Publications associées à Scalable Convex Methods for Phase Retrieval

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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

Scalable Convex Methods for Phase Retrieval

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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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

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

Adaptive Gradient Descent without Descent

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

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

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

Large Steps in Inverse Rendering of Geometry

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