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🔥 Visit our website for online classes and camps ➡️ https://algoritmiacademy.com In the previous video, Arpa solved the RMQ problem using Sqrt Decomposition with O(sqrt(n)) query time. In this video, Arpa takes it to the next level by introducing Sparse Table, a powerful data structure that allows us to answer range minimum queries in O(1) constant time! We start by building intuition on how to optimize block sizes, moving from fixed blocks to overlapping blocks of power-of-two sizes. Then, we dive into the core logic of Sparse Table: how to preprocess the array in O(N log N) using dynamic programming and how to query any range [L, R] instantly by combining two overlapping power-of-two blocks. What we cover: Review of the Sqrt Decomposition method. The intuition behind using overlapping blocks. Why Powers of Two? (1, 2, 4, 8...) Preprocessing logic: Building the table layer by layer. Query logic: How to cover any range [L, R] with just two lookups. Live Implementation: Solving the RMQSQ problem on SPOJ using Sparse Table. Optimizations: CPU cache-friendly array dimensions and using `__builtin_clz` for fast log calculation. Useful Links =========================== Problem link: https://www.spoj.com/problems/RMQSQ/ =========================== Connect with us! =========================== Website ➡️︎ www.algoritmiacademy.com 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 - RMQ problem description 0:49 - Attacking the problem 08:29 - Solution 12:26 - Implementation 18:34 - Some useful tips =========================== ♫ Music & Sound Effects ♪ =========================== Music: bensound.com Artist: Benjamin Tissot License code: L8K8DMUW2CS3L3T2 ===========================