Error estimates for stochastic differential games: the adverse stopping case

Abstract : We obtain error bounds for monotone approximation schemes of a particular Isaacs equation. This is an extension of the theory for estimating errors for the Hamilton-Jacobi-Bellman equation. For obtaining upper error bound, we consider the ``Krylov regularization'' of the Isaacs equation to build an approximate sub-solution of the scheme. To get lower error bound we extend the method of Barles and Jakobsen which consists in introducing a switching system whose solutions are local super-solutions of the Isaacs equation.
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Submitted on : Friday, May 19, 2006 - 8:53:47 PM
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J. Frederic Bonnans, Stefania Maroso, Hasnaa Zidani. Error estimates for stochastic differential games: the adverse stopping case. [Research Report] RR-5441, INRIA. 2004, pp.28. ⟨inria-00070566⟩

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