Accelerated gradient methods and dual decomposition in distributed model predictive control
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TLDR
The evaluation shows that the proposed distributed optimization algorithm for mixed L"1/L"2-norm optimization based on accelerated gradient methods using dual decomposition can outperform current state-of-the-art optimization software CPLEX and MOSEK.About:
This article is published in Automatica.The article was published on 2013-03-01 and is currently open access. It has received 265 citations till now. The article focuses on the topics: Optimization problem & Duality (optimization).read more
Citations
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Proceedings ArticleDOI
A Sparse Polytopic LPV Controller for Fully-Distributed Nonlinear Optimal Control
TL;DR: A sparse controller is designed to stabilize trajectories of the nonlinear system at each iteration of the distributed algorithm to show the effectiveness of the strategy on simulations performed on a multi-agent formation control problem.
Book ChapterDOI
Distributed Robust Predictive Control of Linear Time-Varying Systems by Using Contractive Sets
Journal ArticleDOI
Accelerated Conjugate Gradient for Second-Order Blind Signal Separation
Hai Huyen Dam,Sven Nordholm +1 more
TL;DR: In this article , the authors proposed a new adaptive algorithm for the second-order blind signal separation (BSS) problem with convolutive mixtures by utilizing a combination of an accelerated gradient and a conjugate gradient method.
Journal ArticleDOI
Design and Analysis of Bayesian Model Predictive Controller
TL;DR: A novel predictive controller based on a Bayesian inferring nonlinear model (BMPC) is presented and the simulation results show that the closed loop control system demonstrates the abilities of anti-disturbance and robustness.
Posted Content
A Sparse Polytopic LPV Controller for Fully-Distributed Nonlinear Optimal Control.
TL;DR: In this article, the authors proposed a distributed optimal control for nonlinear dynamical systems over graph, that is large-scale systems in which the dynamics of each subsystem depends on neighboring states only.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Amir Beck,Marc Teboulle +1 more
TL;DR: A new fast iterative shrinkage-thresholding algorithm (FISTA) which preserves the computational simplicity of ISTA but with a global rate of convergence which is proven to be significantly better, both theoretically and practically.
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Introductory Lectures on Convex Optimization: A Basic Course
TL;DR: A polynomial-time interior-point method for linear optimization was proposed in this paper, where the complexity bound was not only in its complexity, but also in the theoretical pre- diction of its high efficiency was supported by excellent computational results.
Journal ArticleDOI
Smooth minimization of non-smooth functions
TL;DR: A new approach for constructing efficient schemes for non-smooth convex optimization is proposed, based on a special smoothing technique, which can be applied to functions with explicit max-structure, and can be considered as an alternative to black-box minimization.