Publications associées à Practical Sketching Algorithms For Low-Rank Matrix Approximation

A fast spectral divide-and-conquer method for banded matrices

Based on the spectral divide-and-conquer algorithm by Nakatsukasa and Higham [SIAM J. Sci. Comput., 35(3):A1325-A1349, 2013], we propose a new algorithm for computing all the eigenvalues and eigenvectors of a symmetric banded matrix with small bandwidth, w ...

WILEY2021

Bootstrapping massive quantum field theories

Denis Karateev

We propose a new non-perturbative method for studying UV complete unitary quantum field theories (QFTs) with a mass gap in general number of spacetime dimensions. The method relies on unitarity formulated as positive semi-definiteness of the matrix of inne ...

SPRINGER2020

New Results in Integer and Lattice Programming

Christoph Hunkenschröder

An integer program (IP) is a problem of the form

\min \{f(x) : \, Ax = b, \ l \leq x \leq u, \ x \in \Z^n\}

, where

A \in \Z^{m \times n}

,

b \in \Z^m

,

l,u \in \Z^n

, and

f: \Z^n \rightarrow \Z

is a separable convex objective function. The problem o ...

EPFL2020

On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Daniel Kressner, Stefano Massei, Alice Cortinovis

The problem of finding a k x k submatrix of maximum volume of a matrix A is of interest in a variety of applications. For example, it yields a quasi-best low-rank approximation constructed from the rows and columns of A. We show that such a submatrix can a ...

ELSEVIER SCIENCE INC2020

Sub-Matrix Factorization for Real-Time Vote Prediction

Patrick Thiran, Matthias Grossglauser, Victor Kristof

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, effic ...

2020

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Stefano Massei

Evaluating the action of a matrix function on a vector, that is

x=f(\mathcal M)v

, is an ubiquitous task in applications. When

\mathcal M

is large, one usually relies on Krylov projection methods. In this paper, we provide effective choices for the pole ...

2019

Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation

Volkan Cevher, Alp Yurtsever

This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of data matrices that arise from large-scale scientific simulations and data collection. The technical contribution consists in a new algorithm for constructing ...

2019

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Daniel Kressner, Stefano Massei, Alice Cortinovis

The problem of finding a

k\times k

submatrix of maximum volume of a matrix A is of interest in a variety of applications. For example, it yields a quasi-best low-rank approximation constructed from the rows and columns of A. We show that such a submatrix ...

MATHICSE2019

MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations

Daniel Kressner, Stefano Massei

In this work, we consider two types of large-scale quadratic matrix equations: Continuous-time algebraic Riccati equations, which play a central role in optimal and robust control, and unilateral quadratic matrix equations, which arise from stochastic proc ...

MATHICSE2019

Unlabeled Sensing with Random Linear Measurements

Martin Vetterli, Jayakrishnan Unnikrishnan, Saeid Haghighatshoar

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in wh ...

Institute of Electrical and Electronics Engineers2018

Practical Sketching Algorithms For Low-Rank Matrix Approximation

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A fast spectral divide-and-conquer method for banded matrices

Bootstrapping massive quantum field theories

New Results in Integer and Lattice Programming

On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Sub-Matrix Factorization for Real-Time Vote Prediction

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations

Unlabeled Sensing with Random Linear Measurements

New Results in Integer and Lattice Programming

Bootstrapping massive quantum field theories

Sub-Matrix Factorization for Real-Time Vote Prediction

A fast spectral divide-and-conquer method for banded matrices

On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices

Rational Krylov for Stieltjes matrix functions: convergence and pole selection

MATHICSE Technical Report : Low-rank updates and divide-andconquer methods for quadratic matrix equations

Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation

Unlabeled Sensing with Random Linear Measurements

MATHICSE Technical Report : On maximum volume submatrices and cross approximation for symmetric semidefinite and diagonally dominant matrices