logarithmic complexity

Logarithmic complexity describes an algorithm’s efficiency where the number of steps increases very slowly relative to the size of the input. Specifically, as data size doubles, the steps grow by a fixed amount rather than proportionally. For example, a binary search looks for an item in a sorted list by repeatedly dividing the list in half, quickly narrowing down possibilities. This means even very large datasets can be processed efficiently. In essence, logarithmic complexity ensures that solutions scale well, with performance only gradually increasing as data size grows.