Range Minimum Query (RMQ) | Sqrt Decomposition + Implementation

🔥 Visit our website for online classes and camps ➡️ https://algoritmiacademy.com In this video, Arpa tackles the fundamental Range Minimum Query (RMQ) problem, but with a fresh approach using Sqrt Decomposition. If you are familiar with the naive O(N) solution or the O(N^2) caching method, this is the next step in optimizing your queries for competitive programming. We explain the logic behind dividing your array into blocks to drastically reduce query time, finding the optimal block size (square root), and how this structure efficiently handles updates compared to other methods. Finally, we implement the solution in C++ to solve the "RMQSQ" problem on SPOJ. What we cover: - The Naive Approaches: Why O(N) per query is too slow. - The Optimization Idea: Splitting the array into blocks (sqrt decomposition). - Visualizing the Query: How to jump over blocks and handle edge elements. - Live Implementation: Solving the RMQSQ problem on SPOJ from scratch. - A clean, short implementation trick for Sqrt Decomposition loops. Useful links =========================== RMQ problem link (SPOJ): https://www.spoj.com/problems/RMQSQ/ =========================== Connect with us! =========================== Facebook ➡️︎ www.facebook.com/people/Algoritmi-Academy/61554522885080 LinkedIn ➡️︎ www.linkedin.com/company/algoritmiacademy Telegram ➡️︎ t.me/Algoritmi_Acad X ➡️︎ x.com/algoritmi_acad =========================== ⏰ Timecodes ⏰ =========================== 0:00 - Problem description 0:50 - Attacking the problem 2:35 - Solution 5:08 - Implementation =========================== ♫ Music & Sound Effects ♪ =========================== Music I use: Bensound Artist: Benjamin Tissot License code: R2IUXHJX3Q4EIDLF =========================== #competitiveprogramming #competitive #decomposition #sqrt #sqd #rmq #cpp