Time and Space Complexity: How Algorithm Efficiency Is Measured

Time complexity describes how the number of operations an algorithm performs grows with the size of its input, rather than how long it takes on a stopwatch. Measuring it matters because computing resources are limited, and it helps developers write more efficient programs. Space complexity similarly tracks how memory use scales, such as allocating an array whose size depends on N. The article explains three bounds: Big O for the worst case, Omega for the best case, and Theta for the average case. It then walks through common growth classes, from constant O(1) and linear O(N) up to logarithmic, quadratic, and exponential forms, with practice questions to apply them.