Time Complexity in Programming: Understanding Efficiency

Day 18 of My Programming Journey – Time Complexity Today I learned about Time Complexity, which helps measure how efficient an algorithm is as the input size grows. Understanding time complexity helps developers write optimized and scalable code. 📌 What is Time Complexity? Time complexity describes how the running time of an algorithm increases with the size of the input (n). 🔹 Common Time Complexities • O(1) – Constant Time • O(log n) – Logarithmic Time • O(n) – Linear Time • O(n log n) – Linearithmic Time • O(n²) – Quadratic Time 🔹 Types of Time Complexity Analysis ✔ Best Case The minimum time required for an algorithm to run. Example: Finding an element at the first position in linear search. ✔ Average Case The expected time an algorithm takes for typical input. ✔ Worst Case The maximum time an algorithm takes when the input is in the most difficult case. Example: Searching an element at the last position in linear search. 💻 Problems I Practiced • Linear Search • Finding largest element in an array • Nested loop programs (O(n²)) • Comparing different algorithm efficiencies #Programming #Java #TimeComplexity #DSA #CodingJourney #LearningDaily

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