Abstract
We derive an algorithm in the spirit of Rogers and Davis & Burstein that leads to upper bounds for stochastic control problems. Our bounds complement lower biased estimates recently obtained in the work of Guyon & Henry-Labordère. We evaluate our estimates in numerical examples motivated from mathematical finance.
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