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Decentralized Decision-Making Over Multi-Task Networks

Related publications (32)

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Linear convergence of primal-dual gradient methods and their performance in distributed optimization

Ali H. Sayed, Sulaiman A S A E Alghunaim

In this work, we revisit a classical incremental implementation of the primal-descent dual-ascent gradient method used for the solution of equality constrained optimization problems. We provide a short proof that establishes the linear (exponential) conver ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Layout optimization of wind farms constitutes an important and challenging task in complex terrain. This is especially due to the complex interactions of the boundary layer flows in complex terrain and wind turbine wakes, which renders wake modelling in co ...

ELSEVIER SCI LTD2020

Distributed Learning over Networks under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...

IEEE2019

Modeling and Assessment of Urban Energy Systems

Amarasinghage Tharindu Dasun Perera

Improving the energy sustainability of our cities involves the integration of multiple renewable energy technologies into existing energy infrastructure, stretching the capabilities of traditional energy systems to the limit. To consider the transition tak ...

EPFL2019

Distributed Optimization and Control using Operator Splitting Methods

Georgios Stathopoulos

The significant progress that has been made in recent years both in hardware implementations and in numerical computing has rendered real-time optimization-based control a viable option when it comes to advanced industrial applications. At the same time, t ...

EPFL2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Many medical image analysis tasks require complex learning strategies to reach a quality of image-based decision support that is sufficient in clinical practice. The analysis of medical texture in tomographic images, for example of lung tissue, is no excep ...

2016

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabili ...

2015

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive opti ...

EPFL2015

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Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...

2015