Accelerated gradient methods and dual decomposition in distributed model predictive control
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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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A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems
TL;DR: A review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models is presented and some challenges are raised to guide the future research.
Journal ArticleDOI
Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning
Benjamin Karg,Sergio Lucia +1 more
TL;DR: It is shown that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model predictive control (MPC) of linear time-invariant systems.
Journal ArticleDOI
An Augmented Lagrangian Based Algorithm for Distributed NonConvex Optimization
TL;DR: A parallelizable method is proposed that combines ideas from the fields of sequential quadratic programming and augmented Lagrangian algorithms that negotiates shared dual variables that may be interpreted as prices, a concept employed in dual decomposition methods and the alternating direction method of multipliers (ADMM).
Journal ArticleDOI
Maximum Hands-Off Control: A Paradigm of Control Effort Minimization
TL;DR: In this article, the maximum hands-off control is defined as the minimum support (or sparsest) per unit time among all control objectives that achieve control objectives, and the equivalence between the maximum handoff control and the optimal control under a uniqueness assumption called normality is shown.
The decomposition algorithm for linear programming: notes on linear programming and extensions-part 57.
George B. Dantzig,Philip Wolfe +1 more
TL;DR: In this paper, a procedure for the efficient computational solution of linear programs having a certain structural property characteristic of a large class of problems of practical interest is presented, which makes possible the decomposition of the problem into a sequence of small linear programs whose iterate solutions solve the given problem through a generalization of the simple method for linear programming.
References
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Distributed asynchronous deterministic and stochastic gradient optimization algorithms
TL;DR: A model for asynchronous distributed computation is presented and it is shown that natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms retain the desirable convergence properties of their centralized counterparts.
Proceedings ArticleDOI
Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms
TL;DR: A model for asynchronous distributed computation is presented and it is shown that natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms retain the desirable convergence properties of their centralized counterparts.
Journal ArticleDOI
The decomposition algorithm for linear programs
George B. Dantzig,Philip Wolfe +1 more
TL;DR: In this article, a procedure for the efficient computational solution of linear programs having a certain structural property characteristic of a large class of problems of practical interest is presented, which makes possible the decomposition of the problem into a sequence of small linear programs whose iterated solutions solve the given problem through a generalization of the simplex method for linear programming.