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Differential privacy (DP) is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects. It enables a data holder to share aggregate patterns of the group while limiting information that is leaked about specific individuals.
For example, the best case for a simple linear search on a list occurs when the desired element is the first element of the list. Development and choice of algorithms is rarely based on best-case performance: most academic and commercial enterprises are more interested in improving average-case complexity and worst-case performance .
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.
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.
Discounted maximum loss, also known as worst-case risk measure, is the present value of the worst-case scenario for a financial portfolio.. In investment, in order to protect the value of an investment, one must consider all possible alternatives to the initial investment.
Working independently, the students quickly learned the language and the system. They were free to explore the system, often using existing code available in public Workspaces as models for their own creations. Eventually, curiosity drove the students to explore the system's wider context.
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.