We consider a homogeneous self-dual interior point algorithm for solving multistage stochastic linear programs. The algorithm is particularly suitable for the so-called "scenario formulation" of the problem, whose constraint system consists of a large block-diagonal matrix together with a set of sparse nonanticipativity constraints. Due to this structure, the major computational work required by the homogeneous self-dual interior point method can be split into three steps, each of which is

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... y decomposable. Numerical results are presented to demonstrate efficiency of the algorithm.