What does linear complexity, denoted as O(n), indicate about an algorithm?

Linear complexity, denoted as O(n), indicates that the runtime of an algorithm increases in direct proportion to the input size. This means that if you were to double the size of the input, the runtime would also approximately double. This type of complexity signifies that the performance of the algorithm scales predictably and linearly in relation to the data being processed.

In practical terms, an algorithm with linear complexity effectively handles increases in input size without causing exponential increases in processing time, making it efficient for many applications. For instance, if an algorithm iterates through a list of items performing a simple operation on each item, its runtime will increase linearly with the size of that list.

This characteristic of O(n) complexities is particularly advantageous because it strikes a balance between efficiency and scalability, ensuring that as problems grow in size, the algorithm still performs adequately without significant slowdowns.