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Bobkov, Houdre, and the last author introduced a Poincare-type functional parameter, $lambda_infty$, of a graph $G$. They related $lambda_infty$ to the {em vertex expansion} of the graph via a Cheeger-type inequality, analogous to the inequality relating the spectral gap of the graph, $lambda_2$, to its {em edge expansion}. While $lambda_2$ can be computed efficiently, the computational complexity of $lambda_infty$ has remained an open question. Following the work of the second author with Raghavendra and Vempala, wherein the complexity of $lambda_infty$ was related to the so-called small-set expansion (SSE) problem, it has been believed that computing $lambda_infty$ is a hard problem. We confirm this conjecture by proving that computing $lambda_infty$ is indeed NP-hard, even for weighted trees. Our gadget further proves NP-hardness of computing emph{spread constant} of a weighted tree; i.e., a geometric measure of the graph, introduced by Alon, Boppana, and Spencer, in the context of deriving an asymptotic isoperimetric inequality of Cartesian products of graphs. We conclude this case by providing a fully polynomial time approximation scheme. We further study a generalization of spread constant in machine learning literature, namely the {em maximum variance embedding} problem. For trees, we provide fast combinatorial algorithms that avoid solving a semidefinite relaxation of the problem. On the other hand, for general graphs, we propose a randomized projection method that can outperform the optimal orthogonal projection, i.e., PCA, classically used for rounding of the optimum lifted solution (to SDP relaxation) of the problem.
We study the complexity of approximating the vertex expansion of graphs $G = (V,E)$, defined as [ Phi^V := min_{S subset V} n cdot frac{|N(S)|}{|S| |V backslash S|}. ] We give a simple polynomial-time algorithm for finding a subset with vertex expa
This paper is aimed to investigate some computational aspects of different isoperimetric problems on weighted trees. In this regard, we consider different connectivity parameters called {it minimum normalized cuts}/{it isoperimteric numbers} defined
In this note we investigate the complexity of the Minimum Label Alignment problem and we show that such a problem is APX-hard.
We study the NP-hard textsc{$k$-Sparsest Cut} problem ($k$SC) in which, given an undirected graph $G = (V, E)$ and a parameter $k$, the objective is to partition vertex set into $k$ subsets whose maximum edge expansion is minimized. Herein, the edge
We revisit the complexity of online computation in the cell probe model. We consider a class of problems where we are first given a fixed pattern or vector $F$ of $n$ symbols and then one symbol arrives at a time in a stream. After each symbol has ar