**NOTE** : Knowledge of Binary Indexed Trees is a prerequisite.

## Problem Statement

Assume we need to solve the following problem. We have an array, *A* of length *N* with only non-negative values. We want to perform the following operations on this array:

Update value at a given position

Compute prefix sum of

*A*upto*i*,*i*≤*N*Search for a prefix sum (something like a

`lower_bound`

in the prefix sums array of*A*)

## Basic Solution

Seeing such a problem we might think of using a Binary Indexed Tree (BIT) and implementing a binary search for type 3 operation. Its easy to see that binary search is possible here because prefix sums array is monotonic (only non-negative values in *A*).

The only issue with this is that binary search in a BIT has time complexity of *O*(*log* ^{2}(*N*)) (other operations can be done in *O*(*log*(*N*))). Even though this is naive, here is how to do it:

**Implementation**

Most of the times this would be fast enough (because of small constant of above technique). But if the time limit is very tight, we will need something faster. Also we must note that there are other techniques like segment trees, policy based data structures, treaps, etc. which can perform operation 3 in *O*(*log*(*N*)). But they are harder to implement and have a high constant factor associated with their time complexities due to which they might be even slower than *O*(*log* ^{2}(*N*)) of BIT.

Hence we need an efficient searching method in BIT itself.

## Efficient Solution

We will make use of **binary lifting** to achieve *O*(*log*(*N*)) (well I actually do not know if this technique has a name but I am calling it binary lifting because the algorithm is similar to binary lifting in trees using sparse table).

#### What is binary lifting?

In binary lifting, a value is increased (or lifted) by powers of 2, starting with the highest possible power of 2, 2^{⌊ log(N)⌋}, down to the lowest power, 2^{0}.

#### How binary lifting is used?

We are trying to find `pos`

, which is the position of lower bound of `v`

in prefix sums array, where `v`

is the value we are searching for. So, we initialize `pos = 0`

and set each bit of `pos`

, from most significant bit to least significant bit. Whenever a bit is set to 1, the value of `pos`

increases (or lifts). While increasing or lifting `pos`

, we make sure that prefix sum till `pos`

should be less than `v`

, for which we maintain the prefix `sum`

and update it whenever we increase or lift `pos`

. See implementation.

**More insight**

It is not very difficult to come up with a rigorous proof of correctness and I am leaving it as an exercise for the readers.

**HINT** : Each position in `bit`

stores sum of a power of 2 elements, sum of last *i*& - *i* (this isolates least significant bit of *i*) elements till *i* are stored at position *i* in `bit`

. I hope this will atleast help you think of an intuitive proof.

#### Implementation :

```
// This is equivalent to calculating lower_bound on prefix sums array
// LOGN = log(N)
int bit[N]; // BIT array
int bit_search(int v)
{
int sum = 0;
int pos = 0;
for(int i=LOGN; i>=0; i--)
{
if(pos + (1 << i) < N and sum + bit[pos + (1 << i)] < v)
{
sum += bit[pos + (1 << i)];
pos += (1 << i);
}
}
return pos + 1; // +1 because 'pos' will have position of largest value less than 'v'
}
```

#### Example

I am using the example from TopCoder BIT Tutorial, which I recommend you to take a look at if you haven't already (**very important** for understanding this).

Let this be array *A*,

The BIT for this array will look as follows,

(Illustrations taken from https://www.topcoder.com/community/data-science/data-science-tutorials/binary-indexed-trees/)

Let us assume we want to search for `v = 27`

. The blue arrow shows the direction in which we proceed in our search. Red shows that we can't lift `pos`

. Green shows that we lift `pos`

.

This is how the algorithm proceeds,

Finally, *pos* = 13, *sum* = 26 after last step.

*Targetpos* = *pos* + 1 = 14. You can verify that 14 is indeed the lower bound of 27 in prefix sums array!

I hope this helps in understanding the algorithm better. If it is still unclear go through TopCoder BIT Tutorial to understand the structure of BIT so that it can be related to this example.

#### Taking this forward

You must have noted that proof of correctness of this approach relies on the property of the prefix sums array that it **monotonic**. This means that this approach can be used for with any operation that maintains the monotonicity of the prefix array, like multiplication of positive numbers, etc.

You can also take a look at a similar blog by adamant.

Thats all folks!

**PS** : Please let me know if there are any mistakes.

**UPDATE** : As requested by some people, I have added an example for explain the algorithm.

Auto comment: topic has been updated by sdnr1 (previous revision, new revision, compare).Link to problem on this technique

It would be nice if you could provide solution to this problem as an example in your blog

Link to actual problem on this technique

Auto comment: topic has been updated by sdnr1 (previous revision, new revision, compare).This trick is really simple and awesome, thanks for the easy to understand code, I was able to learn the idea purely from the implementation.

This kinda reminds me of segment tree walks, and I find this type of thing (logn instead of binary search) really cool.

Basically here is how I understand it:

1) Greedily (biggest power of 2 first) take the biggest possible position such that prefix_sum[pos] < v. (<= for upper_bound)

2) Add 1 to this position, because prefix_sum[pos+1] >= v. Note that it is impossible for it to be < v, otherwise it wouldn't be greatest position.

3) We can just track the sum by adding bit[pos + (1 << i)] < v because when we are looping in decreasing order the newly added bit will always be the least significant 1 bit, and i & -i actually just gives you the least significant 1 bit.

