Sentence examples for best case time and from inspiring English sources
(3) and (4) show that the best case time and space bounds for any asymptotically incompressible and thorough model are both in Ω (N⌈ log2(N ⌉).
T-ENS holds a worst case time complexity of (O MN^2)) (the same with that of ENS) and a best case time complexity of (O MNmathtt{ln}N/mathtt{ln}M)), which is better than (O MNmathtt{ln}N)) of ENS-BS and (O MNsqrt{N})) of ENS-SS.
The M-front has a best case time complexity of O MN) or (O MNmathtt{ln}N)) if the k-d tree is used, and a worst case time complexity of (O MN^2)).
This algorithm has a best-case time complexity of O(mn), and a worst-case time complexity of O(mnM 2 N ), wherem is the number of SSoD policies, n is the number of SA policies, M is the number of users, and N is the number of permissions.
We will now consider the best cases for time and space performance for asymptotically incompressible and asymptotically thorough models.
These definitions are then used to analyze the best cases for time and space.
A timing constrain is defined (13). in which is a consecutive path of tasks and channels and and are the required worst-case response time and best case response time for the to be completed after the first element of has been triggered.
The worst and best case execution time (WCET and BCET) often play an integral role in this relation especially the former, which has applications in traditional methods for verification of schedulability such as response time analysis [1].
The complete timing model is a network of timed automata which directly facilitates safe estimates of worst and best case execution time to be determined using the Uppaal model checker.
One of the novel optimizations (dynamic partitioning, generalized from AllDifferent) was found to speed up search by 5.6 times in the best case and 1.56 times on average, while exploring the same search tree.
As can be seen from the above analysis, the time complexity of frequent probability subgraph recognition algorithm based on two-step hierarchical clustering is O (n log n) in the best case, and the time complexity in the worst case is O(n ) since the algorithm is reduced to a classical hierarchical clustering.
