Скачать или смотреть Divide & Conquer + Sparse Table = Disjoint Sparse Table

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In this video, Arpa introduces the Disjoint Sparse Table (DST), a powerful optimization of the traditional Sparse Table that allows for O(1) query complexity even when the "merge" operation is expensive (like matrix multiplication).

We start by revisiting the Range Minimum Query (RMQ) problem and solving it using a simple Divide & Conquer approach. Arpa explains how precomputing prefix and suffix arrays allows us to answer queries that cross the midpoint in O(1). This concept is then extended to build the full Disjoint Sparse Table structure.

Next, we tackle a challenging USACO Platinum problem: "Counting Non-Decreasing Subsequences" in a given range. Arpa demonstrates why a standard Sparse Table with O(K^3) merge would fail (TLE) and how Disjoint Sparse Table reduces the complexity to O(K) per query by avoiding the expensive merge step during the query phase.

What we cover:
Review of RMQ and Divide & Conquer on arrays.
How Disjoint Sparse Table works: Precomputing prefix/suffix data for every power-of-two level.
Comparison: Standard Sparse Table vs. Disjoint Sparse Table vs. Segment Tree.
Solving USACO Platinum "Non-Decreasing Subsequences":
Defining the DP state (Matrix size K x K).
Why standard merging is O(K^3) and how DST makes it O(K).
Live implementation and debugging of the Divide & Conquer solution.
Optimizing the solution with prefix sums and faster modulo operations to pass tight time limits.

Useful links
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USACO problem link: https://usaco.org/index.php?page=view...
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⏰ Timecodes ⏰
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0:00 Introduction
0:16 Problem Description
1:14 Divide & Conquer
9:00 USACO Non-Decreasing Subsequences Implementation
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