Publications associées à Derivatives with respect to metrics and applications: subgradient marching algorithm

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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

Derivatives with respect to metrics and applications: subgradient marching algorithm

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The effect of smooth parametrizations on nonconvex optimization landscapes

Spatiotemporal energy-density distribution of time-reversed electromagnetic fields

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Continuation Methods For Riemannian Optimization

Learning to Represent and Reconstruct 3D Deformable Objects

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

Geodesic flows on real forms of complex semi-simple Lie groups of rigid body type

Adaptive Gradient Descent without Descent

Sampling can be faster than optimization

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

The effect of smooth parametrizations on nonconvex optimization landscapes

Adaptive Gradient Descent without Descent

Sampling can be faster than optimization

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

Continuation Methods For Riemannian Optimization

Improving hand and wrist activity detection using tactile sensors and tensor regression methods on Riemannian manifolds

Learning to Represent and Reconstruct 3D Deformable Objects

Spatiotemporal energy-density distribution of time-reversed electromagnetic fields

Geodesic flows on real forms of complex semi-simple Lie groups of rigid body type

An Accelerated First-Order Method for Non-convex Optimization on Manifolds