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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Book ChapterDOI
Literature Review and Background
TL;DR: This chapter presents a literature review related to the main topics treated in this doctoral dissertation, a review of model predictive control focusing on non-centralized schemes, i.e., the architectures for decentralized and distributed MPC controllers.
Distributed Model Predictive Control of a Hydro-Power Valley by Dual Decomposition
Alexandra Grancharova,Sorin Olaru,Guillaume Sandou,Cristina Stoica Maniu,Pedro Rodriguez-Ayerbe +4 more
TL;DR: A suboptimal distributed MPC approach for linear interconnected systems is considered, where it is assumed that the systems are coupled through their control inputs and an optimal reference tracking problem for the overall system is solved.
Journal ArticleDOI
Analysis of Model and Iteration Dependencies in Distributed Feasible-Point Algorithms for Optimal Control Computation
TL;DR: The main results concern network structures for which the iterates of three feasible-point algorithms can be computed exactly on a subsystem-by-subsystem basis with access restricted to local model-data and algorithm-state information.
Proceedings ArticleDOI
A Stochastic Second-Order Proximal Method for Distributed Optimization
TL;DR: In this paper , a distributed stochastic second-order proximal (St-SoPro) method is proposed, which enables agents in a network to cooperatively minimize the sum of their local loss functions without any centralized coordination.
Journal ArticleDOI
An ADMM-Based Algorithm for Stabilizing Distributed Model Predictive Control without Terminal Cost and Constraint
Ramin Rostami,Daniel Görges +1 more
TL;DR: In this paper , a consensus form of alternating direction method of multipliers (ADMM) is employed to stabilize the DMPC scheme and simulation results reveal that this method outperforms the existing gradient-based approaches in terms of the required number of communications.
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.
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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.