A. A. Ahmadi, S. Dash, and G. Hall, “

Optimization over Structured Subsets of Positive Semidefinite Matrices via Column Generation,”

Discrete Optimization, 2016.

Abstract

We develop algorithms for inner approximating the cone of positive semidefinite matrices via linear programming and second order cone programming. Starting with an initial linear algebraic approximation suggested recently by Ahmadi and Majumdar, we describe an iterative process through which our approximation is improved at every step. This is done using ideas from column generation in large-scale linear and integer programming. We then apply these techniques to approximate the sum of squares cone in a nonconvex polynomial optimization setting, and the copositive cone for a discrete optimization problem.

Download Here E. Abbe, A. S. Bandeira, and G. Hall, “

Exact Recovery in the Stochastic Block Model,”

IEEE: Transactions on Information Theory, vol. 62, no. 1, 2016.

On ArXivAbstractThe stochastic block model (SBM) with two communities, or equivalently the planted bisection model, is a popular model of random graph exhibiting a cluster behaviour. In the symmetric case, the graph has two equally sized clusters and vertices connect with probability p within clusters and q across clusters. In the past two decades, a large body of literature in statistics and computer science has focused on providing lower-bounds on the scaling of |p−q| to ensure exact recovery. In this paper, we identify a sharp threshold phenomenon for exact recovery: if α=pn/log(n) and β=qn/log(n) are constant (with α>β), recovering the communities with high probability is possible if (α+β)/2-√αβ>1 and impossible if (α+β)/2−√αβ<1. In particular, this improves the existing bounds. This also sets a new line of sight for efficient clustering algorithms. While maximum likelihood (ML) achieves the optimal threshold (by definition), it is in the worst-case NP-hard. This paper proposes an efficient algorithm based on a semidefinite programming relaxation of ML, which is proved to succeed in recovering the communities close to the threshold, while numerical experiments suggest it may achieve the threshold. An efficient algorithm which succeeds all the way down to the threshold is also obtained using a partial recovery algorithm combined with a local improvement procedure.

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