  We introduce and analyze a family of linear least-squares Monte Carlo schemes
for backward SDEs, which interpolate between the one-step dynamic programming
scheme of Lemor, Warin, and Gobet (Bernoulli, 2006) and the multi-step dynamic
programming scheme of Gobet and Turkedjiev (Mathematics of Computation, 2016).
Our algorithm approximates conditional expectations over segments of the time
grid. We discuss the optimal choice of the segment length depending on the
`smoothness' of the problem and show that, in typical situations, the
complexity can be reduced compared to the state-of-the-art multi-step dynamic
programming scheme.
