Dynamic Programming in a Generalized Decision Model
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چکیده
We present two dynamic programming strategies for a general class of decision processes. Each of these algorithms includes among others the following graph theoretic optimization algorithms as special cases: the Ford-Bellman Strategy for optimal paths in acyclic digraphs, the Greedy Method for optimal forests and spanning trees in undirected graphs. In our general decision model, we deene several structural properties of cost measures in order to formulate suucient conditions for the correctness of our algorithms. Our rst algorithm works as fast as the original Ford-Bellman Strategy and the Greedy Method, respectively. Our second algorithm solves a larger class of optimization problems than our rst search strategy.
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