Concept

Méthode de la puissance itérée

Publications associées (65)

Forward-reflected-backward method with variance reduction

We propose a variance reduced algorithm for solving monotone variational inequalities. Without assuming strong monotonicity, cocoercivity, or boundedness of the domain, we prove almost sure convergence of the iterates generated by the algorithm to a soluti ...

2021

Second-order adjoint-based sensitivity for hydrodynamic stability and control

Edouard Boujo

Adjoint-based sensitivity analysis is routinely used today to assess efficiently the effect of open-loop control on the linear stability properties of unstable flows. Sensitivity maps identify regions where small-amplitude control is the most effective, i. ...

CAMBRIDGE UNIV PRESS2021

Active Learning of Bayesian Probabilistic Movement Primitives

Jean-Marc Odobez, Sylvain Calinon, Hakan Girgin, Thibaut Antoine Kulak

Learning from Demonstration permits non-expert users to easily and intuitively reprogram robots. Among approaches embracing this paradigm, probabilistic movement primitives (ProMPs) are a well-established and widely used method to learn trajectory distribu ...

IEEE2021

Compress-and-restart block Krylov subspace methods for Sylvester matrix equations

Daniel Kressner, Stefano Massei, Kathryn Dianne Lund

Block Krylov subspace methods (KSMs) comprise building blocks in many state-of-the-art solvers for large-scale matrix equations as they arise, for example, from the discretization of partial differential equations. While extended and rational block Krylov ...

2020

Interactive Real-time Structural Modelling - Solids

The aim of this master project is to develop a fast computing method for the analysis of structures with non-linear geometric behavior. The Dynamic Relaxation (DR) method is employed instead of the better known Newton-Raphson iteration process based on Fin ...

2020

Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization

Ali H. Sayed, Kun Yuan

This paper introduces a new algorithm for consensus optimization in a multi-agent network, where all agents collaboratively find a minimizer for the sum of their private functions. All decentralized algorithms rely on communications between adjacent nodes. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization

Martin Jaggi, Thijs Vogels

We study lossy gradient compression methods to alleviate the communication bottleneck in data-parallel distributed optimization. Despite the significant attention received, current compression schemes either do not scale well, or fail to achieve the target ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Backpropagation-Friendly Eigendecomposition

Pascal Fua, Mathieu Salzmann, Zheng Dang, Wei Wang, Yinlin Hu

Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it with the Power Iteration method, particularly when dealing with large matri ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Multi space reduced basis preconditioners for parametrized Stokes equations

Alfio Quarteroni, Simone Deparis, Andrea Manzoni, Niccolò Dal Santo

We introduce a two-level preconditioner for the efficient solution of large scale saddle point linear systems arising from the finite element (FE) discretization of parametrized Stokes equations. This preconditioner extends the Multi Space Reduced Basis (M ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Eigenmode Analysis in Plasmonics: Application to Second Harmonic Generation and Electron Energy Loss Spectroscopy

Gabriel David Bernasconi

Eigenmodes are central to the study of resonant phenomena in all areas of physics. However, their use in nano-optics seems to have been hindered and delayed for various reasons. First, due to their small size, the response of nanostructures to a far-field ...

EPFL2019

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