An alternating direction method of multipliers (ADMM) solver is described for optimal resource allocation problems with separable convex quadratic costs and constraints and linear coupling constraints. We describe a parallel implementation of the solver on a graphics processing unit (GPU) using a bespoke quartic function minimizer. An application to robust optimal energy management in hybrid electric vehicles is described, and the results of numerical simulations comparing the computation times of the parallel GPU implementation with those of an equivalent serial implementation are presented
In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for...
We consider constraint-coupled optimization problems in which agents of a network aim to cooperative...
This work is motivated by a simple question: how to find a relatively good solution to a very large ...
An alternating direction method of multipliers (ADMM) solver is described for optimal resource alloc...
Large-scale convex optimization problems arise in various practical applications. Even though there ...
The alternating direction method of multipliers (ADMM) is a first-order optimization algorithm for s...
The alternating direction multiplier method (ADMM) was originally devised as an iterative method for...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/87...
In this letter we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm f...
Summarization: Lately, in engineering it has been necessary to develop algorithms that handle “big d...
International audienceIn large-scale control optimization problems, a decentralized control structur...
We propose an algorithm for solving quadratic programming (QP) problems with inequality and equality...
We consider a proximal operator given by a quadratic function subject to bound constraints and give ...
© 2018 Society for Industrial and Applied Mathematics. We consider the sequence acceleration proble...
Abstract. This paper introduces a parallel and distributed extension to the alternating direc-tion m...
In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for...
We consider constraint-coupled optimization problems in which agents of a network aim to cooperative...
This work is motivated by a simple question: how to find a relatively good solution to a very large ...
An alternating direction method of multipliers (ADMM) solver is described for optimal resource alloc...
Large-scale convex optimization problems arise in various practical applications. Even though there ...
The alternating direction method of multipliers (ADMM) is a first-order optimization algorithm for s...
The alternating direction multiplier method (ADMM) was originally devised as an iterative method for...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/87...
In this letter we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm f...
Summarization: Lately, in engineering it has been necessary to develop algorithms that handle “big d...
International audienceIn large-scale control optimization problems, a decentralized control structur...
We propose an algorithm for solving quadratic programming (QP) problems with inequality and equality...
We consider a proximal operator given by a quadratic function subject to bound constraints and give ...
© 2018 Society for Industrial and Applied Mathematics. We consider the sequence acceleration proble...
Abstract. This paper introduces a parallel and distributed extension to the alternating direc-tion m...
In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for...
We consider constraint-coupled optimization problems in which agents of a network aim to cooperative...
This work is motivated by a simple question: how to find a relatively good solution to a very large ...