Publications related to Least-Squares Minimization Under Constraints

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We rst investigate regularized algorithms adapted to a projection operator on a closed subspace ...

2020

On raw inertial measurements in dynamic networks

Jan Skaloud, Davide Antonio Cucci

Dynamic Networks have been introduced in the literature to solve multi-sensor fusion problems for navigation and mapping. They have been shown to outperform conventional methods in challenging scenarios, such as corridor mapping or self-calibration. In thi ...

2019

Basic gestalt laws cannot explain uncrowding

Michael Herzog, Oh-Hyeon Choung, Einat Rashal

Visual crowding is the inability to perceive elements within clutter. Traditional crowding models, such as pooling, predict that performance deteriorates when flankers are added. However, this prediction has been disproved. For example, performance was fou ...

SAGE PUBLICATIONS LTD2019

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Alexandre Caboussat, Dimitrios Gourzoulidis

In this article, we address the numerical solution of the Dirichlet problem for the three-dimensional elliptic Monge-Ampere equation using a least-squares/relaxation approach. The relaxation algorithm allows the decoupling of the differential operators fro ...

SPRINGER/PLENUM PUBLISHERS2018

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin

We investigate regularized algorithms combining with projection for least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space. We prove convergence results with respect to variants of n ...

ICML2018

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

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

Dominique Bonvin, Julien Léo Billeter, Michael Amrhein

Multivariate curve resolution via alternating least squares (ALS) is used to resolve the concentration profiles C and the pure component spectra E of S species from the multivariate absorbance data A, assuming the bilinear model ...

2016

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

Least-Squares Minimization Under Constraints

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Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

On raw inertial measurements in dynamic networks

Basic gestalt laws cannot explain uncrowding

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

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

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

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

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

On raw inertial measurements in dynamic networks

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

ALS Scheme using Extent-based Constraints for the Analysis of Chemical Reaction Systems

Basic gestalt laws cannot explain uncrowding

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

A Least-Squares/Relaxation Method for the Numerical Solution of the Three-Dimensional Elliptic Monge-Ampere Equation

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