SPOJ DCEPC206

Problem link: SPOJ Problem Set (classical): 10810. Its a Murder!

Given a sequence of integers (at most 10⁵ integers ranging from 0 to 10⁶. You have to go from left to right, and for each position, you have to add the sum of all the numbers before current position which are strictly smaller than the number in current position.

Well, if the numbers where sorted, then we could do it more easily, i.e. keep an array for cumulative sum, and then calculate the answer in O(n) time. However, this is not the case. So, what can we do? According the problem, you have to maintain the cumulative sum anyway. So for each number in the sequence, if we keep adding them one by one, we can break down the problem in two parts. Let's think of a data structure which keeps cumulative sums, and we can add numbers anywhere and still maintain the sum. Segment tree / or binary indexed tree could come handy in this situation, where, the later one is more straight forward and less memory hungry. Now the steps are:

1 ⇒ Query: When a number is to be added, what is the sum of numbers smaller than current one? Note, as we are adding one by one in the order they appear, so the question whether there are any numbers which came after current number is eliminated. It is guaranteed that, when you are adding the i^th number, all the numbers already present in the data-structure came before position i.

2 ⇒ Update: Once you have taken the partial sum for current number, it is now time to add the number in proper place, and update the cumulative sum structure. Here, clearly, appropriate position means, the position where the number would be placed when sorted. Now, we can do two things here. (a), we can sort the number and give each number an unique id to indicate its position in the sorted array. And (b), as the numbers are up to 10⁶, we can use the numbers as their indices or positions in the sorted array. It is faster and the memory overhead is not that of a big issue here.

Now, here are two more things to be taken care about, (a), there can be duplicate numbers, and (b), 0 can be present in the input sequence, which can cause some trouble if you use BIT. However, (a) is handled naturally, because the problem wanted you to add only strictly smaller numbers, so it doesn't matter if you add same numbers in the same positions. There will be no query points in-between the equal numbers. And for (b), there is really no need to worry about 0, cause it contributes nothing at all, just ignore 0.

So, the overall algorithm looks like the following:

for each number ai:
    if ai > 0:
        ans += BIT_Query(ai - 1)
        BIT_Update(ai, ai)

Here: BIT_Query(pos) returns the sum up to position pos and BIT_Update(pos, val) adds val in pos in the BIT structure. Solutions involving the segment tree is also similar. Just keep the tree of total sums, whenever you are trying to add a number, read the partial sum, and then update the structure.

Happy coding..

Various Usage of B.I.T.

I’ve always imagined what BIT (I will omit the dots, but don't confuse it with binary bits) can do and can’t. I have seen numerous times, experienced solvers say “Oh it’s easily solvable using BIT” leaving me wondering, “BIT on this problem! How on earth!” Still I haven’t found quite a good answer for this question. Looks like BIT can do as much as you can imagine it to do (and obviously design it to do so).

However, I am not going to explain the structure of BIT or how it works etc. As there are lots of well documented articles available on the net. I can’t help putting one specific link here, which is a TopCoder algorithm tutorial for BIT. So, if you are not familiar with BIT yet, first go ahead, and read that post.

In this post, we will discuss some of the most common application of BIT for solving data structure related problems, more specifically, where range sum updates and queries are involved.

Some methods used in this post:
Update(x, v): Adds V at the position x in BIT.
Query(x): Returns the cumulative value on position x in BIT.
Read the above tutorial to know how these methods are implemented.
We will be using 1-based indexing throughout the post.

(I) Point Update, Range Query:

This is the most common form of BIT, and just about 99% of online tutorials you will find on the net will describe this application. So here we don't have much to say. If your updates are like “Add v in the x position of the array”, and queries are like “What is the sum of elements in index range [a, b] in the array?”, then all you do for update is call Update(x, v) and answer queries by Query(b) - Query(a-1). Simple cumulative sum formula, nothing really astonishing. This problem is can be solved using BIT: SPOJ - MSE06H.

