The Markov Recombining Scenario Tree method approximates multistage stochastic programs from two historical trajectories and is claimed to achieve poly(T) sample complexity, avoiding the exponential-in-T sample requirement of SAA.
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Sample Complexity of Data-driven Multistage Stochastic Programming under Markovian Uncertainty
The Markov Recombining Scenario Tree method approximates multistage stochastic programs from two historical trajectories and is claimed to achieve poly(T) sample complexity, avoiding the exponential-in-T sample requirement of SAA.