Publications associées à Fast hard thresholding with Nesterov's gradient method

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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

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

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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

Good Vibrations and Unknown Excitations

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

Distributed Design for Decentralized Control using Chordal Decomposition and ADMM

Convex optimization in sums of Banach spaces

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

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

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