Optimization, Control, and Applications of Stochastic by Ari Arapostathis (auth.), Daniel Hernández-Hernández, J. PDF

By Ari Arapostathis (auth.), Daniel Hernández-Hernández, J. Adolfo Minjárez-Sosa (eds.)

ISBN-10: 0817683364

ISBN-13: 9780817683368

ISBN-10: 0817683372

ISBN-13: 9780817683375

This quantity offers a normal review of discrete- and continuous-time Markov regulate approaches and stochastic video games, in addition to a glance on the diversity of functions of stochastic keep an eye on and a few of its contemporary theoretical advancements. those issues comprise a number of elements of dynamic programming, approximation algorithms, and infinite-dimensional linear programming. In all, the paintings includes 18 conscientiously chosen papers written by way of specialists of their respective fields.

Optimization, regulate, and functions of Stochastic Systems could be a beneficial source for all practitioners, researchers, and execs in utilized arithmetic and operations learn who paintings within the components of stochastic regulate, mathematical finance, queueing conception, and stock structures. it could possibly additionally function a supplemental textual content for graduate classes in optimum regulate and dynamic games.

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Additional resources for Optimization, Control, and Applications of Stochastic Systems: In Honor of Onésimo Hernández-Lerma

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Operations Research. 31, 957–965 (1983). 8. : Optimal Inventory Policy with Two Suppliers. Operations Research. 54, 389–393 (2006). 9. : Analysis of Inventory Systems. Prentice-Hall, New-Jersey (1963). 10. : Optimality of (s, S) policies in the infinite horizon dynamic inventory problem. Management Science. 9,259–267 (1963). 11. : (Q, r) inventory model with a mixture of lost sales and time-weighted backorders. Journal of the Operational Research Society. 36, 231–238 (1985). 12. : A continuous review for an inventory system with two supply modes.

In this case, there exists a unique pair (g, h(·)) ∈ IR × C (S) such that the following assertions (i)–(v) hold: (i) g = J ∗ (x) for each x ∈ S. (ii) h(z) = 0 and |h(x)| ≤ (1 + (z)) · (x) for all x ∈ S. 9) holds. 7). (iv) For each x ∈ S, the mapping a → ∑y∈S px y (a)h(y) is continuous in a ∈ A(x). 7) has a minimizer f (x) ∈ A(x), and the corresponding policy f ∈ F is optimal. 11) holds. 1 in [6] for a proof of the inequality in part (ii). 1. 2 is uniquely determined, since it is the optimal (expected) average cost at every state.

Discrete-time controlled Markov processes with average cost criterion: a survey. SIAM J. Control Optim. 31(2), 282–344 (1993) 3. : Ergodic control of diffusion processes, Encyclopedia of Mathematics and its Applications, vol. 143. Cambridge University Press, Cambridge (2011) 4. : Ergodic control problem for one-dimensional diffusions with nearmonotone cost. Systems Control Lett. 5(2), 127–133 (1984) 5. : On regularity of transition probabilities and invariant measures of singular diffusions under minimal conditions.

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Optimization, Control, and Applications of Stochastic Systems: In Honor of Onésimo Hernández-Lerma by Ari Arapostathis (auth.), Daniel Hernández-Hernández, J. Adolfo Minjárez-Sosa (eds.)


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