Concept

Optimisation par essaims particulaires

Publications associées (296)

From Data to Decisions: Distributionally Robust Optimization is Optimal

We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...

2020

Convergence without Convexity: Sampling, Optimization, and Games

Ya-Ping Hsieh

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

EPFL2020

Real-time optimization of load sharing for gas compressors in the presence of uncertainty

Dominique Bonvin, Timm Faulwasser, Alejandro Gabriel Marchetti, Predrag Milosavljevic

This paper investigates the problem of load-sharing optimization of gas compressors in the presence of uncertainty. The objective is to operate a set of compressor units in an energy-efficient way, while at the same time meeting a varying load demand. The ...

ELSEVIER SCI LTD2020

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

Daniel Kuhn

In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming o ...

2020

Being Part of the Swarm: Experiencing Human-Swarm Interaction with VR and Tangible Robots

Pierre Dillenbourg, Barbara Bruno, Arzu Güneysu Özgür, Kevin Gonyop Kim, Hala Khodr

A swarm is the coherent behavior that emerges ubiquitously fromsimple interaction rules between self-organized agents. Understand-ing swarms is of utmost importance in many disciplines and jobs,but hard to teach due to the elusive nature of the phenomenon, ...

2020

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

Design and Calibration of a Lightweight Physics-Based Model for Fluid-Mediated Self-Assembly of Robotic Modules

Alcherio Martinoli, Hala Khodr, Bahar Haghighat

In this paper, we consider a system consisting of multiple floating robotic modules performing self-assembly. Faithfully modeling such a system and its inter-module interactions typically involves capturing the hydrodynamic forces acting on the modules usi ...

Springer2019

Very-High-Speed Miniaturized Permanent Magnet Motors: Design and Optimization

Yves Perriard, Guillaume Paul-André Burnand

This paper aims to present an optimization method for very-high-speed electrical machines based on multiphysical analytical models. Analytical models allow a fast design but have to be robust and reliable. The set of models presented is very complete and c ...

IEEE2019

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

Controlling the bias error of Fokker- Planck methods for rarefied gas dynamics simulations

Direct simulation Monte-Carlo (DSMC) is the most established method for rarefied gas flow simulations. It is valid from continuum to near vacuum, but in cases involving small Knudsen numbers (Kn), it suffers from high computational cost. The Fokker-Planck ...

2019

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