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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Modeling, robust and distributed model predictive control for freeway networks
TL;DR: In this article, the authors developed and used multi-class traffic models to improve the effectiveness of Model Predictive Control (MPC) for traffic networks and developed robust MPC approaches for handling these uncertainties.
Journal ArticleDOI
Partition-based multi-agent optimization in the presence of lossy and asynchronous communication
TL;DR: In this paper, the authors proposed a multi-agent partition-based convex optimization algorithm based on the block Jacobi iteration, where each agent eventually computes only the optimal values for its own variables via local communication with its neighbors.
Journal ArticleDOI
Hierarchical Scheduling and Utility Disturbance Management in the Process Industry
Anna Lindholm,Charlotta Johnsson,Nils-Hassan Quttineh,Helene Lidestam,Mathias Henningsson,Joakim Wikner,Ou Tang,Nils-Petter Nytzén,Krister Forsman +8 more
TL;DR: Optimization in two timescales is suggested to handle the scheduling and disturbance management problems in a hierarchical fashion together with the integration towards production scheduling.
Proceedings Article
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
D. A. Kovalev,Aleksandr Beznosikov,Abdurakhmon Sadiev,Michael Persiianov,Peter Richtárik,Alexander Gasnikov +5 more
TL;DR: This work considers decentralized stochastic (sum-type) variational inequalities over fixed and time-varying networks and presents lower complexity bounds for both communication and local iterations and construct optimal algorithms that match these lower bounds.
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A Distributed Augmented Lagrangian Method Over Stochastic Networks for Economic Dispatch of Large-Scale Energy Systems
TL;DR: The scheme uses a distributed optimization algorithm that works over random communication networks and asynchronous updates, implying the resiliency of the proposed scheme with respect to communication problems, such as link failures, data packet drops, and delays.
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.