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Two fundamental classes of problems in large-scale linear and quad- ratic programming are described. Multistage problems covering a wide variety of models in dynamic programming and stochastic programming are represented in a new way. Strong properties of duality are revealed which support the devel- opment of iterative approximate techniques of solution in terms of saddlepoints. Optimality conditions are derived in a form that emphasizes the possibilities of decomposition.

Generalized Linear-Quadratic Problems of Deterministic and Stochastic Optimal Control in Discrete Time