Imagine we have an n1×n2 matrix from which we only get to see a small number of the entries. Is it possible from the available entries to guess the many entries that are missing? In general it is an impossible task because the unknown entries could be anything. However, if one knows that the matrix is low rank and makes a few reasonable assumptions, then the matrix can indeed be reconstructed a...

Abstract. In this paper we are concentrated on a problem of linear chanceconstrained programming where the constraint matrix is considered random with a known distribution of the matrix rows. The rows are not considered to be independent; instead, we make use of the copula notion to describe the dependence of the matrix rows. In particular, the distribution of the rows is driven by so-called Ar...

<p style='text-indent:20px;'>In this paper we propose a multiscale traffic model, based on the family of Generic Second Order Models, which integrates multiple trajectory data into velocity function. This combination second order macroscopic model with microscopic information allows us to reproduce significant variations in speed and acceleration that strongly influence emissions. We obta...

We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX). The notion of basis fields, which are an extension of scalar basis functions, arises naturally in our framework from a rotational invariance requirement. We consider a regression setting as well as inverse problems. All variants discussed lead to second-order cone programming formulations. ...