Publications associées à Transformation de suite

Plane Waves Versus Correlation-Consistent Basis Sets: A Comparison of MP2 Non-Covalent Interaction Energies in the Complete Basis Set Limit

Ursula Röthlisberger, Justin Villard, Martin Peter Bircher

Second-order Moller-Plesset perturbation theory (MP2) is the most expedient wave function-based method for considering electron correlation in quantum chemical calculations and, as such, provides a cost-effective framework to assess the effects of basis se ...

Amer Chemical Soc2023

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever, Maria-Luiza Vladarean

We propose a stochastic conditional gradient method (CGM) for minimizing convex finitesum objectives formed as a sum of smooth and non-smooth terms. Existing CGM variants for this template either suffer from slow convergence rates, or require carefully inc ...

2022

On Preconditioning of Decentralized Gradient-Descent When Solving a System of Linear Equations

Nirupam Gupta

This article considers solving an overdetermined system of linear equations in peer-to-peer multiagent networks. The network is assumed to be synchronous and strongly connected. Each agent has a set of local data points, and their goal is to compute a line ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Ali H. Sayed, Kun Yuan, Sulaiman A S A E Alghunaim

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framewor ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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

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

On the Stability of FDTD-Based Numerical Codes to Evaluate Lightning-Induced Overvoltages in Overhead Transmission Lines

Farhad Rachidi-Haeri

One of the main issues that has to be faced when evaluating lightning-induced overvoltages on a realistic network configuration is to implement the algorithm that solves the field-to-line coupling problem into an electromagnetic simulator that is able to r ...

2020

On Convergence-Diagnostic based Step Sizes for Stochastic Gradient Descent

Nicolas Henri Bernard Flammarion, Scott William Pesme, Aymeric Daphnis Kévin Dieuleveut

Constant step-size Stochastic Gradient Descent exhibits two phases: a transient phase during which iterates make fast progress towards the optimum, followed by a stationary phase during which iterates oscillate around the optimal point. In this paper, we s ...

2020

Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

Ali H. Sayed, Bicheng Ying, Kun Yuan

Part I of this paper developed the exact diffusion algorithm to remove the bias that is characteristic of distributed solutions for deterministic optimization problems. The algorithm was shown to be applicable to the larger set of locally balanced left-sto ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development

Ali H. Sayed, Bicheng Ying, Kun Yuan

This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range an ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Location Accuracy Evaluation of ToA-Based Lightning Location Systems Over Mountainous Terrain

Farhad Rachidi-Haeri, Marcos Rubinstein, Wolfgang Schulz, Dongshuai Li

In this paper, we analyze the location error of time of arrival (ToA)-based lightning location systems (LLSs) caused by propagation effects over mountainous terrain around the Santis tower located in the Swiss Alps. The study is based on a full-wave three- ...

Amer Geophysical Union2017

Quantization Design for Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbors and the channel has a limited data-rate. A co ...

2017

Analysis of a Plane Wave-Excited Subwavelength Circular Aperture in a Planar Conducting Screen Illuminating a Multilayer Uniaxial Sample

Juan Ramon Mosig, Krzysztof Michalski

A spectral domain analysis is presented of a plane wave-excited subwavelength circular aperture in a planar perfectly conducting screen in close proximity to a multilayer stack, which may comprise uniaxially anisotropic slices. The formulation employs the ...

Institute of Electrical and Electronics Engineers2015

Quantization Design for Unconstrained Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider an unconstrained distributed optimization problem and assume that the bit rate of the communication in the network is limited. We propose a distributed optimization algorithm with an iteratively refining quantization design, which bounds the qu ...

2015

Splitting methods in control

Colin Neil Jones, Ye Pu, Georgios Stathopoulos, Alexander Szücs

The need for optimal control of processes under a restricted amount of resources renders first order optimization methods a viable option. Although computationally cheap, these methods typically suffer from slow convergence rates. In this work we discuss t ...

IEEE2014

A Statistical Analysis of Certain Iterative Learning Control Algorithms

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher

Iterative Learning Control (ILC) is a technique used to improve the tracking performance of systems carrying out repetitive tasks, which are affected by deterministic disturbances. The achievable performance is greatly degraded, however, when non-repeating ...

2008

A Comparison of Iterative Learning Control Algorithms with application to a Linear Motor System

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher

Iterative Learning Control (ILC) is a technique used to improve the tracking performance of systems carrying out repetitive tasks, which are affected by deterministic disturbances. The achievable performance is greatly degraded, however, when non-repeating ...

2006