space complexity

space complexity

(complexity)
The way in which the amount of storage space required by an algorithm varies with the size of the problem it is solving. Space complexity is normally expressed as an order of magnitude, e.g. O(N^2) means that if the size of the problem (N) doubles then four times as much working storage will be needed.

See also computational complexity, time complexity.
References in periodicals archive ?
This method is very accurate, but it is useful when the query is short in kilobytes, because it requires the quadratic time and space complexity, 0([n.sup.2]) where n is the length of target and query sequences.
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.
The computational complexity includes space complexity and time complexity.
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 space complexity of SPICKER is 0([N.sup.2] + N *S + K* S + S + N).
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.
In addition, in order to improve the accuracy of community detection, some label propagation methods adopt the process of modularity optimization to get more robust results, but the running time and space complexity significantly increases [14,15].
However, when the data is mass and sparse, time and space complexity will surge.

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