In this paper, we present algorithms for the solution of the dynamic minimax problem in stochastic programs. This dynamic minimax approach is suggested for the analysis of multi-stage stochastic decision problems when there is only partial knowledge on the joint probability distribution of the random data. The algorithms proposed in this paper are based on projected sub-gradient and bundle methods.