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Publications associées (69)

Generalized Incremental Model Identification for Chemical Reaction Systems

Dominique Bonvin, Julien Léo Billeter, Diogo Filipe Mateus Rodrigues, Adrien Oulevey

Identification of kinetic models and estimation of kinetic parameters in chemical reaction systems can be done using Incremental Model Identification (IMI). By using IMI, it is possible to separate the effect of the different reactions and thus investigate ...

2017

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

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

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

MATHICSE Technical Report : Multilevel weighted least squares polynomial approximation

Fabio Nobile

Weighted least squares polynomial approximation uses random samples to determine projections of functions onto spaces of polynomials. It has been shown that, using an optimal distribution of sample locations, the number of samples required to achieve quasi ...

MATHICSE2017

DETERMINING POSITIONS OF TRANSDUCERS FOR RECEIVING AND/OR TRANSMITTING WAVE SIGNALS

Paul Hurley, Matthieu Martin Jean-André Simeoni, Nezihe Merve Gürel

The invention is notably directed to a method for determining positions {pi}i=1,..., N of transducers {Ai}i=1,..., N of an apparatus. The transducers are assumed to be configured for receiving wave signals from and/or transmitting wave signals to one or mo ...

2017

Importance Sampling Tree for Large-scale Empirical Expectation

François Fleuret, Olivier Canévet, Cijo Jose

We propose a tree-based procedure inspired by the Monte-Carlo Tree Search that dynamically modulates an importance-based sampling to prioritize computation, while getting unbiased estimates of weighted sums. We apply this generic method to learning on very ...

2016

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Ali H. Sayed, Roula Nassif

In this work, we consider multitask learning problems where clusters of nodes are interested in estimating their own parameter vector. Cooperation among clusters is beneficial when the optimal models of adjacent clusters have a good number of similar entri ...

IEEE2016

Multilevel and weighted reduced basis method for stochastic optimal control problems constrained by Stokes equations

Alfio Quarteroni, Gianluigi Rozza, Peng Chen

In this paper we develop and analyze a multilevel weighted reduced basis method for solving stochastic optimal control problems constrained by Stokes equations. We prove the analytic regularity of the optimal solution in the probability space under certain ...

Springer Heidelberg2016

A production planning in highly automated manufacturing system considering multiple process plans with different energy requirements

Paul Xirouchakis, Yong Chan Choi

This paper focuses on a production planning problem in a highly automated manufacturing system considering multiple process plans with different energy requirements. The system consists of several closely interconnected sub-systems such as the processing s ...

Springer London Ltd2014

A WebGIS application for the monitoring of Farm Animal Genetic Resources

We developed used a WebGIS platform prototype (GenMon) based on open source software (PostgreSQL, PostGIS, OpenLayers, Geoserver), to assess pedigree information, geographical concentration, socio-economic and environmental information. GenMon includes Pop ...

2014

Single machine scheduling with scenarios

Ola Nils Anders Svensson

In the field of robust optimization, the goal is to provide solutions to combinatorial problems that hedge against variations of the numerical parameters. This constitutes an effort to design algorithms that are applicable in the presence of uncertainty in ...

2013

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