Name
Abstract | ||
|---|---|---|
Summary Two kinds of problems regarding measurement optimization in stochastic decision processes, when measurements are costly or constrained not to exceed a given number, have been investigated in the last years: the first one refers to the optimum timing of observations on the state vector of the process, while the second refers to the convenience of buying information on the random actions exerted by a stochastic environment. In this paper the two problems are considered from a unified point of view. In other words, the decision maker has to determine the optimal observation policy, under the assumption that both state and random vectors are measurable. A solution based on the application of dynamic programming is discussed for a general class of multistage processes. Analytical results are then obtained for scalar linear systems with quadratic cost on state and control. In this case, an efficient algorithm to determine the optimal sequence of measurements is presented, on the base of which a sensitivity analysis with respect to the process parameters is also carried out. |
Year | DOI | Venue |
|---|---|---|
1973 | 10.1007/BF01956857 | Zeitschr. für OR |
Field | DocType | Volume |
Dynamic programming,State vector,Mathematical optimization,Optimal decision,Linear system,Scalar (physics),Markov decision process,Multivariate random variable,Stochastic programming,Mathematics | Journal | 17 |
Issue | Citations | PageRank |
3 | 1 | 0.63 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. P. Puliafito | 1 | 11 | 2.96 |
| R. Zoppoli | 2 | 279 | 51.51 |