# Summary of the---tree array + reverse order to the problem.

Source: Internet
Author: User

Cough, this map must be ....

First, when there is a very large number of arrays, we may change the value of a a[i], require a[n, and all add up, is undoubtedly the time complexity of O (n).

But if n is very large, O (n) time complexity is sure to kneel, so, what to do, with a magical tree-like array.

The tree-like array code is simple, but very powerful! More exciting is that its time complexity value requires O (LOGN)!!!

Well, the first thing is to put the c[n] to show out, how to do it, the code is as follows:

`int lowbit (int  t) {    return t& (-t);}`

This code, simple to explode, but can be the jurisdiction of each c[i] to find out! (specifically with binary)

Then, after changing a certain value, it is necessary to update c[i], of course, after using c[n], the number of arrays has changed from N to Logn!

So, we use the Updata function:

`void updata (int l,int  k) {     while (l<=N)     {           c[l]  +=k; //         l+=lowbit (l);  //    }}`

Finally, there is a sum function:

`int sum (int  k) {       int s=0;         while (k>0)     {           s+ =C[k];           K-= Lowbit (k);      }    Renturn s;}`

Of course, a tree-like array can be used for reverse order, which is a very efficient code:

` for (i=1; i<=n;i++) {      updata (c[i],1);      Ans+=sum (n)-sum (C[i]);} // Don't ask me why, I don't know ...`

Summary of the---tree array + reverse order to the problem.

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