The way to solve these sorts of problems is to write out the first few values, and look for a pattern
Number binary # bits set F(n)
1 0001 1 1
2 0010 1 2
3 0011 2 4
4 0100 1 5
5 0101 2 7
6 0110 2 9
7 0111 3 12
8 1000 1 13
9 1001 2 15
10 1010 2 17
11 1011 3 20
12 1100 2 22
13 1101 3 25
14 1110 3 28
15 1111 4 32
It takes a bit of staring at, but with some thought you notice that the binary-representations of the first 8 and the last 8 numbers are exactly the same, except the first 8 have a 0
in the MSB (most significant bit), while the last 8 have a 1
. Thus, for example to calculate F(12)
, we can just take F(7)
and add to it the number of set bits in 8, 9, 10, 11 and 12. But that's the same as the number of set-bits in 0, 1, 2, 3, and 4 (ie. F(4)
), plus one more for each number!
# binary
0 0 000
1 0 001
2 0 010
3 0 011
4 0 100
5 0 101
6 0 110
7 0 111
8 1 000 <--Notice that rightmost-bits repeat themselves
9 1 001 except now we have an extra '1' in every number!
10 1 010
11 1 011
12 1 100
Thus, for 8 <= n <= 15
, F(n) = F(7) + F(n-8) + (n-7)
. Similarly, we could note that for 4 <= n <= 7
, F(n) = F(3) + F(n-4) + (n-3)
; and for 2 <= n <= 3
, F(n) = F(1) + F(n-2) + (n-1)
. In general, if we set a = 2^(floor(log(n)))
, then F(n) = F(a-1) + F(n-a) + (n-a+1)
This doesn't quite give us an O(log n)
algorithm; however, doing so is easy. If a = 2^x
, then note in the table above that for a-1
, the first bit is set exactly a/2
times, the second bit is set exactly a/2
times, the third bit... all the way to the x'th bit. Thus, F(a-1) = x*a/2 = x*2^(x-1)
. In the above equation, this gives us
F(n) = x*2
x-1 + F(n-2
x) + (n-2
x+1)
Where x = floor(log(n))
. Each iteration of calculating F
will essentially remove the MSB; thus, this is an O(log(n))
algorithm.
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