Publications associées à Non-convex optimization for robust multi-view imaging

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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

Non-convex optimization for robust multi-view imaging

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

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

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

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

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

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Universal and adaptive methods for robust stochastic optimization

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

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∞ Performance

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

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

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

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