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Dynamic Programming. Foundations and Principles. Second Edition. Moshe Sniedovich. University of Melbourne. Melbourne, Australia. CRC Press. Taylor &.
Table of contents
- Ebook Dynamic Programming Foundations And Principles
- Dynamic programming - Encyclopedia of Mathematics
- Dynamic programming : foundations and principles
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Ebook Dynamic Programming Foundations And Principles
Dynamic programming - Encyclopedia of Mathematics
Simultaneously with the construction of the functions one also finds the conditional optimal controls at each step i. The final optimal controls are found by successively computing the values. The following property of the method of dynamic programming is evident from the above: It solves not merely one specific problem for a given , but all problems of this type, for all initial states.
Since the numerical realization of the method of dynamic programming is laborious, i. Even though dynamic programming problems are formulated for discrete processes, the method may often be successfully employed in solving problems with continuous parameters. Dynamic programming furnished a novel approach to many problems of variational calculus. An important branch of dynamic programming is constituted by stochastic problems, in which the state of the system and the objective function are affected by random factors.
Such problems include, for example, optimal inventory control with allowance of random inventory replenishment.
Controlled Markov processes are the most natural domains of application of dynamic programming in such cases. The method of dynamic programming was first proposed by Bellman. Rigorous foundations of the method were laid by L. Pontryagin and his school, who studied the mathematical theory of control process cf. Optimal control, mathematical theory of. Even though the method of dynamic programming considerably simplifies the initial problems, its explicit utilization is usually very laborious.
Attempts are made to overcome this difficulty by developing approximation methods. If the process to be controlled is described by a differential equation instead of a difference equation as considered above, a "continuous" version of the Bellman equation exists, but it is usually referred to as the Hamilton—Jacobi—Bellman equation the HJB-equation.
Dynamic programming : foundations and principles
The HJB-equation is a partial differential equation. Application of the so-called "method of characteristics" solution method to this partial differential equation leads to the same differential equations as involved in the Pontryagin maximum principle.
Derivation of this principle via the HJB-equation is only valid under severe restrictions. Another derivation exists, using variational principles, which is valid under weaker conditions. For a comparison of dynamic programming approaches versus the Pontryagin principle see [a1] , [a2]. In the terminology of automatic control theory one could say that the dynamic programming approach leads to closed-loop solutions of the optimal control and Pontryagin's principle leads to open-loop solutions.
For some discussion of stochastic dynamic programming cf.
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Jump to: navigation , search. This transition process is realized by a given function , and the new state is determined by the values , : Thus, the control operations convert the system from its initial state into the final state , where and are the sets of feasible initial and final states of. In the example above, this means that Another requirement of the method is the absence of "after-effects" in the problem, i. In other words, processes of the type are not considered. The final optimal controls are found by successively computing the values The following property of the method of dynamic programming is evident from the above: It solves not merely one specific problem for a given , but all problems of this type, for all initial states.
References  R.