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Using asymptotic analysis, we can get an idea about the performance of the algorithm based on the input size. We should not calculate the exact running time, but we should find the relation between the running time and the input size. We should follow the running time when the size of input is increased.
For the space complexity, our goal is to get the relation or function that how much space in the main memory is occupied to complete the algorithm.
For a function f(n) the asymptotic behavior is the growth of f(n) as n gets large. Small input values are not considered. Our task is to find how much time it will take for large value of the input.
For example, f(n) = c * n + k as linear time complexity. f(n) = c * n2 + k is quadratic time complexity.
The analysis of algorithms can be divided into three different cases. The cases are as follows −
Best Case − Here the lower bound of running time is calculated. It describes the behavior of algorithm under optimal conditions.
Average Case − In this case we calculate the region between upper and lower bound of the running time of algorithms. In this case the number of executed operations are not minimum and not maximum.
Worst Case − In this case we calculate the upper bound of the running time of algorithms. In this case maximum number of operations are executed.
- Asymptotic Analysis
- Asymptotic Notations
- Amortized Complexity
- Asymptotic Notation - O(), o(), Ω(), ω(), and θ()
- Complexity of Interval Heap Operations
- What is 'Space Complexity’?
- Amortized time complexity in Data Structures
- An interesting time complexity question in C++
- Time and Space Complexity in Data Structure
- Complexity of an algorithm for size n=3
- An Insertion Sort time complexity question in C++
- Practice Questions on Time Complexity Analysis in C++
- What is the complexity of the Apriori Algorithm?
- How to calculate the time complexity of interpolation search?
- Mccabe's Cyclomatic Complexity: Calculate with Flow Graph