The Experts below are selected from a list of 47976 Experts worldwide ranked by ideXlab platform
Maria Prandini - One of the best experts on this subject based on the ideXlab platform.
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regularized jacobi iteration for decentralized convex quadratic optimization with separable constraints
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multiagent, convex quadratic optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so-called Jacobi algorithm for decentralized computation in such problems. We provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem under more relaxed conditions on the regularization coefficient from those available in the literature, and in particular with respect to scaled projected gradient algorithms. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
Automatica, 2019Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:Abstract We address the optimization of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent paper to the MILP approximate solution via dual decomposition and constraint tightening, but shows finite-time convergence to a feasible solution and provides sharper performance guarantees by means of an adaptive tightening. The two approaches are compared on a plug-in electric vehicles optimal charging problem.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
arXiv: Optimization and Control, 2017Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:We address the optimal design of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent method to the MILP approximate solution via dual decomposition and constraint tightening, and presents the advantage of guaranteeing feasibility in finite-time and providing better performance guarantees. The two approaches are compared on a numerical example on plug-in electric vehicles optimal charging.
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regularized jacobi iteration for decentralized convex optimization with separable constraints
arXiv: Optimization and Control, 2016Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multi-agent, convex optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so called Jacobi algorithm for decentralized computation in such problems. We first consider the case where the objective function is quadratic, and provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem. Moreover, we quantify the potential benefits of such an iterative scheme by comparing it against a scaled projected gradient algorithm. We then consider the general case and show that all limit points of the proposed iteration are optimal solutions of the centralized problem. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles, where, as opposed to earlier approaches, we show convergence to an optimal charging scheme for a finite, possibly large, number of vehicles.
Kostas Margellos - One of the best experts on this subject based on the ideXlab platform.
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regularized jacobi iteration for decentralized convex quadratic optimization with separable constraints
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multiagent, convex quadratic optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so-called Jacobi algorithm for decentralized computation in such problems. We provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem under more relaxed conditions on the regularization coefficient from those available in the literature, and in particular with respect to scaled projected gradient algorithms. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
Automatica, 2019Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:Abstract We address the optimization of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent paper to the MILP approximate solution via dual decomposition and constraint tightening, but shows finite-time convergence to a feasible solution and provides sharper performance guarantees by means of an adaptive tightening. The two approaches are compared on a plug-in electric vehicles optimal charging problem.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
arXiv: Optimization and Control, 2017Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:We address the optimal design of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent method to the MILP approximate solution via dual decomposition and constraint tightening, and presents the advantage of guaranteeing feasibility in finite-time and providing better performance guarantees. The two approaches are compared on a numerical example on plug-in electric vehicles optimal charging.
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regularized jacobi iteration for decentralized convex optimization with separable constraints
arXiv: Optimization and Control, 2016Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multi-agent, convex optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so called Jacobi algorithm for decentralized computation in such problems. We first consider the case where the objective function is quadratic, and provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem. Moreover, we quantify the potential benefits of such an iterative scheme by comparing it against a scaled projected gradient algorithm. We then consider the general case and show that all limit points of the proposed iteration are optimal solutions of the centralized problem. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles, where, as opposed to earlier approaches, we show convergence to an optimal charging scheme for a finite, possibly large, number of vehicles.
Luca Deori - One of the best experts on this subject based on the ideXlab platform.
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regularized jacobi iteration for decentralized convex quadratic optimization with separable constraints
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multiagent, convex quadratic optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so-called Jacobi algorithm for decentralized computation in such problems. We provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem under more relaxed conditions on the regularization coefficient from those available in the literature, and in particular with respect to scaled projected gradient algorithms. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles.