Hope you all had as much as fun as I did reading this.

Nobody asked you, you are just a borderline expert.

starting with the highest possible power of 2, 2^{⌈ log(N)⌉ }, down to the lowest power, 2^{0}.The highest power should be 2

^{⌈ log(N)⌉ - 1}. This is because ⌈log(N)⌉ represents the total number of bits needed to represent a non-negative integerN. But we need to subtract 1 from this number as your power is indexed from 0.Thanks for pointing out. Although I had noticed it earlier but since we are already checking everytime before we increment

`pos`

, it does not matter even if we start from a higher power, thus did not change it. Anyway I have made a change from ceil to floor (ceil — 1 will miss the last position if N is power of 2).It would nice if you could provide a step-by-step run of your algorithm on a sample. This would help readers to better visualize what you are trying to express.

Whenever a bit is set to 1, the value of pos increases (or lifts)This statement is pretty confusing. You initially defined

posto be the lower bound ofvin the prefix sums of arrayA— this means that, assumingvexists in the prefix sums,prefix_sums[pos] = v. In other words,posshould be a fixed value.We actually are trying to find the value of

`pos`

. So initially it is 0, and we greedily lift it to the target value. Also it is not necessary thatvexists in prefix sums array (similar to how lower_bound works for`set`

).Thanks for pointing out. Fixed it now.

Nice tutorial. Can you provide some more problems using the concept of binary lifting?

The similar tutorial by adamant

Don't worry bro, all these blogs are basically made for contribution whoring, no other reason.

Blogewoosh too is nothing but translated words of previously existing polish blogs to english and voila, you are now on top of contribution list.

I believe that the desire to share knowledge is also a reason. I don't worry, as I don't think that it's bad that there are several topics on the same theme. Also, the navigation on CF is really bad, I mean sometimes it's hard to find some topic that you have read before, and the reference to adamant's article here may help someone...

Exactly, sometimes it's too hard to find something you've read before, it might be good to have a whole new section on codeforces for tutorials and related stuff, separate from other blogs.

That is true. I did not come across this blog before. When I discovered this technique I wanted to share it. The contribution I am getting is just an added bonus :').

Also, thanks for sharing adamant's blog. I have added the link to the above mentioned tutorial in my blog itself.

I would like to suggest a formal proof for this technique.

I read somewhere that form of binary search is actually called meta binary search.

Given a non-decreasing function and a target value

Vwhere anda_{ i}is thei^{ th}digit in the base 2 representation ofx. Meta binary search returns a value,X, whose base 2 representation has exactlyndigits andf(X) =V. It should be clear thatg(i) must also be non-decreasing for alli. Otherwise,f(x) might not be non-decreasing.The correctness of meta binary search can be proved the exact same way as that of the classical binary search. In fact, they operate in the same way just that meta binary search is more fancy.

Now, let

BIT(i) represent the value stored at indexiin a binary-indexed tree/fenwick tree.Let's assume that

BIT(i) is non-decreasing for alliand prove the following lemma:We want to show that, for all indexes,

x, of the actual array, whereArepresents the actual array andk_{ i}is the value that we obtain after thei^{ th}iteration of the meta binary search.Proof by induction:

We shall follow the fenwick tree construction strategy in this topcoder article.

Note that the value of

fis initially 0 because alla_{ i}are 0.Base case: If we have

xas a power of 2 to be the input tof, our lemma is trivially true because we havef(x) =BIT(x) andBIT(x) contains the sum of elements from index 1 toxas mentioned in the article linked above. This proves our lemma for values forxwith exactly 1 set bit.Induction Hypothesis: Let's assume that our lemma is true for values of

xwith at mostkbits set where 1 ≤k. Formally, for values ofxwith at mostkbits set,Now, suppose that iteration

iof the meta binary search produces the valueX, which has exactlykbits set. Now, what happens if we set an additional bit (suppose that it is theb^{ th}bit)? We have:Hence, we now have the fact that given any index

xin the actual array, we have the mappingQ.E.D.With the above lemma, we can use meta binary search to swiftly compute

xfor whichf(x) =Vfor any givenVwhich is exactly what we were after.who are you ?

That's for me to know and for you to find out.

This algorithm is called exponential search.

Thanks for correcting me. When I first heard of this algorithm, I know it as meta binary search. Now I know the formal name.

Here is a Codeforces problem you can practice this technique on: 992E - Nastya and King-Shamans

39424824 is my solution performing this search in time, vs. 39424729 using plain binary search on the binary indexed tree for a search.

can u pls explain how this query function is working in your code?

The same in Petr's blog: https://petr-mitrichev.blogspot.com/2018/02/a-fenwick-bound-week.html

Yeah. But it is somewhat difficult to understand the concept directly from his text as he doesn't explain in detail.

I tried to use this instead of binary search on the interactive problem 1011D - Rocket but my Submission 42010046 gives WA(Unexpected End of file) on Test case 9 even after all the debugging I could possibly do (42010611), I am probably making the most noobish mistake ever but could someone help me out on this?

Is it really useful to do this, when you can do binary search on queries on the fenwick tree?

Apart from the extra $$$log(N)$$$ factor in case of binary search, it doesn't really make any difference right?

So basically, is there some use case where binary lifting would work but binary search won't?

Another similar problem from Educational Codeforces Round 87

Video explanation of same blog. Link