the *subspace enumeration* technique is introduced. As a brief outline, the proof consists of three pieces:

- every \(A \in \mathcal{A}(G)\) corresponds to a small cut,
- a collection \(\mathcal{C}^{\rm cut}\) of "principal" cuts can be efficiently constructed using subspace enumeration s.t. every small cut is close to some "principal" cut,
- given a "principal" cut \(C \in \mathcal{C}^{\rm cut}\), we can enumerate those small cuts being close to \(C\).

By the term *cut* we refer to a subset \(C \subseteq V = X \cup Y\), with its *value* \(|\nabla(C)| := |(C \times (V \backslash C)) \cap E|\) defined as the number of edges with precisely one endpoint in \(C\).

The first part holds naturally by setting cut \(S_A = A \cup N(A)\), where \(|\nabla(S_A)| \leq t d\) since every \(A \in \mathcal{A}(G)\) is \(t\)-contracting.

## The Subspace Enumeration Method

The second part starts from a spectral observation: \(|\nabla(C)|\) can be written as \(\vec{c}^T L \vec{c}\), where \(\vec{c}\) denotes the indicator vector of \(C\) with ground set \(V\), and \(L = dI-A\) denotes the Laplacian matrix of \(G\). For a \(d\)-regular \(G\), \[ \operatorname{Tr}(A^2) = Nd = \sum_{i \in [N]} \lambda_i^2, \] thus by an averaging argument, at most \(4N/d\) eigenvalues of \(A\) have absolute value \(\geq d/2\). By the symmetry of the spectrum of \(A\) about 0, at most \(4n/d\) eigenvalues of \(L\) are \(\leq d/2\). We denote the precise amount by \(k\), and let \(E = \left\lbrace \vec{e}_1, \ldots, \vec{e}_N \right\rbrace\) be an orthonormal basis of eigenvectors of \(L\) with corresponding eigenvalues sorted: \[ 0 = \mu_1 \leq \cdots \leq \mu_k \leq \frac{d}{2} < \mu_{k+1} \leq \cdots \leq \mu_{N-k} < \frac{3d}{2} \leq \mu_{N-k+1} \leq \cdots \leq \mu_N = 2d. \]

Abusing notations slightly, for a set (or cut) labeled by a capital letter, we use the same letter in lowercase for its indicator vector and its coordinates over \(E\) (e.g., \(\vec{s}= \sum_{i \in [N]} s_i \vec{e}_i\) is the indicator vector of set \(S\)). Let \(U = \operatorname{span}(\left\lbrace \vec{e}_1, \ldots, \vec{e}_k \right\rbrace)\), intuitively for every small cut \(S\) with \(|\nabla(S)| \leq td\), \(\vec{s}\) is close to its projection \(\vec{u}\) onto \(U\). (Here we take \(d\) as a "unit" of measure because \(G\) is \(d\)-regular, and of course \(t \geq 1\) since even the smallest cut of \(G\) will have value \(\geq d\).) To be precise, \[ td \geq |\nabla(S)| = \vec{s}^T L \vec{s}= \sum_{i \in [N]} \mu_i s_i^2 \geq \frac{d}{2} \sum_{i=k+1}^N s_i^2 = \frac{d}{2} \bigl\lVert \vec{s}-\vec{u} \bigr\rVert_2^2, \] which implies \(\left\lVert \vec{s}-\vec{u} \right\rVert_2 \leq \sqrt{2t}\).

Now the goal becomes constructing a collection of cuts "dominating" \(U\). In [CDKP23], a brute-force method is chosen since (WLOG) a cut \(S\) has \(L^2\)-norm \(\left\lVert \vec{s} \right\rVert_2 \leq \sqrt{n}\): taking an \(\epsilon\)-net \[ \mathcal{E}:= \left\lbrace \vec{p}= \sum_{i \in [k]} x_i \vec{e}_i : x_1, \ldots, x_k \in \frac{\epsilon}{\sqrt{k}} \mathbb{Z}, \left\lVert \vec{p} \right\rVert_2 \leq \sqrt{n} \right\rbrace \] is acceptable by bounding its size with \(|\mathcal{E}| \leq (2\sqrt{nk} / \epsilon)^k = n^{O(1/\delta)}\). But it's not sufficient since the lattice points in \(\mathcal{E}\) may be not an indicator vector (i.e. 0-1 vector). Fortunately, simply rounding them to the nearest 0-1 vector (breaking ties arbitrarily is OK, and we choose \(1/2 \mapsto 1\) for consistency) will not introduce much error: suppose the nearest lattice point to \(\vec{u}\) is \(\vec{p}\) and \(p\) is rounded to \(\vec{c}\), we know \(\vec{c}\) is closer to \(\vec{p}\) than \(\vec{s}\) (noticing that \(\vec{s}\) is also an 0-1 vector), thus \(\left\lVert \vec{c}-\vec{p} \right\rVert_2 \leq \left\lVert \vec{s}-\vec{p} \right\rVert_2 \leq \sqrt{2t} + \epsilon\) by the triangle inequality, and furthur \(\left\lVert \vec{c}-\vec{s} \right\rVert_2 \leq 2\sqrt{2t} + 2\epsilon\). Let \(C\) be the cut corresponding to \(\vec{c}\) and pick \(\epsilon= \sqrt{2}\), \[ \left\lvert S \triangle C \right\rvert = \left\lVert \vec{s}-\vec{c} \right\rVert_2^2 \leq 8 (\sqrt{t} + 1)^2 \leq 32t. \] Also \(C\) is a small cut: \[ |\nabla(C)| \leq |\nabla(S)| + d\left\lvert S \triangle C \right\rvert \leq td + 32td = 33td. \]

