  Adaptive Partition-based Methods (APM) are numerical methods to solve
two-stage stochastic linear problems (2SLP). The core idea is to iteratively
construct an adapted partition of the space of alea in order to aggregate
scenarios while conserving the true value of the cost-to-go for the current
first-stage control. Relying on the normal fan of the dual admissible set, we
extend the classical and generalized APM method by i) extending the method to
almost arbitrary 2SLP, ii) giving a necessary and sufficient condition for a
partition to be adapted even for non-finite distribution, and iii) proving the
convergence of the method. We give some additional insights by linking APM to
the L-shaped algorithm.
