Publications associées à Scalable Convex Methods for Phase Retrieval

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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

Scalable Convex Methods for Phase Retrieval

Graph Chatbot

Chattez avec Graph Search

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

A Newton Frank-Wolfe method for constrained self-concordant minimization

Large Steps in Inverse Rendering of Geometry

Adaptive Gradient Descent without Descent

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

MATHICSE Technical Report : Multi space reduced basis preconditioners for large-scale parametrized PDEs

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Adaptive Gradient Descent without Descent

Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

A Newton Frank-Wolfe method for constrained self-concordant minimization

Large Steps in Inverse Rendering of Geometry

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

A Non-Euclidean Gradient Descent Framework for Non-Convex Matrix Factorization

MATHICSE Technical Report : Multi space reduced basis preconditioners for large-scale parametrized PDEs