Section 5 analyses the performance of the proposed algorithm in key space, chosen-plaintext attack and the noise test, and compares the proposed algorithm with the existing algorithms in time complexity,
space complexity and correlation coefficient.
Space Complexity. The huge memory requirements of global path planning algorithms based on visibility graph are the visible edges.
worst-case time and O(dn)
space complexity, where f(n, d) denotes the number of iterations of the main stage of the algorithm, and parameters [s.sub.min] and [c.sub.min] regulate the duration of each iteration.
The time and
space complexity of proposed method is discussed theoretically.
How can this data be compressed, how to make its
space complexity smaller, because a big data set can be problematic.
The advantage is reduce the requirements of electronic components so that the
space complexity is reduced.
Next, one can then investigate the relation between these two frameworks and, in particular, whether complex algorithms (in terms of time and
space complexity) get translated to complex (in the sense of fractal dimension) space-time diagrams.
However, when the data is mass and sparse, time and
space complexity will surge.