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Programming dynamic, strategy

The bottom-up approach contains two distinct stages. First, by successive backpropagation steps one builds a decision policy. Then, this uncovered policy is evaluated and refined, and its expected benefits confirmed before any implementation actually takes place. This two-stage process is conceptually similar to dynamic programming solution strategies, where first a decision policy is constructed by backward induction, and then one finds a realization of the process for the given policy, in order to check its expected performance (Bradley et al., 1977). [Pg.145]

A policy, sometimes ctilled a strategy, prescribes the way a decision is to be made at each point in time, given the information available to the decision maker at the point in time. Therefore, a policy is a solution for a dynamic program. [Pg.2639]

In this section we present a d5mamic program to find the optimal dynamic transshipment strategy. [Pg.26]

From this it follows that the second-best action strategy can be derived using a dynamic programming recursion and it is history-independent ... [Pg.127]

Theorem 5 (a) There exists an optimal long-term contract in which the action strategy a is memoryless. This strategy is given by the actions that maximize the right-hand side of the dynamic programming recursion ... [Pg.127]

The analysis of the remainder of the problem follows predictable lines and the second-best action strategy, which now represents an equilibrium profile, can be obtained using dynamic programming as before. If we let denote the optimal objective value for the optimization problem (4.27)-(4.30), then the second-best equilibrium action strategy can now be obtained by solving the following dynamic programming recursion ... [Pg.129]

Figure 8. Results showing the parallel performance of a domain decomposition molecular dynamics program using the first force evaluation strategy on a Cray T3D. The results are for a system of 16384 Gay-Berne particles using standard PVM calls on a Cray T3D. Improved performance over these results is possible by using cache-cache data transfers for the global sums at the end of the force evaluation. Figure 8. Results showing the parallel performance of a domain decomposition molecular dynamics program using the first force evaluation strategy on a Cray T3D. The results are for a system of 16384 Gay-Berne particles using standard PVM calls on a Cray T3D. Improved performance over these results is possible by using cache-cache data transfers for the global sums at the end of the force evaluation.

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