# Posts published in “Bit”

Given 3 positives numbers ab and c. Return the minimum flips required in some bits of a and b to make ( a OR b == c ). (bitwise OR operation).
Flip operation consists of change any single bit 1 to 0 or change the bit 0 to 1 in their binary representation.

Example 1:

Input: a = 2, b = 6, c = 5
Output: 3
Explanation: After flips a = 1 , b = 4 , c = 5 such that (a OR b == c)

Example 2:

Input: a = 4, b = 2, c = 7
Output: 1


Example 3:

Input: a = 1, b = 2, c = 3
Output: 0


Constraints:

• 1 <= a <= 10^9
• 1 <= b <= 10^9
• 1 <= c <= 10^9

## Solution: Bit operation

If the bit of c is 1, a / b at least has one 1. cost = 1 – ((a | b) & 1)
If the bit of c is 0, a / b must be 0, cost = (a & 1) + (b & 1)

Time complexity: O(32)
Space complexity: O(1)

## C++

Reverse bits of a given 32 bits unsigned integer.

Example 1:

Input: 00000010100101000001111010011100
Output: 00111001011110000010100101000000
Explanation: The input binary string 00000010100101000001111010011100 represents the unsigned integer 43261596, so return 964176192 which its binary representation is 00111001011110000010100101000000.


Example 2:

Input: 11111111111111111111111111111101
Output: 10111111111111111111111111111111
Explanation: The input binary string 11111111111111111111111111111101 represents the unsigned integer 4294967293, so return 3221225471 which its binary representation is 10101111110010110010011101101001.

Note:

• Note that in some languages such as Java, there is no unsigned integer type. In this case, both input and output will be given as signed integer type and should not affect your implementation, as the internal binary representation of the integer is the same whether it is signed or unsigned.
• In Java, the compiler represents the signed integers using 2’s complement notation. Therefore, in Example 2 above the input represents the signed integer -3 and the output represents the signed integer -1073741825.

If this function is called many times, how would you optimize it?

Solution: Bit operation

Time complexity: O(1)
Space complexity: O(1)

## C++

Every non-negative integer N has a binary representation.  For example, 5 can be represented as "101" in binary, 11 as "1011" in binary, and so on.  Note that except for N = 0, there are no leading zeroes in any binary representation.

The complement of a binary representation is the number in binary you get when changing every 1 to a 0 and 0 to a 1.  For example, the complement of "101" in binary is "010" in binary.

For a given number N in base-10, return the complement of it’s binary representation as a base-10 integer.

Example 1:

Input: 5
Output: 2
Explanation: 5 is "101" in binary, with complement "010" in binary, which is 2 in base-10.


Example 2:

Input: 7
Output: 0
Explanation: 7 is "111" in binary, with complement "000" in binary, which is 0 in base-10.


Example 3:

Input: 10
Output: 5
Explanation: 10 is "1010" in binary, with complement "0101" in binary, which is 5 in base-10.


Note:

1. 0 <= N < 10^9

## Solution: Bit

Find the smallest binary number c that is all 1s, (e.g. “111”, “11111”) that is greater or equal to N.
ans = C ^ N or C – N

Time complexity: O(log(n))
Space complexity: O(1)

## C++

Given an array of integers A, find the number of triples of indices (i, j, k) such that:

• 0 <= i < A.length
• 0 <= j < A.length
• 0 <= k < A.length
• A[i] & A[j] & A[k] == 0, where & represents the bitwise-AND operator.

Example 1:

Input: [2,1,3]
Output: 12
Explanation: We could choose the following i, j, k triples:
(i=0, j=0, k=1) : 2 & 2 & 1
(i=0, j=1, k=0) : 2 & 1 & 2
(i=0, j=1, k=1) : 2 & 1 & 1
(i=0, j=1, k=2) : 2 & 1 & 3
(i=0, j=2, k=1) : 2 & 3 & 1
(i=1, j=0, k=0) : 1 & 2 & 2
(i=1, j=0, k=1) : 1 & 2 & 1
(i=1, j=0, k=2) : 1 & 2 & 3
(i=1, j=1, k=0) : 1 & 1 & 2
(i=1, j=2, k=0) : 1 & 3 & 2
(i=2, j=0, k=1) : 3 & 2 & 1
(i=2, j=1, k=0) : 3 & 1 & 2


Note:

1. 1 <= A.length <= 1000
2. 0 <= A[i] < 2^16

## Solution: Counting

Time complexity: O(n^2 + n * max(A))
Space complexity: O(max(A))

# Problem

We have an array A of non-negative integers.

For every (contiguous) subarray B = [A[i], A[i+1], ..., A[j]] (with i <= j), we take the bitwise OR of all the elements in B, obtaining a result A[i] | A[i+1] | ... | A[j].

Return the number of possible results.  (Results that occur more than once are only counted once in the final answer.)

Example 1:

Input: 
Output: 1
Explanation:
There is only one possible result: 0.


Example 2:

Input: [1,1,2]
Output: 3
Explanation:
The possible subarrays are , , , [1, 1], [1, 2], [1, 1, 2].
These yield the results 1, 1, 2, 1, 3, 3.
There are 3 unique values, so the answer is 3.


Example 3:

Input: [1,2,4]
Output: 6
Explanation:
The possible results are 1, 2, 3, 4, 6, and 7.

Note:

1. 1 <= A.length <= 50000
2. 0 <= A[i] <= 10^9 # Solution 1: DP (TLE)

dp[i][j] := A[i] | A[i + 1] | … | A[j]

dp[i][j] = dp[i][j – 1] | A[j]

ans = len(set(dp))

Time complexity: O(n^2)

Space complexity: O(n^2) -> O(n)

# Solution 2: DP opted

dp[i] := {A[i], A[i] | A[i – 1], A[i] | A[i – 1] | A[i – 2], … , A[i] | A[i – 1] | … | A}, bitwise ors of all subarrays end with A[i].

|dp[i]| <= 32

Proof: all the elements (in the order of above sequence) in dp[i] are monotonically increasing by flipping 0 bits to 1 from A[i].

There are at most 32 0s in A[i]. Thus the size of the set is <= 32.

e.g. 举例：Worst Case 最坏情况 A = [8, 4, 2, 1, 0] A[i] = 2^(n-i)。

A = 0，dp = {0, 0 | 1, 0 | 1 | 2, 0 | 1 | 2 | 4, 0 | 1 | 2 | 4 | 8} = {0, 1, 3, 7, 15}.

Time complexity: O(n*log(max(A))) < O(32n)

Space complexity: O(n*log(max(A)) < O(32n)

## Python3

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