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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

Automatic Laboratory 1

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

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

Sample Complexity of Linear Quadratic Gaussian (LQG) Control for Output Feedback Systems

Stabilization and Accuracy Improvement of Power-Hardware-in-the-Loop Test Using a Robust Data-Driven Method

A Continuized View on Nesterov Acceleration

The role of PDE-based parameterization techniques in gradient-based IGA shape optimization applications

On the Equivalence of Youla, System-Level, and Input-Output Parameterizations

Whole Body Model Predictive Control with a Memory of Motion:Experiments on a Torque-Controlled Talos

Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Probabilistic Adaptive Control for Robust Behavior Imitation