Research Output per year

## Personal profile

### Research interests

My main area of research is domain decomposition methods for solving large-scale problems in massively parallel environments.

## Fingerprint Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Lagrange multiplier Method
Mathematics

Lagrange multipliers
Engineering & Materials Science

Domain Decomposition Method
Mathematics

Preconditioner
Mathematics

Domain decomposition methods
Engineering & Materials Science

Condition number
Mathematics

GMRES
Mathematics

Elliptic PDE
Mathematics

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Co Author Network
Recent external collaboration on country level. Dive into details by clicking on the dots.

## Research Output 2008 2018

## Comparisons among several methods for handling missing data in principal component analysis (PCA)

Loisel, S. & Takane, Y., 18 Jan 2018, In : Advances in Data Analysis and Classification.Research output: Contribution to journal › Article

principal component analysis

comparison

method

rate

## An optimal Schwarz preconditioner for a class of parallel adaptive finite elements

Loisel, S. & Nguyen, H., Sep 2017, In : Journal of Computational and Applied Mathematics. 321, p. 90–107 18 p.Research output: Contribution to journal › Article

Open Access

File

Adaptive Finite Elements

Preconditioner

Mesh

Smallest Eigenvalue

Largest Eigenvalue

## A comparison of additive schwarz preconditioners for parallel adaptive finite elements

Loisel, S. & Nguyen, T. H., 2016,*Domain Decomposition Methods in Science and Engineering XXII.*Springer International Publishing, p. 345-354 10 p. (Lecture Notes in Computational Science and Engineering; vol. 104)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

## On the PLS algorithm for multiple regression (PLS1)

Takane, Y. & Loisel, S., 16 Oct 2016,*The Multiple Facets of Partial Least Squares and Related Methods.*Springer, p. 17-28 12 p. (Springer Proceedings in Mathematics & Statistics; vol. 173)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Partial Least Squares

Least Square Algorithm

Multiple Regression

Least Squares Estimator

Dimensionality

## Schwarz preconditioner for the stochastic finite element method

Subber, W. & Loisel, S., 2016,*Domain Decomposition Methods in Science and Engineering XXII.*Springer, p. 397-405 9 p. (Lecture Notes in Computational Science and Engineering; vol. 104)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Stochastic Finite Element

Polynomial Chaos

Stochastic Methods

Chaos theory

Preconditioner