What is Time Complexity O(n)? A Must-Learn Efficiency Concept for Data Structure Beginners

The article explains the necessity of time complexity: due to differences in hardware and compilers, direct timing is impractical, and an abstract description of the algorithm's efficiency trend is required. The core is linear time complexity O(n), which indicates that the number of operations is proportional to the input size n (such as the length of an array). Scenarios like traversing an array to find a target or printing all elements require n operations. Big O notation ignores constants and lower-order terms, focusing only on the growth trend (e.g., O(2n+5) is still O(n)). Comparing common complexities (O(1) constant, O(n) linear, O(n²) quadratic, O(log n) logarithmic), O(n) is more efficient than O(n²) but less efficient than O(1) or O(log n). Understanding O(n) is fundamental to algorithms, helping optimize code and avoid timeout issues caused by "brute-force solutions."