An O(log N) Parallel Time Exact Hidden-LineAlgorithm

Similarly, many parameterized problems that on general graphs cannot be solved in time 2 o(k) · n O(1) unless the Exponential Time Hypothesis (ETH) of Impagliazzo, Paturi and Zane

In particular, we present algorithms to enumerate all minimal connected dominating sets of chordal graphs in time O(1.7159 n ), of split graphs in time O(1.3803 n ), and of

Using the best known exact algorithm for this problem, an O ∗ (2 |E|/4 ) algorithm by Fedin and Kulikov [FK02], we get a running time of O ∗ (2 k/2 ) which is equivalent to

It is clear that both an efficient search algorithm to identify all the domains (stored in the LDF) that overlap and an efficient algorithm to generate the domains

In this paper we show a highly oarallel method to do image reconstruction which performs at real-tIme, using an asynchronous cellular array.. The highly parallel

Therefore the proposed method takes, in total, 4n random numbers and O ( n logn ) time in the worst case to generate 4n coordinates for a set of n non-intersecting random

Given a set of n geometric objects in the plane, the polar diagram can be used as preprocessing to find the maximum orthogonal visibility angle problem in O(log n)

In addition to the overall size of the data set, what makes large time-varying data visualization hard is the need to constantly transfer each time step of the data from disk to