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We consider large Hermitian matrices whose entries are defined by evaluating the exponential function along orbits of the skew-shift (2j)ω+jy+xmod1 for irrational ω. We prove that the eigenvalue distribution of these matrices conv ...
A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...
Like conventional material products, waste is the last stage of the life cycle of engineered nanomaterials, which are then incinerated or stabilized before disposal. However, because of their special physical characteristics, the fate of the thermally trea ...
The QZ algorithm for computing eigenvalues and eigenvectors of a matrix pencil A - lambda B requires that the matrices first be reduced to Hessenberg-triangular (HT) form. The current method of choice for HT reduction relies entirely on Givens rotations re ...
Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...
A Quasi Toeplitz (QT) matrix is a semi-infinite matrix of the kind A=T(a)+E whereT(a)=(aj−i)i,j∈Z+, E=(ei,j)i,j∈Z+ is compact and the norms ∥a∥W=∑i∈Z∣a∣j and $|E|_{ ...
The matrix completion problem consists of finding or approximating a low-rank matrix based on a few samples of this matrix. We propose a new algorithm for matrix completion that minimizes the least-square distance on the sampling set over the Riemannian ma ...
One of the most basic graph problems, All-Pairs Shortest Paths (APSP) is known to be solvable in n^{3-o(1)} time, and it is widely open whether it has an O(n^{3-ε}) time algorithm for ε > 0. To better understand APSP, one often strives to obtain subcubic t ...
Schloss Dagstuhl -- Leibniz-Zentrum für Informatik2021
Euclidean distance matrices (EDMs) are matrices of the squared distances between points. The definition is deceivingly simple; thanks to their many useful properties, they have found applications in psychometrics, crystallography, machine learning, wireles ...