The term algorithm analysis was given by Donald Knuth and it is a very important part of computer theory that produces theoretical estimation for an algorithm. It is a type of process which is also used to solve a particular computational problem. Many of algorithm processes are developed to work with input of arbitrary length and algorithm analysis is a measurement method which is used to calculate time and space required to solve an algorithm (Saunders, Russell, & Crabb, 2015). It is observed that the running time of any source code is started as a function which is related to the input length and this process is called as time complexity. The algorithm is a different process and objective of an algorithm process is to understand the concept of any programming. In the field of computer science, it is a process to determine the computational complexity of an algorithm (Saunders, Russell, & Crabb, 2015). It is researched that an algorithm is called as more efficient when values of time complexity and space complexity are very small as compare to the size of input signals. In this type of process big O notation, big theta notation and big omega notation are used to measure time and space complexity of any source code (Saunders, Russell, & Crabb, 2015).
The main aim of algorithm analysis is to determine the overall performance of various algorithms in order to design decisions (Tsoukalas, & Fragiadakis, 2016). A complete algorithm analysis process involves a few steps which are following
- Implement an algorithm process
- Measure the time required to perform each operation
- Investigate unknown quantities
- Produce a realistic system for the input signals
- Determine time complexity and space complexity for particular source code (Tsoukalas, & Fragiadakis, 2016).
In the field of computer science, the efficiency of an algorithm is a property of any source code that is related to various computational resources. Algorithm efficiency is defined as analogous to improve the productivity of any computer devices and for maximum efficiency; people can reduce the use of additional resources (Ahmed, & Salam, 2015). It is estimated that different resources, for example, time and space cannot be compared directly, therefore, two algorithm process is used to measure the efficiency of any source code (Ahmed, & Salam, 2015). Algorithm efficiency was developed by Ada Lovelace in the year 1843 and it is divided into two parts such as time and space efficiency.
Time efficiency is used to determine the total amount of time taken by the algorithm to execute particular task and space efficiency is used to calculate to space required to execute an algorithm process (Yu, Li, Jia, Zhang, & Wang, 2015). For bubble sort processes the overall efficiency is given by O(N2) and in which N/2 comparison is needed to perform one task at a time. It is researched that when the order of two or more algorithm processes are the same than their efficiency are also equal in terms of computation. The main advantage of this process is to implement the difficulties and problems of any source code (Yu, Li, Jia, Zhang, & Wang, 2015). Small-o complexity is a process which is used in algorithm efficiency and it calculates the total time needed to execute the algorithm. Therefore users can understand time and space complexity by algorithm efficiency and they can easily calculate both quantities for any source code (Yu, Li, Jia, Zhang, & Wang, 2015).
Ahmed, J., & Salam, Z. (2015). An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency. Applied Energy, 150, 97-108.
Saunders, L. J., Russell, R. A., & Crabb, D. P. (2015). Measurement precision in a series of visual fields acquired by the standard and fast versions of the Swedish interactive thresholding algorithm: analysis of large-scale data from clinics. JAMA Ophthalmology, 133(1), 74-80.
Tsoukalas, V. D., & Fragiadakis, N. G. (2016). Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis. Safety Science, 83, 12-22.
Yu, W., Li, B., Jia, H., Zhang, M., & Wang, D. (2015). Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88, 135-143.