Publications associées à Projection matrix

Outlier-free spline spaces for isogeometric discretizations of biharmonic and polyharmonic eigenvalue problems

We present outlier-free isogeometric Galerkin discretizations of eigenvalue problems related to the biharmonic and the polyharmonic operator in the univariate setting. These are Galerkin discretizations in certain spline subspaces that provide accurate app ...

Elsevier Science Sa2023

Application of optimal spline subspaces for the removal of spurious outliers in isogeometric discretizations

Espen Sande

We show that isogeometric Galerkin discretizations of eigenvalue problems related to the Laplace operator subject to any standard type of homogeneous boundary conditions have no outliers in certain optimal spline subspaces. Roughly speaking, these optimal ...

2022

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Nicolas Macris, Jean François Emmanuel Barbier

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

AMER PHYSICAL SOC2022

Leverage Point Identification Method for LAV-Based State Estimation

Mario Paolone, Guglielmo Frigo, Ali Abur, Mathias Dorier

In this paper we enunciate and rigorously demonstrate a new lemma that, based on a previously proposed theorem, proves the identifiability of leverage points in state estimation with specific reference to the Least Absolute Value (LAV) estimator. In this c ...

2021

Single-projection reconstruction technique for positioning monodisperse spheres in 3D with a divergent x-ray beam

Edward Carlo Giorgio Andò

The measurement of the position of single-sized spheres in 3D from a single, divergent, radiographic projection is addressed in the present study with the development of a novel method. Generally speaking, the location of the shadow cast by a single sphere ...

2021

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Zheng Dang, Kwang Moo Yi, Fei Wang, Yinlin Hu

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

FANOK: Knockoffs in Linear Time

Quentin Rebjock

We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large-scale feature selection problems. Identifying the knockoff distribution requires solving a large-scale semidefinite progra ...

SIAM PUBLICATIONS2021

Cortico–reticulo–spinal circuit reorganization enables functional recovery after severe spinal cord contusion

Grégoire Courtine, Bernard Schneider, Quentin Barraud, Léonie Asboth, Mark Andrew Anderson, Lucia Florinda Friedli Wittler, Laetitia Danielle Philippine Baud, Polina Shkorbatova, Janine Beauparlant, Galyna Pidpruzhnykova, Selin Anil, Julie Kreider, Cristina Martinez Gonzalez, Elodie Rey

Severe spinal cord contusions interrupt nearly all brain projections to lumbar circuits producing leg movement. Failure of these projections to reorganize leads to permanent paralysis. Here we modeled these injuries in rodents. A severe contusion abolished ...

2018

Complex heavy-quark potential and Debye mass in a gluonic medium from lattice QCD

Yannis Burnier

We improve and extend our study of the complex in-medium heavy-quark potential and its Debye mass m(D) in a gluonic medium with a finer scan around the deconfinement transition and newly generated ensembles closer to the thermodynamic limit. On the lattice ...

American Physical Society2017

A matrix DEIM technique for model reduction of nonlinear parametrized problems in cardiac mechanics

Alfio Quarteroni, Andrea Manzoni

When relying on Newton iterations to solve nonlinear problems in the context of Reduced Basis (RB) methods, the assembling of the RB arrays during the online stage depends on the dimension of the underlying high-fidelity approximation. This is more of an i ...

Elsevier Science Sa2017

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Martin Vetterli, Marta Martinez-Camara, Benjamin Bejar Haro

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Jean-Yves Le Boudec, Mario Paolone, Lorenzo Zanni, Sidi-Rachid Cherkaoui

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

Functional data analysis by matrix completion

Marie-Hélène Descary

Traditional approaches to analysing functional data typically follow a two-step procedure, consisting in first smoothing and then carrying out a functional principal component analysis. The idea underlying this procedure is that functional data are well ap ...

EPFL2017

Group-Sparse Model Selection: Hardness and Relaxations

Volkan Cevher, Anastasios Kyrillidis, Luca Baldassarre, Nirav Bhan

Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these models is the recovery of ``interpretable" signals along with ...

Institute of Electrical and Electronics Engineers2016

Multi-Period Portfolio Optimization: Translation of Autocorrelation Risk to Excess Variance

Napat Rujeerapaiboon, Byung-Geun Choi

Growth-optimal portfolios are guaranteed to accumulate higher wealth than any other investment strategy in the long run. However, they tend to be risky in the short term. For serially uncorrelated markets, similar portfolios with more robust guarantees hav ...

2016

Neutrino masses and cosmological parameters from a Euclid-like survey: Markov Chain Monte Carlo forecasts including theoretical errors

Julien Lesgourgues, Benjamin Audren

We present forecasts for the accuracy of determining the parameters of a minimal cosmological model and the total neutrino mass based on combined mock data for a future Euclid-like galaxy survey and Planck. We consider two different galaxy surveys: a spect ...

Iop Publishing Ltd2013

Manifold Sparse Beamforming

Volkan Cevher, Afsaneh Asaei, Baran Gözcü

We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this settin ...

2013

Investigating Reliability on Fuel Cell Model Identification. Part III: Behavior of Assessment Criteria and Limits in Identification

Daniel Favrat, Jan Van Herle, Leonidas Tsikonis

The present paper is the third and last part of an investigation on what determines reliability in fuel cell model identification. In continuation to the effect of experimental design (Part I) and a process method for stochastic calculation of a model's pa ...

Wiley-V C H Verlag Gmbh2013

On the use of spatially discrete data to compute energy and mass balance

Marc Parlange, Ivan Lunati, Francesco Ciocca

In many practical applications the state of field soils is monitored by recording the evolution of temperature and soil moisture at discrete depths. We theoretically investigate the systematic errors that arise when mass and energy balances are computed di ...

American Geophysical Union2012

A stopping criterion for the conjugate gradient algorithm in the framework of anisotropic adaptive finite elements

Marco Picasso

We propose a simple stopping criterion for the conjugate gradient (CG) algorithm in the framework of anisotropic, adaptive finite elements for elliptic problems. The goal of the adaptive algorithm is to find a triangulation such that the estimated relative ...

2009