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Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

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MATHICSE Technical Report : Multi-index stochastic collocation for random PDEs

Fabio Nobile, Lorenzo Tamellini

In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the ...

MATHICSE2015

Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization

Ulugbek Kamilov

Generalized Linear Models (GLMs), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = A x arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) meth ...

IEEE2015

Learning of Closed-Loop Motion Control

Jonas Buchli, Farbod Farshidian

Learning motion control as a unified process of designing the reference trajectory and the controller is one of the most challenging problems in robotics. The complexity of the problem prevents most of the existing optimization algorithms from giving satis ...

2014

Splitting methods in control

Colin Neil Jones, Ye Pu, Georgios Stathopoulos, Alexander Szücs

The need for optimal control of processes under a restricted amount of resources renders first order optimization methods a viable option. Although computationally cheap, these methods typically suffer from slow convergence rates. In this work we discuss t ...

IEEE2014

The Spectrum Of The Force-Based Quasicontinuum Operator For A Homogeneous Periodic Chain

We show under general conditions that the linearized force-based quasicontinuum (QCF) operator has a real, positive spectrum. The spectrum is identical to that of the quasinon-local quasicontinuum (QNL) operator in the case of second-neighbor interactions. ...

Siam Publications2012

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Memory-efficient Krylov subspace techniques for solving large-scale Lyapunov equations

Daniel Kressner

This paper considers the solution of large-scale Lyapunov matrix equations of the form AX + XA(T) = -bb(T). The Arnoldi method is a simple but sometimes ineffective approach to deal with such equations. One of its major drawbacks is excessive memory consum ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

Numerical methods and models relevant to transportation applications

Michaël Clément Louis Ghislain Thémans

In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...

EPFL2007

Iterative methods for Stokes/Darcy coupling

Marco Discacciati

We present iterative subdomain methods based on a domain decomposition approach to solve the coupled Stokes/Darcy problem using finite elements. The dependence of the convergence rate on the grid parameter h and on the physical data is discussed; some diff ...

2005

Spline-Based Image-to-Volume Registration for Three-Dimensional Electron Microscopy

Michaël Unser, Philippe Thévenaz

This paper presents an algorithm based on a continuous framework for a posteriori angular and translational assignment in three-dimensional electron microscopy (3DEM) of single particles. Our algorithm can be used advantageously to refine the assignment of ...

Elsevier2005