This introduction to the theory of rigid structures explains how to analyze the performance of
built and natural structures under loads, paying special attention to the role of geometry. The …

We develop a penalized likelihood estimation framework to learn the structure of Gaussian
Bayesian networks from observational data. In contrast to recent methods which accelerate …

The problem of estimating high-dimensional network models arises naturally in the analysis
of many biological and socio-economic systems. In this work, we aim to learn a network …

We present a novel algebraic combinatorial view on low-rank matrix completion based on
studying relations between a few entries with tools from algebraic geometry and matroid …

The problem of completing a low-rank matrix from a subset of its entries is often encountered
in the analysis of incomplete data sets exhibiting an underlying factor model with …

The Handbook of Geometric Constraint Systems Principles is an entry point to the currently
used principal mathematical and computational tools and techniques of the geometric …

This paper is based on the material I presented at the Research Institute for Mathematical
Sciences (RIMS), Kyoto University, in October 2012 in a series of lectures. Thus, on one …

In the network localization problem the goal is to determine the location of all nodes by using
only partial information on the pairwise distances (and by computing the exact location of …

In the random subgraph model we consider random subgraphs G(t) of a graph G obtained
as follows: for each edge in G we independently decide to retain the edge with probability t …

A graph is called $ d $-rigid if there exists a generic embedding of its vertex set into
$\mathbb {R}^ d $ such that every continuous motion of the vertices that preserves the …