Abstract
We consider linear systems of equations and solution approximations derived by projection on a low-dimensional subspace. We propose stochastic iterative algorithms, based on simulation, which converge to the approximate solution and are suitable for very large-dimensional problems. The algorithms are extensions of recent approximate dynamic programming methods, known as temporal difference methods, which solve a projected form of Bellman's equation by using simulation-based approximations to this equation, or by using a projected value iteration method.
Original language | English (US) |
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Pages (from-to) | 27-50 |
Number of pages | 24 |
Journal | Journal of Computational and Applied Mathematics |
Volume | 227 |
Issue number | 1 |
DOIs | |
State | Published - May 1 2009 |
Externally published | Yes |
Keywords
- Dynamic programming
- Jacobi method
- Linear equations
- Projected equations
- Simulation
- Temporal differences
- Value iteration
ASJC Scopus subject areas
- Computational Mathematics
- Applied Mathematics