A convex set of robust D—stabilizing controllers using Cauchy's argument principle

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.

In this thesis we propose and analyze algorithms for some numerical linear algebra tasks: finding low-rank approximations of matrices, computing matrix functions, and estimating the trace of matrices. In the first part, we consider algorithms for building ...

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

In recent years, power systems have evolved in physical and cyber-physical layers. In the physical layer, the changes are motivated by environmental concerns resulting in the integration of new types of generation/demand/storage into the grid. These integr ...

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...

We consider the problem of provably finding a stationary point of a smooth function to be minimized on the variety of bounded-rank matrices. This turns out to be unexpectedly delicate. We trace the difficulty back to a geometric obstacle: On a nonsmooth se ...

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then o ...

Today, automatic control is integrated into a wide spectrum of real-world systems such as electrical grids and transportation networks. Many of these systems comprise numerous interconnected agents, perform safety-critical operations, or generate large amo ...

We describe the first gradient methods on Riemannian manifolds to achieve accelerated rates in the non-convex case. Under Lipschitz assumptions on the Riemannian gradient and Hessian of the cost function, these methods find approximate first-order critical ...

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

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 ...

Large horsepower induction motors play a critical role in the operation of industrial facilities. In this respect, the distribution network operators dedicate a high priority to the operational safety of these motor loads. In this paper, the induction moto ...

A convex set of robust D—stabilizing controllers using Cauchy's argument principle

Chattez avec Graph Search

Fast deterministic and randomized algorithms for low-rank approximation, matrix functions, and trace estimation

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Data-driven Power Electronic Converter Control Design in Power System Applications

Stochastic distributed learning with gradient quantization and double-variance reduction

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

Proximal Point Imitation Learning

Data-driven and Safe Networked Control with Applications to Microgrids

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

Convex optimization in sums of Banach spaces

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

A convex model for induction motor starting transients imbedded in an OPF-based optimization problem