╔(σ_σ)╝ said:It looks to me like n^2 because of the nested loops.
Time complexity in computer science refers to the analysis and measurement of the amount of time it takes for a computer algorithm to run as a function of the input size. It is used to evaluate the efficiency and performance of algorithms.
Time complexity is important because it helps us understand the performance and scalability of algorithms. It allows us to compare different algorithms and choose the most efficient one for a given problem or input size. It also helps in predicting the time it will take for an algorithm to run on larger input sizes, which is crucial for real-world applications.
Time complexity is measured using the Big O notation, which represents the upper bound on the time it takes for an algorithm to run as a function of the input size. It ignores constant factors and lower order terms, providing a general idea of the growth rate of the algorithm.
The worst case time complexity represents the maximum amount of time an algorithm will take to run for a given input size. It is denoted by the Big O notation and is used to evaluate the worst possible performance of an algorithm. It is important to consider the worst case scenario when analyzing the efficiency of an algorithm.
Some common time complexities include O(1) or constant time, O(log n) or logarithmic time, O(n) or linear time, O(n^2) or quadratic time, O(n log n) or linearithmic time, O(n^3) or cubic time, and O(2^n) or exponential time. These complexities represent different growth rates of algorithms and can be used to compare and analyze their efficiency.