Publications associées à Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Splitting Methods for Distributed Optimization and Control

This thesis contributes towards the design and analysis of fast and distributed optimization algorithms based on splitting techniques, such as proximal gradient methods or alternation minimization algorithms, with the application of solving model predictiv ...

EPFL2016

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Michaël Unser, Martin Kurt Storath

We consider

L ^{ 1 }

-TV regularization of univariate signals with values on the real line or on the unit circle. While the real data space leads to a convex optimization problem, the problem is nonconvex for circle-valued data. In this paper, we deriv ...

SIAM2016

Fast Robust PCA on Graphs

Pierre Vandergheynst, Gilles Puy, Nathanaël Perraudin, Nauman Shahid, Vasilis Kalofolias

Mining useful clusters from high dimensional data has received significant attention of the computer vision and pattern recognition community in the recent years. Linear and non-linear dimensionality reduction has played an important role to overcome the c ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Decomposition Strategies for Nonconvex Problems, a Parametric Approach

Jean-Hubert Hours

This thesis deals with the development of numerical methods for solving nonconvex optimisation problems by means of decomposition and continuation techniques. We first introduce a novel decomposition algorithm based on alternating gradient projections and ...

EPFL2016

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Dominique Bonvin

This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise ...

2016

Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

Volkan Cevher

The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of applications. Approximate message passing (AMP) methods, based on Gaussian appr ...

2016

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Michael Maximilian Steinlechner

In this thesis, we present a Riemannian framework for the solution of high-dimensional optimization problems with an underlying low-rank tensor structure. Here, the high-dimensionality refers to the size of the search space, while the cost function is scal ...

EPFL2016

Aircraft panel with sensorless active sound power reduction capabilities through virtual mechanical impedances

Romain Boulandet

This paper deals with an active structural acoustic control approach to reduce the transmission of tonal noise in aircraft cabins. The focus is on the practical implementation of the virtual mechanical impedances method by using sensoriactuators instead of ...

Elsevier2016

MATHICSE Technical Report : A novel iterative method to approximate structured singular values

Daniel Kressner

A novel method for approximating structured singular values (also known as

\mu

- values) is proposed and investigated. These quantities constitute an important tool in the stability analysis of uncertain linear control systems as well as in structured eig ...

MATHICSE2016

A new mathematical representation of demand using choice-based optimization method

Michel Bierlaire, Shadi Sharif Azadeh, Meritxell Pacheco Paneque

Discrete choice models are the state-of-the-art for the mathematical modeling of demand. Based on the concept of random utility, they are able to predict the choice behavior of individuals. However, these models are highly non linear and non convex in the ...

2016

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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Splitting Methods for Distributed Optimization and Control

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Fast Robust PCA on Graphs

Decomposition Strategies for Nonconvex Problems, a Parametric Approach

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach

Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Aircraft panel with sensorless active sound power reduction capabilities through virtual mechanical impedances

MATHICSE Technical Report : A novel iterative method to approximate structured singular values

A new mathematical representation of demand using choice-based optimization method

Decomposition Strategies for Nonconvex Problems, a Parametric Approach

Exact Algorithms for $ L ^{ 1 } $ -TV Regularization of Real-Valued or Circle-Valued Signals

Splitting Methods for Distributed Optimization and Control

MATHICSE Technical Report : A novel iterative method to approximate structured singular values

Riemannian Optimization for Solving High-Dimensional Problems with Low-Rank Tensor Structure

Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

Aircraft panel with sensorless active sound power reduction capabilities through virtual mechanical impedances

A new mathematical representation of demand using choice-based optimization method

Fast Robust PCA on Graphs

Dynamic Optimization of Constrained Semi-Batch Processes Using Pontryagin’s Minimum Principle – An Effective Quasi-Newton Approach