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It is standard to impose the following simplifying assumptions and notation of the dynamic decision problem: 1. Flow utility is additively separable and linear in parameters. The flow utility can be written as an additive sum, consisting of deterministic and stochastic elements.
A decision without a Boolean operator is a condition. A decision does not imply a change of control flow, e.g. an assignment of a boolean expression to a variable is a decision for MC/DC. Condition coverage Every condition in a decision in the program has taken all possible outcomes at least once. Decision coverage
The dynamic programming method breaks this decision problem into smaller subproblems. Bellman's principle of optimality describes how to do this: Principle of Optimality: An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state ...
The word dynamic was chosen by Bellman to capture the time-varying aspect of the problems, and because it sounded impressive. [12] The word programming referred to the use of the method to find an optimal program, in the sense of a military schedule for training or logistics.
Originally introduced by Richard E. Bellman in (Bellman 1957), stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming and dynamic programming , stochastic dynamic programming represents the problem under scrutiny in the form of a Bellman ...
McCabe showed that the cyclomatic complexity of a structured program with only one entry point and one exit point is equal to the number of decision points ("if" statements or conditional loops) contained in that program plus one. This is true only for decision points counted at the lowest, machine-level instructions. [4]
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The SAT problem is important both from theoretical and practical points of view. In complexity theory it was the first problem proved to be NP-complete , and can appear in a broad variety of applications such as model checking , automated planning and scheduling , and diagnosis in artificial intelligence .