Introduction

Hi everyone, because we noticed there was no good tutorial in English explaining this idea, KrowSavcik and I have decided to write a tutorial blog on it!

This idea finds the values of each element from a binary array using $$$O(N / log(N))$$$ sum queries, more specifically it solves problems of the type:

Given a binary array $$$b$$$ (an array consisting only of zeroes and ones) of $$$n$$$ elements, you can ask queries of the following format:

What is the sum of the subsequence of the values on positions: $$$p_1, p_2, ..., p_k$$$. In other words you will give the subsequence $$$p_1, p_2, ..., p_k$$$ to the interaction and it will return you the value of ($$$b_{p_1} + b_{p_2} + ... + b_{p_k}$$$).

And after asking $$$O(N / log(N))$$$ queries we should be able to tell the value of each index.

The technique was mentioned here too but it was not as detailed.

Concept

The idea is to use a divide and conquer-like approach. Because it's not an intuitive concept, firstly I will have to add some simple notations, so it will be easier to understand it.

• $$$x_i$$$ — refers to the position $$$i$$$
• $$$A$$$ — any capital letter (except S) refers to a set of points $$$x_i$$$
• $$$v_{A} = \sum b_{i}$$$ for $$$x_i \in A$$$
• $$$(A)$$$ — a set in brackets is a query
• $$$k$$$ — the layer we are currently considering
• $$$S_i$$$ — Set of queries

Also you should keep in mind that indices are enumerated from 0.

Also keep in mind that even though it's said the queries are used, we actually only query at the end, and we build up our set of queries first for multiple layers first.

As it is a divide and conquer-like approach we will work with layers. Let's say that for layer $$$k$$$ we need $$$2^k$$$ queries and that from it we can get the value of $$$f_k$$$ elements. Then $$$f_{k+1} = 2 \cdot f_k + 2^k - 1$$$.

First of all, let's set our base case, which is $$$k = 0$$$. So, for $$$k = 0, f_0 = 1$$$ and the query set will only be {$$$0$$$}, so we know a single element using a single query.

The block $$$f_{k+1}$$$ is formed from $$$2$$$ blocks of size $$$f_k$$$ and $$$2^k -1$$$ additional elements in this order. Let's say $$$k_1$$$ represents the first block of $$$f_k$$$ elements and the $$$k_2$$$ the second such block.

The first query is used to get the sum on $$$[f_k, 2 \cdot f_k)$$$ — the sum of the second block. Then we add two new queries for each non last query in $$$S_{k_1}$$$ and $$$S_{k_2}$$$. First is $$$S_{k_1}[i] \cup S_{k_2}[i]$$$. Second one is $$$S_{k_1}[i] \cup ([f_k, 2 \cdot f_k) \cap S_{k_2}[i]) \cup x_{(2 \cdot f_k + i)}$$$.

The last query is for entire range $$$[0, f_{k+1})$$$.

I think it's easy to see why we now have $$$2^{k+1}$$$ queries. The harder part is to understand why we don't lose any value in this process, and how we can solve it recursively. In fact, having answered all the $$$S_{k+1}$$$ queries, we can calculate all the $$$v_{S_{k_1}[i]}$$$ and $$$v_{S_{k_2}[i]}$$$.

Let's denote the numbers of $$$1$$$-s in $$$[f_k, 2 \cdot f_k)$$$ with $$$c$$$. It's obvious that $$$c = S_{k+1}[0]$$$.