DSA EP2: Complexity Analysis
In this newsletter, I’ll provide 3 topics that cover the knowledge of Complexity Analysis.
Time, Space Complexity
Big O notation
Time, Space Complexity
Complexity Analysis is a process that evaluates the performance of an algorithm, particularly in Time and Space. This process helps Engineers to estimate the resources of an algorithm to be executed: time processing, and resources consumed regarding input size.
Time Complexity
It represents the taken Time to execute an algorithm/function. It measures the number of basic operations or steps that are performed during execution time. Time Complexity will be calculated in terms of Big O Notation.
Space Complexity
It represents the taken Memory consumed to execute an algorithm/function. Like Time Complexity, Space Complexity uses Big O Notation to estimate the memory cost for an algorithm or function execution.
Big O notation
Big O notation is a mathematical notation used to describe the performance of an algorithm. This helps the Engineers to compare the effectiveness of solutions.
In Big O notation, the performance of an algorithm is expressed as a function of input size (usually represented as n). The goal of Big O notation is to capture the worst-case scenario, this helps to determine the scalability and behavior when dealing with large inputs.
Happy coding!