Publications related to Rate of convergence

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

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

Remarks on Poincare and interpolation estimates for Truncated Hierarchical B-splines

Annalisa Buffa

This paper should be considered as an addendum to [A. Buffa and C. Giannelli, Adaptive isogeometric methods with hierarchical splines: Error estimator and convergence, Math. Models Methods Appl. Sci. 26 (2016) 1-25] and [A. Buffa and C. Giannelli, Adaptive ...

2021

Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates

Ali H. Sayed, Kun Yuan, Lucas Cesar Eduardo Cassano

This article develops a fully decentralized multiagent algorithm for policy evaluation. The proposed scheme can be applied to two distinct scenarios. In the first scenario, a collection of agents have distinct datasets gathered by following different behav ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Harsh Ambarishkumar Shukla

The research community has been making significant progress in hardware implementation, numerical computing and algorithm development for optimization-based control. However, there are two key challenges that still have to be overcome for optimization-base ...

EPFL2021

On the preconditioning of the INTERNODES matrix for applications in contact mechanics

In this paper, we extend the work of Günther-Hanssen (2020) in applying the INTERNODES method to problems in contact mechanics. In an initial work on this topic, Günther-Hanssen showed that the INTERNODES method could be successfully applied but also revea ...

2021

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Vincent Gramoli

There is an increasing interest in a fast-growing machine learning technique called Federated Learning (FL), in which the model training is distributed over mobile user equipment (UEs), exploiting UEs' local computation and training data. Despite its advan ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Sylvain Calinon, Amirreza Razmjoo Fard, Teguh Santoso Lembono

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

IEEE2021

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

Sub‐asymptotic motivation for new conditional multivariate extreme models

Anthony Christopher Davison, Thomas Lugrin, Jonathan A. Tawn

Statistical models for extreme values are generally derived from non-degenerate probabilistic limits that can be used to approximate the distribution of events that exceed a selected high threshold. If convergence to the limit distribution is slow, then th ...

2021

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

Nicolas Henri Bernard Flammarion, Scott William Pesme, Loucas Pillaud-Vivien

Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the dynamics of stochastic gradient descent over diagonal linear networks through i ...

2021

Rate of convergence

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

Remarks on Poincare and interpolation estimates for Truncated Hierarchical B-splines

Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

On the preconditioning of the INTERNODES matrix for applications in contact mechanics

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Sub‐asymptotic motivation for new conditional multivariate extreme models

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity

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

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

On the preconditioning of the INTERNODES matrix for applications in contact mechanics

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Remarks on Poincare and interpolation estimates for Truncated Hierarchical B-splines

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates

Federated Learning Over Wireless Networks: Convergence Analysis and Resource Allocation

Sub‐asymptotic motivation for new conditional multivariate extreme models

Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity