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The resolution step leads to a worst-case exponential blow-up in the size of the formula. The Davis–Putnam–Logemann–Loveland algorithm is a 1962 refinement of the propositional satisfiability step of the Davis–Putnam procedure which requires only a linear amount of memory in the worst case.
Worst-case analysis is the analysis of a device (or system) that assures that the device meets its performance specifications. These are typically accounting for tolerances that are due to initial component tolerance, temperature tolerance, age tolerance and environmental exposures (such as radiation for a space device).
Worst-case space complexity O ( n ) {\displaystyle O(n)} (basic algorithm) In logic and computer science , the Davis–Putnam–Logemann–Loveland ( DPLL ) algorithm is a complete , backtracking -based search algorithm for deciding the satisfiability of propositional logic formulae in conjunctive normal form , i.e. for solving the CNF-SAT problem.
The terms are used in other contexts; for example the worst- and best-case outcome of an epidemic, worst-case temperature to which an electronic circuit element is exposed, etc. Where components of specified tolerance are used, devices must be designed to work properly with the worst-case combination of tolerances and external conditions.
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graph with an example of steps in a failure mode and effects analysis. Failure mode and effects analysis (FMEA; often written with "failure modes" in plural) is the process of reviewing as many components, assemblies, and subsystems as possible to identify potential failure modes in a system and their causes and effects. For each component, the ...
For example, a triangular distribution might be used, depending on the application. In three-point estimation, three figures are produced initially for every distribution that is required, based on prior experience or best-guesses: a = the best-case estimate; m = the most likely estimate; b = the worst-case estimate
They show that next-fit-increasing bin packing attains an absolute worst-case approximation ratio of at most 7/4, and an asymptotic worst-case ratio of 1.691 for any concave and monotone cost function. Cohen, Keller, Mirrokni and Zadimoghaddam [49] study a setting where the size of the items is not known in advance, but it is a random variable.