The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform
J A Rossiter - One of the best experts on this subject based on the ideXlab platform.
-
a comparison of the computational efficiency of multi parametric predictive control using Generalised Function parameterisations
IFAC Proceedings Volumes, 2012Co-Authors: Bilal Khan, J A RossiterAbstract:Abstract This paper considers the computational efficiency of using Generalised Function parameterisations for multi-parametric quadratic programming (mp-QP) solutions to MPC. Earlier work demonstrated the potential of Laguerre parameterisations for improving computational efficiency in that the parametric solutions either required fewer regions and/or gave larger volumes. This paper considers the potential of extending this concept to more general Function parameterisations. Specifically the aim is to consider to what extent different Function parameterisations affect the parametric solution complexity and feasible volumes. Extensive simulation results which suggest there are indeed benefits from using more general parameterisations than Laguerre.
-
a comparison of the computational efficiency of Generalised Function mpc using active set methods
IFAC Proceedings Volumes, 2012Co-Authors: Bilal Khan, J A RossiterAbstract:Abstract This paper considers the computational efficiency of the recently developed Generalised Function parameterisations predictive control algorithms using active set methods. Alternative parameterisations have been shown to improve the volume of the feasible region when the number of degrees of freedom is limited. However, earlier work also demonstrates that some of the structure of optimisation problem is lost when using these parameterisations and thus requires more computation operations per step. This paper considers the dense problem structure arising from removing any redundant constraints and then considers the computational efficiency using the minimal constraint sets. Extensive simulation results suggest there can still be benefits from using more general parameterisations, although less significant then indicated by the number of degrees of freedom alone.
Bilal Khan - One of the best experts on this subject based on the ideXlab platform.
-
a comparison of the computational efficiency of multi parametric predictive control using Generalised Function parameterisations
IFAC Proceedings Volumes, 2012Co-Authors: Bilal Khan, J A RossiterAbstract:Abstract This paper considers the computational efficiency of using Generalised Function parameterisations for multi-parametric quadratic programming (mp-QP) solutions to MPC. Earlier work demonstrated the potential of Laguerre parameterisations for improving computational efficiency in that the parametric solutions either required fewer regions and/or gave larger volumes. This paper considers the potential of extending this concept to more general Function parameterisations. Specifically the aim is to consider to what extent different Function parameterisations affect the parametric solution complexity and feasible volumes. Extensive simulation results which suggest there are indeed benefits from using more general parameterisations than Laguerre.
-
a comparison of the computational efficiency of Generalised Function mpc using active set methods
IFAC Proceedings Volumes, 2012Co-Authors: Bilal Khan, J A RossiterAbstract:Abstract This paper considers the computational efficiency of the recently developed Generalised Function parameterisations predictive control algorithms using active set methods. Alternative parameterisations have been shown to improve the volume of the feasible region when the number of degrees of freedom is limited. However, earlier work also demonstrates that some of the structure of optimisation problem is lost when using these parameterisations and thus requires more computation operations per step. This paper considers the dense problem structure arising from removing any redundant constraints and then considers the computational efficiency using the minimal constraint sets. Extensive simulation results suggest there can still be benefits from using more general parameterisations, although less significant then indicated by the number of degrees of freedom alone.
Marco Rossi - One of the best experts on this subject based on the ideXlab platform.
-
the Generalised scaling Function a note
Nuclear Physics, 2010Co-Authors: Davide Fioravanti, Paolo Grinza, Marco RossiAbstract:Abstract A method for determining the Generalised scaling Functions f n ( g ) arising in the high spin behaviour of long operator anomalous dimensions in the planar sl ( 2 ) sector of N = 4 SYM is proposed. The all-order strong coupling expansion is detailed for the prototypical third and fourth scaling Functions ( f 3 ( g ) , f 4 ( g ) , respectively) and — together with high-precision numerical computations — reveals itself as crucial for disentangling the emergence of the O ( 6 ) Non-Linear Sigma Model mass-gap from different SYM ‘mass’ Functions. Remarkably, only the fourth one gains contribution from the non-BES reducible densities and also shows up, as first, NLSM interaction and specific model dependence. Finally, the computation of the n-th Generalised Function is sketched and might be easily finalised for checks versus the computations in the sigma model or the complete string theory.
Davide Fioravanti - One of the best experts on this subject based on the ideXlab platform.
-
the Generalised scaling Function a note
Nuclear Physics, 2010Co-Authors: Davide Fioravanti, Paolo Grinza, Marco RossiAbstract:Abstract A method for determining the Generalised scaling Functions f n ( g ) arising in the high spin behaviour of long operator anomalous dimensions in the planar sl ( 2 ) sector of N = 4 SYM is proposed. The all-order strong coupling expansion is detailed for the prototypical third and fourth scaling Functions ( f 3 ( g ) , f 4 ( g ) , respectively) and — together with high-precision numerical computations — reveals itself as crucial for disentangling the emergence of the O ( 6 ) Non-Linear Sigma Model mass-gap from different SYM ‘mass’ Functions. Remarkably, only the fourth one gains contribution from the non-BES reducible densities and also shows up, as first, NLSM interaction and specific model dependence. Finally, the computation of the n-th Generalised Function is sketched and might be easily finalised for checks versus the computations in the sigma model or the complete string theory.
Paolo Grinza - One of the best experts on this subject based on the ideXlab platform.
-
the Generalised scaling Function a note
Nuclear Physics, 2010Co-Authors: Davide Fioravanti, Paolo Grinza, Marco RossiAbstract:Abstract A method for determining the Generalised scaling Functions f n ( g ) arising in the high spin behaviour of long operator anomalous dimensions in the planar sl ( 2 ) sector of N = 4 SYM is proposed. The all-order strong coupling expansion is detailed for the prototypical third and fourth scaling Functions ( f 3 ( g ) , f 4 ( g ) , respectively) and — together with high-precision numerical computations — reveals itself as crucial for disentangling the emergence of the O ( 6 ) Non-Linear Sigma Model mass-gap from different SYM ‘mass’ Functions. Remarkably, only the fourth one gains contribution from the non-BES reducible densities and also shows up, as first, NLSM interaction and specific model dependence. Finally, the computation of the n-th Generalised Function is sketched and might be easily finalised for checks versus the computations in the sigma model or the complete string theory.