1997
DOI: 10.1002/(sici)1099-1514(199711/12)18:6<423::aid-oca612>3.0.co;2-5
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Approximation of integro-differential equations associated with piecewise deterministic process

Abstract: Aim of this paper is to present an approximation scheme for optimal control problems of piecewise deterministic processes and corresponding integro‐differential Hamilton–Jacobi–Bellman equations. The method is based on a discrete dynamic programming approach. We discretize the continuous process and the cost functional obtaining a discrete time optimal control problem. The corresponding dynamic programming equation gives an approximation of the integro‐differential equation. The main feature of the method is t… Show more

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Cited by 8 publications

(4 citation statements)
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“…In the first order case and when the Levy measure is bounded, convergence rate 1/2 for semi-Lagrangian schemes like (3.8) have previously be obtained in [16]. However the integral term in [16] has a different form compared to the one we consider here.…”
Section: Convergence Estimates For the Discrete-time Problemmentioning
confidence: 81%
“…In [11] it is proved that u is the unique viscosity solution of equation (1.1). Following the approach of [16,17] we construct an approximation scheme for the equation (1.1) by discretizing the associated control problem. We fix a discretization step h > 0 and consider two stochastic processes N n and Z n , n ∈ N, taking values in N and in R N and representing the n-th jump time and the corresponding z-jump (size and direction) of the Poisson measure μ.…”
Section: Construction Of the Schemementioning
confidence: 99%
“…Semi-Lagrangian schemes for second order HJB equations with no integral terms have been considered in [37,17,5,1]. Moreover in [16] such schemes were derived for HJB equations associated to piecewise deterministic processes (compound Poisson processes with drift). One advantage of this type of schemes is that in general they produce also an approximation of the optimal control law in feedback form and the optimal trajectories (this is a key point in the study of a control problem).…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…In the first order case and when the Levy measure is bounded, convergence rate 1/2 for semi-Lagrangian schemes like (3.8) have previously be obtained in [16]. However the integral term in [16] has a different form compared to the one we consider here.…”
Section: Convergence Estimates For the Discrete-time Problemmentioning
confidence: 81%
“…In [11] it is proved that u is the unique viscosity solution of equation (1.1). Following the approach of [16,17] we construct an approximation scheme for the equation (1.1) by discretizing the associated control problem. We fix a discretization step h > 0 and consider two stochastic processes N n and Z n , n ∈ N, taking values in N and in R N and representing the n-th jump time and the corresponding z-jump (size and direction) of the Poisson measure μ.…”
Section: Construction Of the Schemementioning
confidence: 99%
“…Semi-Lagrangian schemes for second order HJB equations with no integral terms have been considered in [37,17,5,1]. Moreover in [16] such schemes were derived for HJB equations associated to piecewise deterministic processes (compound Poisson processes with drift). One advantage of this type of schemes is that in general they produce also an approximation of the optimal control law in feedback form and the optimal trajectories (this is a key point in the study of a control problem).…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
“…where we set the discount factor β = e −ωh . Camilli (1997) shows that V h (x, i) satisfies the following dynamic programming equation…”
Section: P Hmentioning
confidence: 99%
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.
Exaggerated anticipatory anxiety is common in social anxiety disorder (SAD). Neuroimaging studies have revealed altered neural activity in response to social stimuli in SAD, but fewer studies have examined neural activity during anticipation of feared social stimuli in SAD. The current study examined the time course and magnitude of activity in threat processing brain regions during speech anticipation in socially anxious individuals and healthy controls (HC). Method Participants (SAD n = 58; HC n = 16) underwent functional magnetic resonance imaging (fMRI) during which they completed a 90s control anticipation task and 90s speech anticipation task.