Time complexity is a way to measure how fast an algorithm is. When we talk about the time complexity of an algorithm, we’re trying to figure out how long it takes for the algorithm to finish running. We do this by counting the number of steps or operations the algorithm takes.
The way we measure time complexity is by using something called Big O notation. Big O notation is a way to write down how the number of steps an algorithm takes changes as the size of the input gets bigger. We use the letter “O” followed by a number, such as O(n) or O(1).
For example, let’s say we have an algorithm that looks through a list of numbers and finds the biggest one. If the list has 5 numbers, the algorithm might take 5 steps to find the biggest number. If the list has 10 numbers, it might take 10 steps. In this case, the time complexity of the algorithm is O(n), where n is the number of items in the list. This means that as the number of items in the list gets bigger, the number of…
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