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Exact Diffusion for Distributed Optimization and Learning-Part I: Algorithm Development
Related publications (32)
Exact Diffusion for Distributed Optimization and Learning---Part I: Algorithm Development
Ali H. Sayed, Bicheng Ying, Kun Yuan
This work 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 to have a wider stability range and superior conv ...
2017Exact Diffusion for Distributed Optimization and Learning---Part II: Convergence Analysis
Ali H. Sayed, Bicheng Ying, Kun Yuan
Part I of this work 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 a larger set of combination policies than ea ...
2017Exact diffusion strategy for optimization by networked agents
Ali H. Sayed, Bicheng Ying, Kun Yuan
This work develops a distributed optimization algorithm with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown to have a wider stability range and superior convergence pe ...
IEEE2017Quantization 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 ...
2017Splitting Methods for Distributed Optimization and Control
This thesis contributes towards the design and analysis of fast and distributed optimization algorithms based on splitting techniques, such as proximal gradient methods or alternation minimization algorithms, with the application of solving model predictiv ...
EPFL2016Multi-index Stochastic Collocation for random PDEs
Fabio Nobile, Lorenzo Tamellini
In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a combination technique based on mixed differences of spatial approximations and quadratures over the ...
2016Distributed Modifier-Adaptation Schemes for the Real-Time Optimization of Interconnected Systems in the Presence of Structural Plant-Model Mismatch
Dominique Bonvin, René Uwe Schneider, Predrag Milosavljevic
The desire to operate chemical processes in a safe and economically optimal way has motivated the development of so-called real-time optimization (RTO) methods [1]. For continuous processes, these methods aim to compute safe and optimal steady-state set ...
2016Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity
Fabio Nobile, Lorenzo Tamellini
We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient is parametrized by means of a countable sequence of term ...
2016Analysis 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 Engineers2015Quantization 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 neighbours and the channel has a limited data-rate. A c ...
2015