Arnoldi iteration

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Publications associées (27)

Novel corrector problems with exponential decay of the resonance error for numerical homogenization

Edoardo Paganoni

Multiscale problems, such as modelling flows through porous media or predicting the mechanical properties of composite materials, are of great interest in many scientific areas. Analytical models describing these phenomena are rarely available, and one mus ...

EPFL2020

A note on efficient computation of privileged directions in modifier adaptation

Dominique Bonvin, Martand Singhal, Timm Faulwasser, Alejandro Gabriel Marchetti

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially pe ...

PERGAMON-ELSEVIER SCIENCE LTD2020

A Linearly Convergent Algorithm for Decentralized Optimization: Sending Less Bits for Free!

Martin Jaggi, Anastasiia Koloskova

Decentralized optimization methods enable on-device training of machine learning models without a central coordinator. In many scenarios communication between devices is energy demanding and time consuming and forms the bottleneck of the entire system. We ...

MICROTOME PUBLISHING2020

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 ...

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

Global Auto-regressive Depth Recovery via Iterative Non-local Filtering

Pascal Frossard, Jingyu Yang, Xinchen Ye

Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we prese ...

IEEE2019

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

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