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regularized jacobi iteration for decentralized convex optimization with separable constraints
arXiv: Optimization and Control, 2016Co-Authors: Luca Deori, Kostas Margellos, Maria PrandiniAbstract:We consider multi-agent, convex optimization programs subject to separable constraints, where the constraint function of each agent involves only its local Decision Vector, while the Decision Vectors of all agents are coupled via a common objective function. We focus on a regularized variant of the so called Jacobi algorithm for decentralized computation in such problems. We first consider the case where the objective function is quadratic, and provide a fixed-point theoretic analysis showing that the algorithm converges to a minimizer of the centralized problem. Moreover, we quantify the potential benefits of such an iterative scheme by comparing it against a scaled projected gradient algorithm. We then consider the general case and show that all limit points of the proposed iteration are optimal solutions of the centralized problem. The efficacy of the proposed algorithm is illustrated by applying it to the problem of optimal charging of electric vehicles, where, as opposed to earlier approaches, we show convergence to an optimal charging scheme for a finite, possibly large, number of vehicles.
Alessandro Falsone - One of the best experts on this subject based on the ideXlab platform.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
Automatica, 2019Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:Abstract We address the optimization of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent paper to the MILP approximate solution via dual decomposition and constraint tightening, but shows finite-time convergence to a feasible solution and provides sharper performance guarantees by means of an adaptive tightening. The two approaches are compared on a plug-in electric vehicles optimal charging problem.
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a decentralized approach to multi agent milps finite time feasibility and performance guarantees
arXiv: Optimization and Control, 2017Co-Authors: Alessandro Falsone, Kostas Margellos, Maria PrandiniAbstract:We address the optimal design of a large scale multi-agent system where each agent has discrete and/or continuous Decision variables that need to be set so as to optimize the sum of linear local cost functions, in presence of linear local and global constraints. The problem reduces to a Mixed Integer Linear Program (MILP) that is here addressed according to a decentralized iterative scheme based on dual decomposition, where each agent determines its Decision Vector by solving a smaller MILP involving its local cost function and constraint given some dual variable, whereas a central unit enforces the global coupling constraint by updating the dual variable based on the tentative primal solutions of all agents. An appropriate tightening of the coupling constraint through iterations allows to obtain a solution that is feasible for the original MILP. The proposed approach is inspired by a recent method to the MILP approximate solution via dual decomposition and constraint tightening, and presents the advantage of guaranteeing feasibility in finite-time and providing better performance guarantees. The two approaches are compared on a numerical example on plug-in electric vehicles optimal charging.
Michael J Neely - One of the best experts on this subject based on the ideXlab platform.
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time average stochastic optimization with non convex Decision set and its convergence
Modeling and Optimization in Mobile Ad-Hoc and Wireless Networks, 2015Co-Authors: Sucha Supittayapornpong, Michael J NeelyAbstract:This paper considers time-average stochastic optimization, where a time average Decision Vector, an average of Decision Vectors chosen in every time step from a time-varying (possibly non-convex) set, minimizes a convex objective function and satisfies convex constraints. This formulation has applications in networking and operations research. In general, time-average stochastic optimization can be solved by a Lyapunov optimization technique. This paper shows that the technique exhibits a transient phase and a steady state phase. When the problem has a unique Vector of Lagrange multipliers, the convergence time can be improved. By starting the time average in the steady state, the convergence times become O(1/e) under a locally-polyhedral assumption and O(1/e1.5) under a locally-non-polyhedral assumption, where e denotes the proximity to the optimal objective cost.
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time average stochastic optimization with non convex Decision set and its convergence
arXiv: Optimization and Control, 2014Co-Authors: Sucha Supittayapornpong, Michael J NeelyAbstract:This paper considers time-average stochastic optimization, where a time average Decision Vector, an average of Decision Vectors chosen in every time step from a time-varying (possibly non-convex) set, minimizes a convex objective function and satisfies convex constraints. This formulation has applications in networking and operations research. In general, time-average stochastic optimization can be solved by a Lyapunov optimization technique. This paper shows that the technique exhibits a transient phase and a steady state phase. When the problem has a unique Vector of Lagrange multipliers, the convergence time can be improved. By starting the time average in the steady state the convergence times become $O(1/\epsilon)$ under a locally-polyhedral assumption and $O(1/\epsilon^{1.5})$ under a locally-non-polyhedral assumption, where $\epsilon$ denotes the proximity to the optimal objective cost. Simulations suggest that the results may hold more generally without the unique Lagrange multiplier assumption.
