Publications related to Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Nicolas Henri Bernard Flammarion, Aymeric Daphnis Kévin Dieuleveut

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2017

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Alberto Crivellaro

We propose new algorithms to overcome two of the most constraining limitations of surface reconstruction methods in use. In particular, we focus on the large amount of data characterizing standard acquisitions by scanner and the noise intrinsically introdu ...

Elsevier Science Bv2017

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Andreas Pautz, Mathieu Hursin, Daniel Jerôme Siefman

This paper describes the application of data assimilation methods to CASMO-5 simulations of a Proteus research reactor experiment. Its focus is a comparison and evaluation of three prominent data assimilation methods: generalized linear least squares, MOCA ...

2017

Locating Faults on Untransposed, Meshed Transmission Networks Using a Limited Number of Synchrophasor Measurements

Mario Paolone, Sadegh Azizi

The method of symmetrical components is not effective for fault location in the case of untransposed lines, due to potential couplings between the sequence circuits. This paper proposes a non-iterative algorithm in the phase-coordinates for wide-area fault ...

Institute of Electrical and Electronics Engineers2016

From averaging to acceleration, there is only a step-size

Nicolas Henri Bernard Flammarion

We consider the optimization of a quadratic objective function whose gradients are only accessible through a stochastic oracle that returns the gradient at any given point plus a zero-mean finite variance random error. We present the first algorithm that a ...

2015

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate functions based on random sampling according to a given probability measure. Recent work has shown that ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

Idiap2015

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Fabio Nobile, Giovanni Migliorati

Motivated by the numerical treatment of parametric and stochastic PDEs, we analyze the least-squares method for polynomial approximation of multivariate func- tions based on random sampling according to a given probability measure. Recent work has shown th ...

MATHICSE2014

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

Julien Léo Billeter

This lecture describes the following topics: • Dynamic Models - Conservation of Mass (Concentration Measurements) - Conservation of Energy (Calorimetry) - Beer’s Law (Spectroscop ...

2013

Iterative pre-conditioning for expediting the distributed gradient-descent method: The case of linear least-squares problem

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Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Locating Faults on Untransposed, Meshed Transmission Networks Using a Limited Number of Synchrophasor Measurements

From averaging to acceleration, there is only a step-size

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

Process Design and Optimization (MLS-S03): Model Identification by Gradient Methods

Reconstruction of 3D scattered data via radial basis functions by efficient and robust techniques

Locating Faults on Untransposed, Meshed Transmission Networks Using a Limited Number of Synchrophasor Measurements

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

From averaging to acceleration, there is only a step-size

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs

MATHICSE Technical Report : Discrete least squares polynomial approximation with random evaluations – application to parametric and stochastic elliptic PDES