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High-Level Modeling and Design Techniques
Published in Soumya Pandit, Chittaranjan Mandal, Amit Patra, Nano-Scale CMOS Analog Circuits, 2018
Soumya Pandit, Chittaranjan Mandal, Amit Patra
Computational complexity is divided into (i) time complexity and (ii) space complexity. These estimate the time and memory requirements of an algorithm respectively. The time complexity of an algorithm is loosely considered to be the amount of computer time it needs to run to completion. The space complexity of an algorithm is the amount of memory it needs to run to completion. The former is considered more important compared to the later, because the memory requirements of the majority of algorithms is lower than the capacity of the current machines. Therefore, when the term complexity is used alone, it unambiguously mean time complexity.
Computational Complexity Analysis for Problems in Elastic Optical Networks
Published in Bijoy Chand Chatterjee, Eiji Oki, Elastic Optical Networks: Fundamentals, Design, Control, and Management, 2020
Bijoy Chand Chatterjee, Eiji Oki
The space complexity of an algorithm indicates the amount of space (or memory) taken by the algorithm to run as a function of its input size. Space complexity includes both auxiliary space and space used by the input. Auxiliary space is the temporary or extra space used by the algorithm while it is being executed.
Introduction
Published in Chandrasekar Vuppalapati, Democratization of Artificial Intelligence for the Future of Humanity, 2021
In classical algorithm point of view, Space Complexity is a function describing the amount of memory (space) an algorithm takes regarding the number of inputs given to the algorithm. In AI point of view, the Space Complexity varies from Active vs. Lazy Learners; or varies from streaming vs. batch process.
Research on reducing fuzzy test sample set based on heuristic genetic algorithm
Published in Systems Science & Control Engineering, 2021
Zhihua Wang, Manman Cheng, Yongjian Wang
The evaluation index of the algorithm is a measure of the pros and cons of the algorithm, generally considered from the time complexity and space complexity. Time complexity refers to the time complexity of the algorithm. Suppose the initial sample set of the fuzzy test is n, and the fuzzy test time function is f(n), so the time complexity of the algorithm is also written as . As the number of fuzz tests increases, the growth rate of fuzz test execution time is positively related to the growth rate of f(n).The space complexity of the algorithm refers to the memory space consumed by the algorithm. The calculation and representation methods are similar to those of time complexity, which are generally expressed by asymptotic complexity.