(II) Range Update, Point Query:

Although not something you see every now and then, but it's readily doable. Now you are required to update v over the range [a, b] and the query is performed on a single index x. So now you call update twice, first add v at index a, then remove v for indices larger than b, which is: Update(a, v); Update(b+1, -v); And for query, you simply call Query(x). Now to see why this works, the following reasoning can be made:

Lets consider, initially you have everything 0. So Query(x) will return 0, no matter what index you call it with. Suppose you have called Update(a, v) and Update(b+1, -v). Now what will happen if you call Query(x)? Three cases can arise:
1. if x < a, the query is not affected by the updates. So you get correct result.
2. if x > b, x will be affected by the update(a, v) since x ≥ a, and update(b+1, -v) since x ≥ b+1, therefore, v-v=0 so everything cancels out. Again, you get the correct result.
3. a ≤ x ≤ b. x is only affected by update(a, v), but not update(b+1, -v), therefore, Query(x)'s value is increased by v, and it will return the correct result.

As we can see, in this fashion, we can increment or decrement for a range [a, b], and get a single index effect for some index x. This problem is an example: SPOJ - UPDATEIT

(III) Range Update, Range Query:

I didn't even know it exists until I read some post in TopCoder forums (the post was made by: AnilKishore).

I will just re-state his explanation as it was quite clear itself. All we want here is to support range updates, and do range queries. As from the previous type, if we try to store range updates, the BIT structure effectively captures updates for single positions, instead of range/cumulative sums. However, we can do some tweaking to work around this problem.

Let's just consider only one update: Add v to [a, b] while the rest elements of the array is 0.
Now, consider sum(0, x) for all possible x, again three situation can arise:
1. 0 ≤ x < a : which results in 0
2. a ≤ x ≤ b : we get v * (x - (a-1))
3. b < x < n : we get v * (b - (a-1))

This suggests that, if we can find v*x for any index x, then we can get the sum(0, x) by subtracting T from it, where:
1. 0 ≤ x < : Sum should be 0, thus, T = 0
2. a ≤ x ≤ : Sum should be v*x-v*(a-1), thus, T = v*(a-1)
3. b < x < n : Sum should be 0, thus, T = -v*b + v*(a-1)

As, we can see, knowing T solves our problem, we can use another BIT to store this additive amount from which we can get:
0 for x < a, v*(a-1) for x in [a..b], -v*b+v(a-1) for x > b.

Now we have two BITs.
To add v in range [a, b]: Update(a, v), Update(b+1, -v) in the first BIT and Update(a, v*(a-1)) and Update(b+1, -v*b) on the second BIT.
To get sum in range [0, x]: you simply do Query_BIT1(x)*x - Query_BIT2(x);
Now you know how to find range sum for [a, b]. Just find sum(b) - sum(a-1) using the formula stated above.

Pretty impressive this one! SPOJ - HORRIBLE can be solved in this approach. And thanks to Iqram Mahmud for this nice problem.

(IV) 2D BIT

How to write Update and Query methods for 2D BIT is well described in the TopCoder tutorial I've mentioned above. The only thing to notice here is how the queries and updates work. 2D BIT is basically a BIT where each element is another BIT. Adding v on (x, y) means it's effect will be found throughout the rectangle [(x, y), (W, H)], and query for (x, y) gives you the result of the rectangle [(0, 0), (x, y)], assuming the total rectangle is [(0, 0), (W, H)]. SO when you query and update on this BIT, you have to be careful about how many times you are subtracting a rectangle and adding it. Simple set union formula works here. So if you want to get the result of a specific rectangle [(x1, y1), (x2, y2)], the following steps are necessary to do so:
V = Query(x2, y2)
V -= Query(x2, y1-1)
V -= Query(x1-1, y2)
V += Query(x1-1, y1-1)

This problem is an example: SPOJ - MATSUM

This page is likely to be updated if I find and manage to solve new types of BIT problems. Please let me know if there are mistakes. This post is inspired by some random posts lying around in TopCoder forums, I just wanted to put them in one place for future reference.