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Is there an interpretation of MAXCUT using eigenvalues of the graph that yields constant factor approximation to MAXCUT?

Can the estimates provide sharp lower bound to MAXCUT?

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Yes, there is a connection between the spectrum of the graph and the size of the maximum cut. It might be easiest to see this with the normalized graph Laplacian, $L = I - D^{-1/2}AD^{-1/2}$, where $I$ is the identity, $A$ is the adjacency matrix of the graph $G = ([n], E)$ and $D$ is the diagonal matrix defined by $D_{ii} = d_i$, the degree of vertex $i$. The largest eigenvalue of $L$ is

$$\lambda_{n-1} = \max_x\frac{x^TLx}{x^Tx} = \max_{x}\frac{\sum_{(i, j) \in E}{(x_i - x_j)^2}}{\sum_{i}{d_i x_i^2}}.$$

Because $(x_i - x_j)^2 \leq 2x_i^2 + 2x_j^2$ by Cauchy-Schwarz, $\lambda_{n-1} \leq 2$. Equality is achieved if and only if for each edge $(i, j)$, $x_i = -x_j$, which means that $\lambda_{n-1}= 2$ only if the graph has a bipartite connected component, i.e. a connected component whose maximum cut is equal to the number of edges in the component.

For a robust version of this result, see Luca Trevisan's paper. He shows that

$$ \lambda_{n-1} \geq 2\frac{\text{MaxCut}}{m}, $$

(where $m$ is the number of edges of the graph) and if $\lambda_{n-1} \geq 2(1-\epsilon)$, then there exits a set of vertices $S$ and a partition $(S_1, S_2)$ of $S$ such that

$$ |e(S_1, S_2)| \geq \frac{1}{2}(1-\sqrt{16\epsilon})\text{vol}(S), $$ where $\text{vol}(S) = \sum_{i \in S}{d_i}$ and $e(S_1, S_2)$ is the set of edges with one endpoint in $S_1$ and the other in $S_2$.

Note that the second bound does not necessarily lower bound the max cut in a useful way: it only lower bounds the maximum cut of the set $S$ in terms of $\text{vol}(S)$. This is unavoidable: think of a graph that's the disjoint union of an edge and a large graph with small maximum cut, say a clique. The maximum cut of the graph is very close to $m/2$, which is the minimum possible value of the max cut of a graph with $m$ edges. However, $\lambda_{n-1} = 2$, the maximum possible value of $\lambda_{n-1}$. In other words, while $\lambda_{n-1}$ is a relaxation of the maximum cut (up to appropriate rescaling), the gap is arbitrarily close to 2, and a 2-approximation to max cut is trivial.

Nevertheless, in the paper Luca uses the spectral result to design a recursive approximation algorithm for max cut. This is one cool thing about the paper: it uses a relaxation that, unlike the SDP relaxation of Goemans and Wiliamson, has trivial gap, but despite that an optimal solution to the relaxation actually provides enough guidance to achieve a non-trivial approximation factor.

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  • $\begingroup$ Thankyou. What is $m$? what is $e(S_1,S_2)$ here? And so we have $MaxCut \leq \frac{\lambda_{n-1}}{2m}$? $\endgroup$ – 1.. Sep 30 '13 at 16:52
  • $\begingroup$ Can we have a lower bound to $MaxCut$ from eignevalues? $\endgroup$ – 1.. Sep 30 '13 at 16:56
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    $\begingroup$ I think you did the algebra wrong there, the max cut is at most $m \lambda_{n-1}/2$. This is the easy direction however. Indeed, the second bound is a partial converse. Note that it only says something about the subset $S$, however. In general you cannot lower bound the maximum cut of the entire graph from $\lambda_{n-1}$ because it's possible to have $\lambda_{n-1} = 2$ but the maximum cut being arbitrarily close to its minimum possible value, $m/2$ $\endgroup$ – Sasho Nikolov Sep 30 '13 at 17:01
  • $\begingroup$ I see that $MaxCut*d_{max}\geq|e(S_1,S_2)|$ and hence $MaxCut\geq\frac{1}{2d_{max}}(1-\sqrt{16\epsilon})Vol(S)$. Is this a sharp lower bound? However there is no eigenvalue here. $\endgroup$ – 1.. Sep 30 '13 at 17:02
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    $\begingroup$ So the bad example is quite robust. It's not necessary to have a bipartite connected component: just a small set of vertices that induce a subgraph which is close to bipartite and the cut between these vertices and the rest of the graph is small. $\endgroup$ – Sasho Nikolov Sep 30 '13 at 17:19
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Yes. The original Goemans-Williamson paper also discusses the dual of the PSD relaxation, which is equivalent to minimizing $\lambda_{max}(L_G+D)$, over all the traceless diagonal matrices D.

Trevisan used some of this intuition to design a nontrivial approximation to MaxCut, at the bottom of which lies a spectral partitioning algorithm. In his algorithm, he essentially looks at the largest eigenvector of the normalized Laplacian in order to isolate components of the graph between which there is a large cut. The core of the analysis is a Cheeger-like inequality for the largest eigenvalue.

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  • $\begingroup$ "....all traceless" contains complex matrices? $\endgroup$ – 1.. Sep 30 '13 at 16:54
  • $\begingroup$ No, we're only looking at real matrices. $\endgroup$ – zotachidil Sep 30 '13 at 17:32
  • $\begingroup$ I think since L is real, D the optimal D will be real wlog $\endgroup$ – Sasho Nikolov Sep 30 '13 at 20:34

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