On the convergence of the Sakawa-Shindo algorithm in stochastic control
Résumé
We analyze an algorithm for solving stochastic control problems,
based on Pontryagin's maximum principle, due to Sakawa and
Shindo in the deterministic case and extended to the stochastic
setting by Mazliak. We assume that either the volatility is an
affine function of the state, or the dynamics are linear.
We obtain a monotone decrease of the cost functions as well as,
in the convex case, the fact that the sequence of controls is
minimizing, and converges to an optimal solution if it is bounded.
In a specific case we interpret the algorithm as the gradient plus
projection method and obtain a linear convergence rate to the solution.
Domaines
Optimisation et contrôle [math.OC]| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...
