Name
Abstract | ||
|---|---|---|
It is shown that if the discretization of the state space is sufficiently fine and if the limiting trajectory is an interior point of the admissible policies then the state increment dynamic programming technique (SIDP) has linear convergence, and the coefficient of convergence can be explicitly related to the control problem components. In the course of this analysis, it is seen that the SIDP method is, in essence, a nonlinear generalization of the block Gauss-Seidel algorithm for solving linear equations. Some consequences of this insight are suggested, and a computational example comparing SIDP with differential dynamic programming is offered. |
Year | DOI | Venue |
|---|---|---|
1983 | 10.1016/0005-1098(83)90074-2 | Automatica |
Keywords | Field | DocType |
Computational method,computer control,dynamic programming,large-scale system,multivariable control system,optimal system | Convergence (routing),Discretization,Dynamic programming,Mathematical optimization,Nonlinear system,Differential dynamic programming,Control theory,Rate of convergence,Interior point method,State space,Mathematics | Journal |
Volume | Issue | ISSN |
19 | 1 | 0005-1098 |
Citations | PageRank | References |
1 | 0.54 | 3 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. J. Yakowitz | 1 | 34 | 4.85 |