Accelerated gradient methods and dual decomposition in distributed model predictive control
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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Journal ArticleDOI
Structured Computation of Constrained Quadratic Optimal Controls for Heterogeneous Networks
Iman Shames,Michael Cantoni +1 more
TL;DR: The problem of computing finite-horizon constrained quadratic optimal controls is considered for networks of interconnected heterogeneous linear systems and a projection based iterative algorithm is studied.
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Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation
TL;DR: The Distributed and Localized Model Predictive Control (DLMPC) algorithm for large-scale linear systems as discussed by the authors is a distributed closed-loop model predictive control scheme where only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions.
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Convergence analysis of approximate primal solutions in dual first-order methods
Jie Lu,Mikael Johansson +1 more
TL;DR: It is demonstrated that primal convergence rate guarantees can also be obtained when the dual gradient is only locally Lipschitz, which is a rather restrictive assumption that does not hold for several important classes of problems.
Proceedings ArticleDOI
Constrained distributed MPC for LPV systems with actuator saturation and state delay
TL;DR: An iterative online algorithm for constrained distributed MPC is developed to coordinate the distributed controllers and is a flexible structure of robust control, which allows the independent computation of the state feedback laws for the subsystems.
Proceedings ArticleDOI
Fast Separable Terminal Cost Synthesis for Distributed MPC
TL;DR: A novel design approach based on matrix iteration which provides a fast solution and it can be effectively employed online for networked systems requiring online reconfiguration of the controllers typically in response to changes in the network structure is proposed.
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.
Journal ArticleDOI
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.
Book
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.