Abstract
In order to simplify computational methods based on dynamic programming, a relaxed procedure based on upper and lower bounds of the optimal cost was recently introduced. The convergence properties of this procedure are analysed here. In particular, it is shown that the computational effort in finding an approximately optimal control law by relaxed value iteration is related to the polynomial degree that is needed to approximate the optimal cost. This gives a rigorous foundation for the claim that the search for optimal control laws requires complex computations only if the optimal cost function is complex. A computational example is given for switching control on a graph with 60 nodes, 120 edges and 30 continuous states.
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