Here we finsh the second part and state the lemma:

This lemma is somehow similar to the graph container method: set of representatives "dominating" all objects with certain properties exists and can even be constructed.

### Bounded (and Algorithmic) Dominance of "Principal" Cuts

In this section we show the third part. Let \(C_X := C \cap X\) be the \(X\)-part of "principal" cut \(C\) and \(C_Y\) likewise, our goal is to enumerate all small cuts \(S_A\) corresponding to some \(A \in \mathcal{A}(G)\) and is "dominated" by \(C\) (in particular, \(|S_A \triangle C| \leq 32t\) as stated in Lemma 2.1). For convience we relax the condition and get \(\max(|A \triangle C_X|, |N(A) \triangle C_Y|) \leq 32t\).

A straightforward observation is that every vertex in \(C_Y\) having \(>32t\) neighbors inside \(C_X\) is in \(N(A)\) (more precisely, \(N(A_X)\) where \(A_X := A \cap C_X\)), thus we can reduce the range of \(A\) by enumerating subsets \(S'_Y\) of \(S_Y := \left\lbrace v \in C_Y: |N(v) \cap C_X| \leq 32t \right\rbrace\) and exclude \(N(S'_Y)\) from \(C_X\).

Now we have a restriction on \(A_X\). Moreover, since \(|\nabla(C)|\) is small, the number of neighbors of \(A_X\) outside \(C_Y\) is upper bounded, and \(|N(A_X) \cap C_Y|\) is lower bounded: \[ |N(A_X) \cap C_Y| \geq |A_X| - \frac{|\nabla(C)|}{d} \geq |A| - |A \triangle C_X| - \frac{|\nabla(C)|}{d} = |A| - 64t \] For those possible \(u \in A \backslash C_X\), if \(N(u) \backslash C_Y\) is large, then the disjoint union \((N(u) \backslash C_Y) \cup (N(A_X) \cap C_Y)\) is also provably large. A bound is therefore implied by the \(t\)-contracting property of \(A\): \[ |A| + t \geq |N(A)| \geq \Bigl\lvert (N(u) \backslash C_Y) \cup (N(A_X) \cap C_Y) \Bigr\rvert \geq |N(u) \backslash C_Y| + |A| - 64t, \] that is, \(|N(u) \backslash C_Y| \leq 65t\).

Conclude the two parts, we can enumerate \(A\):

- inside \(C_X\), we enumerate \(S'_Y \subseteq S_Y\) and set \(A_X = C_X \backslash N(S'_Y)\); (if extra vertices are excluded, then either \(C_Y \backslash S_Y\) are not completely covered, or it's the case enumerating other values of \(S'_Y\).)
- outside \(C_X\), we enumerate \(S'_X \subseteq S_X := \left\lbrace u \in X \backslash C_X: |N(u) \backslash C_Y| \leq 65t \right\rbrace\) and set \(A = A_X \cup S'_X\).

The number of such \(A\)'s is thus bounded by \(2^{|S_X| + |S_Y|}\). Noticing that the vertices in the two sets all have lower bounded contributions to \(|\nabla(C)|\), we can hence bound the amount by choosing \(t\) suitably: \[ 33td \geq |\nabla(C)| \geq |S_X|(d - 65t) + |S_Y|(d - 32t) \geq \frac{d}{2} \bigl( |S_X| + |S_Y| \bigr) \] where we choose \(t < d/130\) to derive the last inequality.

Finally we have \(2^{|S_X| + |S_Y|} \leq 2^{66t}\) and state the following lemma with more general parameters:

By picking \(t \leq d/(4c+2)\) we have the bound \(2^{2(c+1)t}\), and inserting \(c = 32\) results in the bound we need.

Combining the three parts shown above, we reach the end:

## References

- [CDKP23] Charlie Carlson, Ewan Davies, Alexandra Kolla, and Aditya Potukuchi.
*Approximately Counting Independent Sets in Dense Bipartite Graphs via Subspace Enumeration*. SODA 2023, [CoRR abs/2307.09533] - [JPP21] M. Jenssen, W. Perkins, and A. Potukuchi.
*Approximately counting independent sets in bipartite graphs via graph containers*. Random Structures & Algorithms, 63(1):215–241, 2023. doi:10.1002/rsa.21145, [CoRR abs/2109.03744].