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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 6 of 6 (0.106 seconds)| 1 |
On the Parameter Selection Problem in the Newton-ADI Iteration for Large Scale Riccati EquationsBenner, Peter, Mena, Hermann, Saak, Jens 26 November 2007 (has links) (PDF)
The numerical treatment of linear-quadratic regulator problems for
parabolic partial differential equations (PDEs) on infinite time horizons
requires the solution of large scale algebraic Riccati equations (ARE).
The Newton-ADI iteration is an efficient numerical method for this task.
It includes the solution of a Lyapunov equation by the alternating directions
implicit (ADI) algorithm in each iteration step. On finite time
intervals the solution of a large scale differential Riccati equation is required.
This can be solved by a backward differentiation formula (BDF)
method, which needs to solve an ARE in each time step.
Here, we study the selection of shift parameters for the ADI method.
This leads to a rational min-max-problem which has been considered by
many authors. Since knowledge about the complete complex spectrum
is crucial for computing the optimal solution, this is infeasible for the
large scale systems arising from finite element discretization of PDEs.
Therefore several alternatives for computing suboptimal parameters are
discussed and compared for numerical examples.
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| 2 |
On the numerical solution of large-scale sparse discrete-time Riccati equationsBenner, Peter, Faßbender, Heike 04 March 2010 (has links) (PDF)
The numerical solution of Stein (aka discrete Lyapunov) equations is the primary step in Newton's method for the solution of discrete-time algebraic Riccati equations (DARE). Here we present a low-rank Smith method as well as a low-rank alternating-direction-implicit-iteration to compute low-rank approximations to solutions of Stein equations arising in this context. Numerical results are given to verify the efficiency and accuracy of the proposed algorithms.
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| 3 |
On the Parameter Selection Problem in the Newton-ADI Iteration for Large Scale Riccati EquationsBenner, Peter, Mena, Hermann, Saak, Jens 26 November 2007 (has links)
The numerical treatment of linear-quadratic regulator problems for
parabolic partial differential equations (PDEs) on infinite time horizons
requires the solution of large scale algebraic Riccati equations (ARE).
The Newton-ADI iteration is an efficient numerical method for this task.
It includes the solution of a Lyapunov equation by the alternating directions
implicit (ADI) algorithm in each iteration step. On finite time
intervals the solution of a large scale differential Riccati equation is required.
This can be solved by a backward differentiation formula (BDF)
method, which needs to solve an ARE in each time step.
Here, we study the selection of shift parameters for the ADI method.
This leads to a rational min-max-problem which has been considered by
many authors. Since knowledge about the complete complex spectrum
is crucial for computing the optimal solution, this is infeasible for the
large scale systems arising from finite element discretization of PDEs.
Therefore several alternatives for computing suboptimal parameters are
discussed and compared for numerical examples.
|
| 4 |
On the numerical solution of large-scale sparse discrete-time Riccati equationsBenner, Peter, Faßbender, Heike 04 March 2010 (has links)
The numerical solution of Stein (aka discrete Lyapunov) equations is the primary step in Newton's method for the solution of discrete-time algebraic Riccati equations (DARE). Here we present a low-rank Smith method as well as a low-rank alternating-direction-implicit-iteration to compute low-rank approximations to solutions of Stein equations arising in this context. Numerical results are given to verify the efficiency and accuracy of the proposed algorithms.
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| 5 |
A cyclic low rank Smith method for large, sparse Lyapunov equations with applications in model reduction and optimal controlPenzl, T. 30 October 1998 (has links) (PDF)
We present a new method for the computation of low rank approximations
to the solution of large, sparse, stable Lyapunov equations. It is based
on a generalization of the classical Smith method and profits by the
usual low rank property of the right hand side matrix.
The requirements of the method are moderate with respect to both
computational cost and memory.
Hence, it provides a possibility to tackle large scale control
problems.
Besides the efficient solution of the matrix equation itself,
a thorough integration of the method into several control
algorithms can improve their performance
to a high degree.
This is demonstrated for algorithms
for model reduction and optimal control.
Furthermore, we propose a heuristic for determining a set of
suboptimal ADI shift parameters. This heuristic, which is based on a
pair of Arnoldi processes, does not require any a priori
knowledge on the spectrum of
the coefficient matrix of the Lyapunov equation.
Numerical experiments show the efficiency of the iterative scheme
combined with the heuristic for the ADI parameters.
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| 6 |
A cyclic low rank Smith method for large, sparse Lyapunov equations with applications in model reduction and optimal controlPenzl, T. 30 October 1998 (has links)
We present a new method for the computation of low rank approximations
to the solution of large, sparse, stable Lyapunov equations. It is based
on a generalization of the classical Smith method and profits by the
usual low rank property of the right hand side matrix.
The requirements of the method are moderate with respect to both
computational cost and memory.
Hence, it provides a possibility to tackle large scale control
problems.
Besides the efficient solution of the matrix equation itself,
a thorough integration of the method into several control
algorithms can improve their performance
to a high degree.
This is demonstrated for algorithms
for model reduction and optimal control.
Furthermore, we propose a heuristic for determining a set of
suboptimal ADI shift parameters. This heuristic, which is based on a
pair of Arnoldi processes, does not require any a priori
knowledge on the spectrum of
the coefficient matrix of the Lyapunov equation.
Numerical experiments show the efficiency of the iterative scheme
combined with the heuristic for the ADI parameters.
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