Publications related to Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

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

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

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

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Nikolaos Arvanitopoulos Darginis

In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which captu ...

2014

Invariant manifolds with boundary for jump-diffusions

Damir Filipovic

We provide necessary and sufficient conditions for stochastic invariance of finite dimensional submanifolds with boundary in Hilbert spaces for stochastic partial differential equations driven by Wiener processes and Poisson random measures. ...

Univ Washington, Dept Mathematics2014

STOCHASTIC PROCESSES AND CONSTRUCTION OF BROWNIAN MOTION

Christophe-Aschkan Mery

This project offers a rigorous introduction to the tools needed to construct a continuous stochastic process. Among other things, we give a very detailed proof of the Kolmogorov continuity criterion. We then construct a Brownian Motion following the formal ...

EPFL2013

Noncontacting benchtop measurements of the elastic properties of shales

Thomas Emmanuel Blum

We evaluated a laser-based noncontacting method to measure the elastic anisotropy of horizontal shale cores. Whereas conventional transducer data contained an ambiguity between phase and group velocity measurements, small laser source and receiver footprin ...

2013

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We introduce a new wavelet-based method for the implementation of Total-Variation-type denoising. The data term is least-squares, while the regularization term is gradient-based. The particularity of our method is to exploit a link between the discrete gra ...

IEEE2012

Least-Squares Minimization Under Constraints

Pascal Fua, Mathieu Salzmann, Raquel Urtasun, Aydin Varol

Unconstrained Least-Squares minimization is a well-studied problem. For example, the Levenberg-Marquardt is extremely effective and numerous implementations are readily available. These algorithms are, however, not designed to perform least-squares minimiz ...

EPFL2010

Convergence properties of a decentralized Kalman filter

Maryam Kamgarpour

We consider the problem of decentralized Kalman filtering in a sensor network. Each sensor node implements a local Kalman filter based on its own measurements and the information exchanged with its neighbors. It combines the information received from other ...

IEEE2008

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Graph Chatbot

Chat with Graph Search

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

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

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

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Invariant manifolds with boundary for jump-diffusions

STOCHASTIC PROCESSES AND CONSTRUCTION OF BROWNIAN MOTION

Noncontacting benchtop measurements of the elastic properties of shales

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Least-Squares Minimization Under Constraints

Convergence properties of a decentralized Kalman filter

Laplacian Support Vector Analysis for Subspace Discriminative Learning

Invariant manifolds with boundary for jump-diffusions

Convergence properties of a decentralized Kalman filter

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

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

Least-Squares Minimization Under Constraints

STOCHASTIC PROCESSES AND CONSTRUCTION OF BROWNIAN MOTION

Noncontacting benchtop measurements of the elastic properties of shales

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising