Solving Range Minimum Queries using Binary Indexed Trees (Fenwick Trees)

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Formally, the Range Minimum Query Problem is:

Given an array A[0, N-1] , Find the position of the element with the minimum value between any two given indices.

Now, the standard solution is to use a segment tree and has been described here. Another data structure used to solve range queries is the Binary-Indexed Tree(Fenwick Tree), and it is much easier to understand code.

Can the range minimum query problem be solved by Binary-Indexed-Trees, and how? An implementation of the update and query function would be appreciated.

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I’m pretty sure that can’t. BIT can solve this type of query: Find min/max element in interval [ 0, x ] where x = { 0, n - 1 }. But there are some restriction with this BIT.

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can we not do min(query[2],query[0])?

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can we not do min(query[2],query[0])?

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can we not do min(query[2],query[0])?

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actually it’s possible to do RMQ with BIT , the following paper describe the idea and i want to share it so it will be useful for everyone interested in this topic

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If it can solve queries in [0,x], then it can also solve queries in any [a,x] which is essentially query[x]-query[a]. Can you be more clear?

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No. You can’t do this. Here is an example:
Array = { 5, 3, 2 },
Look for minimum in interval: [ 1, 2 ].
query[ 2 ] = 2, query[ 0 ] = 5.
Now, you would do this 2 - 5, which is obviously wrong.

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For Array = { 2, 3, 5 }, min(query[2],query[0]) = min(2,2) = 2, while the correct answer is 3. No, it won’t work.

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Refer this