Publications related to Fast hard thresholding with Nesterov's gradient method

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Thomas Jean Debarre

The goal of this thesis is to study continuous-domain inverse problems for the reconstruction of sparse signals and to develop efficient algorithms to solve such problems computationally. The task is to recover a signal of interest as a continuous function ...

EPFL2022

Convex optimization in sums of Banach spaces

Michaël Unser, Shayan Aziznejad

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

2021

A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

2021

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Maryam Kamgarpour

We propose a distributed design method for decentralized control by exploiting the underlying sparsity properties of the problem. Our method is based on chordal decomposition of sparse block matrices and the alternating direction method of multipliers (ADM ...

2020

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Ali H. Sayed, Sulaiman A S A E Alghunaim, Ming Yan

This work studies multi-agent sharing optimization problems with the objective function being the sum of smooth local functions plus a convex (possibly non-smooth) function coupling all agents. This scenario arises in many machine learning and engineering ...

IEEE2020

Good Vibrations and Unknown Excitations

Dalia Salem Hassan Fahmy El Badawy

Inspired by the human ability to localize sounds, even with only one ear, as well as to recognize objects using active echolocation, we investigate the role of sound scattering and prior knowledge in regularizing ill-posed inverse problems in acoustics. In ...

EPFL2020

Learning with Structured Sparsity: From Discrete to Convex and Back.

Marwa El Halabi

In modern-data analysis applications, the abundance of data makes extracting meaningful information from it challenging, in terms of computation, storage, and interpretability. In this setting, exploiting sparsity in data has been essential to the developm ...

EPFL2018

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Jean-Philippe Thiran, Rafael Eduardo Carrillo Rangel, Rémi Charrier

OBJECTIVE The authors developed a new, real-time interactive inverse planning approach, based on a fully convex framework, to be used for Gamma Knife radiosurgery. METHODS The convex framework is based on the precomputation of a dictionary composed of the ...

AMER ASSOC NEUROLOGICAL SURGEONS2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H-infinity performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controller ...

IEEE2018

Fast hard thresholding with Nesterov's gradient method

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Convex optimization in sums of Banach spaces

A first-order primal-dual method with adaptivity to local smoothness

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

Good Vibrations and Unknown Excitations

Learning with Structured Sparsity: From Discrete to Convex and Back.

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

A Multi-Agent Primal-Dual Strategy for Composite Optimization over Distributed Features

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Convex optimization in sums of Banach spaces

A first-order primal-dual method with adaptivity to local smoothness

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Learning with Structured Sparsity: From Discrete to Convex and Back.

A real-time optimal inverse planning for Gamma Knife radiosurgery by convex optimization: description of the system and first dosimetry data

Good Vibrations and Unknown Excitations