  The least squares Monte Carlo algorithm has become popular for solving
portfolio optimization problems. A simple approach is to approximate the value
functions on a discrete grid of portfolio weights, then use control regression
to generalize the discrete estimates. However, the classical global control
regression can be expensive and inaccurate. To overcome this difficulty, we
introduce a local control regression technique, combined with adaptive grids.
We show that choosing a coarse grid for local regression can produce
sufficiently accurate results.
