Loading...
Please wait, while we are loading the content...
Similar Documents
Efficient descriptor-vector multiplications in stochastic automata networks (0).
| Content Provider | CiteSeerX |
|---|---|
| Author | Fernandes, Paulo Stewart, William J. |
| Abstract | This paper examines numerical issues in computing solutions to networks of stochastic automata. It is well-known that when the matrices that represent the automata contain only constant values, the cost of performing the operation basic to all iterative solution methods, that of matrix-vector multiply, is given by ae N = N Y i=1 n i \Theta N X i=1 n i ; where n i is the number of states in the i th automaton and N is the number of automata in the network. We introduce the concept of a generalized tensor product and prove a number of lemmas concerning this product. The result of these lemmas allows us to show that this relatively small number of operations is sufficient in many practical cases of interest in which the automata contain functional and not simply constant transitions. Furthermore, we show how the automata should be ordered to achieve this. Key words: Markov chains, Stochastic automata networks, Generalized tensor algebra, Vectordescriptor multiplication. IMAG--... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Stochastic Automaton Network Efficient Descriptor-vector Multiplication Automaton Contain Many Practical Case Constant Value Generalized Tensor Algebra Iterative Solution Method Constant Transition Key Word Numerical Issue Matrix-vector Multiply Markov Chain Small Number Stochastic Automaton Generalized Tensor Product Vectordescriptor Multiplication |
| Content Type | Text |
| Resource Type | Article |
