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
On the Use of Difference of Log-Sum-Exp Neural Networks to Solve Data-Driven Model Predictive Control Tracking Problems
Sven Brüggemann,Corrado Possieri +1 more
TL;DR: In this article, the authors employ difference of log-sum-exp neural networks to generate a data-driven feedback controller based on Model Predictive Control (MPC) to track a given reference trajectory.
Proceedings ArticleDOI
ADMM-based Cooperative Control for Platooning of Connected and Autonomous Vehicles
TL;DR: In this article , the consensus cost function is formulated, constrained by minimum distance requirements between the vehicles, and the solution is derived via the alternating direction method of multipliers (ADMM) solver with minimal communication demands.
Non-Smooth Setting of Stochastic Decentralized Convex Optimization Problem Over Time-Varying Graphs
TL;DR: In this article , the authors studied a subclass of distributed optimization, namely decentralized optimization in a non-smooth setting, where agents can hold and communicate information about the value of the objective function, which corresponds to a gradient-free oracle.
Journal ArticleDOI
Price Based Linear Quadratic Control Under Transportation Delay
TL;DR: The main contribution is to give an implementation of the feedback law that gives the social optimum, that only depends on the local states and a set of prices defined by a distributed update rule, that align the social and user optimum in a budget neutral way.
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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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.