Publications related to Linear approximation

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

EPFL2023

Proximal Point Imitation Learning

Volkan Cevher, Luca Viano, Igor Krawczuk, Angeliki Kamoutsi

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...

2022

TimeEvolver: A program for time evolution with improved error bound

Sebastian Zell

We present TimeEvolver, a program for computing time evolution in a generic quantum system. It relies on well-known Krylov subspace techniques to tackle the problem of multiplying the exponential of a large sparse matrix iH, where His the Hamiltonian, with ...

ELSEVIER2022

Function Integration, Reconstruction And Approximation Using Rank-1 Lattices

Fabio Nobile, Giovanni Migliorati

We consider rank-1 lattices for integration and reconstruction of functions with series expansion supported on a finite index set. We explore the connection between the periodic Fourier space and the non-periodic cosine space and Chebyshev space, via tent ...

2021

Positive moments for scattering amplitudes

Riccardo Rattazzi, Francesco Riva, Marc Riembau Saperas, Brando Bellazzini

We find the complete set of conditions satisfied by the forward 2 -> 2 scattering amplitude in unitary and causal theories. These are based on an infinite set of energy dependent quantities (the arcs) which are dispersively expressed as moments of a positi ...

American Physical Society2021

Functional Tucker Approximation Using Chebyshev Interpolation

Daniel Kressner, Christoph Max Strössner

This work is concerned with approximating a trivariate function defined on a tensor-product domain via function evaluations. Combining tensorized Chebyshev interpolation with a Tucker decomposition of low multilinear rank yields function approximations tha ...

SIAM PUBLICATIONS2021

Support and approximation properties of Hermite splines

Michaël Unser, Julien René Pierre Fageot, Virginie Sophie Uhlmann, Shayan Aziznejad

In this paper, we formally investigate two mathematical aspects of Hermite splines that are relevant to practical applications. We first demonstrate that Hermite splines are maximally localized, in the sense that the size of their support is minimal among ...

2020

Fast Least-Squares Padé approximation of problems with normal operators and meromorphic structure

Fabio Nobile, Francesca Bonizzoni, Davide Pradovera

In this work, we consider the approximation of Hilbert space-valued meromorphic functions that arise as solution maps of parametric PDEs whose operator is the shift of an operator with normal and compact resolvent, e.g., the Helmholtz equation. In this res ...

2020

Approximate analytic expressions using Stokes model for tokamak polarimetry and their range of validity

Olivier Sauter, Ambrogio Fasoli, Basil Duval, Stefano Coda, Martinus Gijs, Jonathan Graves, Yves Martin, Duccio Testa, Patrick Blanchard, Bruno Emanuel Ferreira De Sousa Correia, Alessandro Pau, Pierre-Thomas Paul Brun, Cristian Sommariva, Henri Weisen, Richard Pitts, Sun Hee Kim, Yann Camenen, Jan Horacek, Javier García Hernández, Marco Wischmeier, Mikhail Maslov, Nicola Vianello, Miguel Fernández Ruiz, Federico Nespoli, Antonio José Pereira de Figueiredo, Liang Yao, Yao Zhou, David Pfefferlé, Dalziel Joseph Wilson, Robin Humphry-Baker, Davide Galassi, José Pedro Rebelo Ferreira Marques, Ana Francisca Leal Silva Soares, Jonathan Marc Philippe Faustin, Daniel Scott Alessi, Arnout Lodewijk M Beckers, Julio Rodriguez, Hamish William Patten, Jonnathan Cesar Hidalgo Acosta, , , , , , , , ,

Méthodes numériques liées à la distribution d'alumine dans une cuve d'électrolyse d'aluminium

Thomas Frédéric Hilke

Metallic aluminium plays a key role in our modern economy. Primary metallic aluminium is produced by the transformation of aluminium oxide using the Hall-Héroult industrial process. This process, which requires enormous quantities of energy, consists in pe ...

EPFL2019

On quadratic matrix equations with infinite size coefficients encountered in QBD stochastic processes

Stefano Massei

Matrix equations of the kind A(1)X(2)+A(0)X+A(-1)=X, where both the matrix coefficients and the unknown are semi-infinite matrices belonging to a Banach algebra, are considered. These equations, where coefficients are quasi-Toeplitz matrices, are encounter ...

WILEY2018

Expectation propagation in the large data limit

Guillaume Philippe Ivan Joseph Dehaene

Expectation propagation (EP) is a widely successful algorithm for variational inference. EP is an iterative algorithm used to approximate complicated distributions, typically to find a Gaussian approximation of posterior distributions. In many applications ...

Wiley2018

Dual Dynamically Orthogonal approximation of incompressible Navier Stokes equations with random boundary conditions

Fabio Nobile, Eleonora Musharbash

In this paper we propose a method for the strong imposition of random Dirichlet boundary conditions in the Dynamical Low Rank (DLR) approximation of parabolic PDEs and, in particular, incompressible Navier Stokes equations. We show that the DLR variational ...

Elsevier2018

Learning Augmented Joint-Space Task-Oriented Dynamical Systems:A Linear Parameter Varying and Synergetic Control Approach

Aude Billard, Nadia Barbara Figueroa Fernandez, Seyed Sina Mirrazavi Salehian, Yonadav Goldwasser Shavit

In this paper, we propose an asymptotically stable joint-space dynamical system that captures desired behaviors in joint-space while stably converging towards a task-space attractor. To encode joint-space behaviors while meeting the stability criteria, the ...

2018

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Mohammad Emtiyaz Khan

Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution. In such situations, existing methods usually resort to a diagonal approxi ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

A Polynomial Regularity Lemma For Semialgebraic Hypergraphs And Its Applications In Geometry And Property Testing

János Pach

In this paper, we prove several extremal results for geometrically defined hypergraphs. In particular, we establish an improved lower bound, single exponentially decreasing in k, on the best constant delta > 0 such that the vertex classes P-1,...,P-k of ev ...

Society for Industrial and Applied Mathematics2016

Exact simulation of max-stable processes

Sebastian Engelke, Clément Dombry

Max-stable processes play an important role as models for spatial extreme events. Their complex structure as the pointwise maximum over an infinite number of random functions makes their simulation difficult. Algorithms based on finite approximations are o ...

Oxford University Press2016

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Timm Faulwasser

The present contribution demonstrates the applicability of polynomial chaos expansion to stochastic (optimal) AC power flow problems that arise in the operation of power grids. For rectangular power flow, polynomial chaos expansion together with Galerkin p ...

IEEE2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs

Andrea Manzoni, Federico Negri

In this paper we present some heuristic strategies to compute rapid and reliable approximations to stability factors in nonlinear, inf-sup stable parametrized PDEs. The efficient evaluation of these quantities is crucial for the rapid construction of a pos ...

Springer Verlag2015