This paper considers the usage of approximate inverses in a preconditioned fast dual proximal gradient method for Model Predictive Control (MPC). We show that for a dualization of the dynamic constraints, the dense preconditioner is an exponentially off-diagonally decaying matrix. By approximating the preconditioner by a banded matrix, the computational cost per iteration can be decreased, while early numerical tests indicate that the number of iterations is almost unaffected in cases where the off-diagonal decay is rapid
This paper presents structure exploitation techniques that lead to faster convergence of first-order...
This paper proposes a parallelizable real-time algorithm for model predictive control (MPC). In cont...
This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A p...
This paper considers the usage of approximate inverses in a preconditioned fast dual proximal gradie...
This paper is concerned with the computing efficiency of model predictive control (MPC) problems for...
This paper proposes a dual fast gradient-projection method for solving quadratic programming problem...
This paper proposes a dual fast gradient-projection method for solving quadratic programming problem...
Dual decomposition is an efficient tool in dealing with Model Predictive Control (MPC) problems, par...
This paper presents a real-time implementation of the proximal gradient method (PGM) in a model pred...
Model predictive control (MPC) is a modern control methodology that is based on the repetitive solut...
We consider distributed model predictive control (DMPC) where a sparse centralized optimization prob...
This thesis considers optimization methods for Model Predictive Control (MPC). MPC is the preferred ...
This paper describes a model predictive control (MPC) approach for discrete-time linear systems with...
. This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A...
Linear quadratic model predictive control (MPC) with input constraints leads to an optimization prob...
This paper presents structure exploitation techniques that lead to faster convergence of first-order...
This paper proposes a parallelizable real-time algorithm for model predictive control (MPC). In cont...
This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A p...
This paper considers the usage of approximate inverses in a preconditioned fast dual proximal gradie...
This paper is concerned with the computing efficiency of model predictive control (MPC) problems for...
This paper proposes a dual fast gradient-projection method for solving quadratic programming problem...
This paper proposes a dual fast gradient-projection method for solving quadratic programming problem...
Dual decomposition is an efficient tool in dealing with Model Predictive Control (MPC) problems, par...
This paper presents a real-time implementation of the proximal gradient method (PGM) in a model pred...
Model predictive control (MPC) is a modern control methodology that is based on the repetitive solut...
We consider distributed model predictive control (DMPC) where a sparse centralized optimization prob...
This thesis considers optimization methods for Model Predictive Control (MPC). MPC is the preferred ...
This paper describes a model predictive control (MPC) approach for discrete-time linear systems with...
. This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A...
Linear quadratic model predictive control (MPC) with input constraints leads to an optimization prob...
This paper presents structure exploitation techniques that lead to faster convergence of first-order...
This paper proposes a parallelizable real-time algorithm for model predictive control (MPC). In cont...
This paper is concerned with a new approach to preconditioning for large, sparse linear systems. A p...