It is possible to speed up algorithms for matrices with special structures, which arise commonly in applications.
But it could dramatically speed up algorithms designed to explore vast numbers of different paths simultaneously, and solve problems that include searching through large data sets, discovering new chemical catalysts, and factoring large numbers used to encrypt data.
These two heuristics sufficiently speed up algorithms running in O(nmE) time.
The Grover quantum search algorithm, one of only a few representative quantum algorithms, can speed up many classical algorithms that use search heuristics.
The paper presents speed up of the k-means algorithm for image segmentation.
In this mixed-norm algorithm and during the transient state, the NLMS algorithm is used to speed up the algorithm's convergence.
Moreover, we accomplish several improvements to speed up the algorithm and show the effectiveness of our algorithm by numerical experiments.
There are several approaches to speed up the algorithm, such as the strategies suggested in [16].
To speed up the algorithm, sets and pixels are visited in the Morton order as shown in Figure 2b and processed by the appropriate pass.
As previously mentioned, the segmentation was inspired on [12] in order to speed up the algorithm and to have better results.
