COMPLEXITY ANALYSIS

 

• Complexity analysis is the process of determining how the time and space requirements of an algorithm change as the size of its input increases.
• Time complexity measures how long an algorithm takes to run, as a function of the size of its input.
• Space complexity measures how much memory an algorithm uses, as a function of the size of its input.
• There are several different ways to measure complexity, but the most common are Big-O notation, Theta notation, and Omega notation.
• Big-O notation is used to describe the asymptotic behavior of an algorithm. This means that it describes how the algorithm's time or space requirements change as the size of its input approaches infinity.
• Theta notation is used to describe the exact time or space requirements of an algorithm.
• Omega notation is used to describe the lower bound of an algorithm's time or space requirements.

examples of complexity analysis in Java:

• The time complexity of the for loop is O(n), where n is the number of iterations in the loop.
• The space complexity of the ArrayList class is O(n), where n is the number of elements in the list.
• The time complexity of the binary search algorithm is O(log n), where n is the number of elements in the array.

Complexity analysis is an important tool for software developers. It can help developers to choose the most efficient algorithms for their problems, and to ensure that their code is scalable.