# time complexity

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## time complexity

(complexity)
The way in which the number of steps required by an algorithm varies with the size of the problem it is solving. Time 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 the algorithm will take four times as many steps to complete.

References in periodicals archive ?
Data expansion and computation time complexity comparison with Paillier cryptosystem are analyzed in Section 5.
Section 3 analyses the time complexity, compares the method with an older algorithm based on static strips, and introduces some additional improvements.
As a solution, they use an algorithm with similar time complexity as the one we propose in the next section, 0(logaN).
Wardoyo, "Time complexity analysis of support vector machines (SVM) in LibSVM," International Journal Computer Application, vol.
Here we compare in terms of the ciphertext size and the time complexity of basic operation implemented during homomorphic evaluation.
(2) It uses bucket sort to speed up the BWT encoding and decoding with time complexity O(N), so that the BWT block size can rise to 2 GB or more to fit the big data compression.
The proposed system has different advantages like complexity is less for a large amount of data, less time complexity and has high accuracy rate of classification.
Time complexity [21] of convolutional layers can be defined as:
Finding an efficient flight path at the cost of time complexity of this algorithm is always the most concerned issue of researchers.
Since, in classical K-means algorithm, every iteration requires calculation of the distance between each protein and each cluster center, the time complexity of classical K-means algorithm is 0(t * K * N); here t is the number of iteration until cluster centers convergence.
However, the time complexity will be increased exponentially with the increase of the thresholds, which limits its application in real time and cannot be tolerated.
The time complexity of VorNSA is ([N.sub.S] log [N.sub.S] + [N.sub.S.sup.[d/2]] + [absolute value of D]), where [N.sub.S] is the size of training dataset, d is the dimension of training dataset, and [absolute value of D] is the size of detectors.

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