Optimal stopping with continuous control of piecewise deterministic Markov processes

2000 ◽  
Vol 70 (1-2) ◽  
pp. 41-73 ◽  
Author(s):  
O.L.V. Costa ◽  
C.A.B. Raymundo ◽  
Dufour F.
2020 ◽  
Vol 57 (2) ◽  
pp. 497-512
Author(s):  
Bertrand Cloez ◽  
Benoîte de Saporta ◽  
Maud Joubaud

AbstractThis paper investigates the random horizon optimal stopping problem for measure-valued piecewise deterministic Markov processes (PDMPs). This is motivated by population dynamics applications, when one wants to monitor some characteristics of the individuals in a small population. The population and its individual characteristics can be represented by a point measure. We first define a PDMP on a space of locally finite measures. Then we define a sequence of random horizon optimal stopping problems for such processes. We prove that the value function of the problems can be obtained by iterating some dynamic programming operator. Finally we prove via a simple counter-example that controlling the whole population is not equivalent to controlling a random lineage.


2009 ◽  
Vol 46 (04) ◽  
pp. 1157-1183 ◽  
Author(s):  
O. L. V. Costa ◽  
F. Dufour

This work is concerned with the existence of an optimal control strategy for the long-run average continuous control problem of piecewise-deterministic Markov processes (PDMPs). In Costa and Dufour (2008), sufficient conditions were derived to ensure the existence of an optimal control by using the vanishing discount approach. These conditions were mainly expressed in terms of the relative difference of the α-discount value functions. The main goal of this paper is to derive tractable conditions directly related to the primitive data of the PDMP to ensure the existence of an optimal control. The present work can be seen as a continuation of the results derived in Costa and Dufour (2008). Our main assumptions are written in terms of some integro-differential inequalities related to the so-called expected growth condition, and geometric convergence of the post-jump location kernel associated to the PDMP. An example based on the capacity expansion problem is presented, illustrating the possible applications of the results developed in the paper.


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