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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Proceedings ArticleDOI
Vulnerabilities in Lagrange-Based DMPC in the Context of Cyber-Security
TL;DR: An analysis of the vulnerability of a distributed model predictive control (DMPC) scheme in the context of cybersecurity and a consensus approach that dismisses the extreme control actions is presented as a way to protect the distributed system from potential threats.
Journal ArticleDOI
An accelerated dual method based on analytical extrapolation for distributed quadratic optimization of large-scale production complexes
TL;DR: This work proposes an extension of the widely used subgradient methods for inequality constrained distributed QPs, which is called analytical extrapolation (AE), and the analytical structure of the dual function is exploited to speed up convergence.
Two-layer distributed optimal control for energy system integration
TL;DR: In this article, the authors study how to embed dynamic agents that transform gas to power, or power to gas in the different energy grids, such as the gas, power, industry and heat grids in a distributed and optimal manner.
Proceedings ArticleDOI
A multi-agent projected dual gradient method with primal convergence guarantees
Jie Lu,Mikael Johansson +1 more
TL;DR: A novel algorithm is derived that allows the agents to collaboratively reach a decision that minimizes the sum of the loss functions over the intersection of the individual constraints.
Proceedings ArticleDOI
A Distributed Proximal Primal-Dual Algorithm for Nonsmooth Optimization with Coupling Constraints
Xuyang Wu,He Wang,Jie Lu +2 more
TL;DR: In this paper, a proximal primal-dual dual algorithm (PPDA) is proposed for convex optimization problems with nonlinear inequality and linear equality constraints, which achieves O(1/k) convergence rate with respect to optimality and feasibility.
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.