Bit manipulation can often feel like magic, but it is actually a precise set of rules governing how computers store data. In this challenge, we explore the relationship between a number and its successor to find a specific pattern in the binary landscape.
Problem Summary
You're given:
An array of prime integers called nums.
Your goal:
For each number, find the smallest possible integer ans[i] such that the bitwise OR of ans[i] and ans[i] + 1 equals the original number. If no such integer exists, return -1.
Intuition
The core of this problem lies in the operation . In binary, when you add 1 to a number, the trailing sequence of 1s (on the right) "flips" to 0s, and the first 0 from the right becomes a 1.
For example:
- If (binary
1001), then (binary1010). - (which is 11).
Notice that the OR operation effectively "fills in" the bit that changed during the addition. Specifically, it keeps all the original 1s and adds the new 1 created by the carry-over.
To minimize ans[i], we need to find the rightmost group of continuous 1s in the binary representation of nums[i]. By changing the most significant bit of that specific group of 1s to a 0, we create our candidate for ans[i]. When we add 1 back to this candidate, it flips that 0 (and all trailing 0s) back to 1s, satisfying the condition while keeping the value as small as possible.
Note on Prime Numbers: The only even prime is 2. For 2 (binary 10), there is no solution because and . Therefore, 2 always results in -1.
Walkthrough: Understanding the Examples
Let's look at nums = [11].
-
Binary Representation: 11 in binary is
1011. -
Find the rightmost group of 1s: The 1s at the end are
...11. - Identify the leading bit of that group: The group starts at the position (the 2s place).
- The Logic:
- If we take 11 and subtract that specific bit (2), we get .
- Binary of 9 is
1001. - Binary of (which is 10) is
1010. 1001 OR 1010 = 1011(which is 11).Result: 9 is our answer.
Code Blocks
C++
class Solution {
public:
vector<int> minBitwiseArray(vector<int>& nums) {
vector<int> ans;
for (const int num : nums) {
// Prime 2 is a special case that cannot satisfy the condition
if (num == 2) {
ans.push_back(-1);
} else {
ans.push_back(num - getLeadingOneOfLastGroupOfOnes(num));
}
}
return ans;
}
private:
int getLeadingOneOfLastGroupOfOnes(int num) {
int leadingOne = 1;
// Find the first 0 bit starting from the right
while ((num & leadingOne) > 0) {
leadingOne <<= 1;
}
// Shift back to get the highest 1-bit in the trailing sequence of 1s
return leadingOne >> 1;
}
};
Python
class Solution:
def minBitwiseArray(self, nums: list[int]) -> list[int]:
ans = []
for num in nums:
if num == 2:
ans.append(-1)
else:
# Find the leading 1 of the trailing group of ones
leading_one = 1
while (num & leading_one) > 0:
leading_one <<= 1
# Use shift-right to pinpoint the bit to subtract
ans.append(num - (leading_one >> 1))
return ans
JavaScript
/**
* @param {number[]} nums
* @return {number[]}
*/
var minBitwiseArray = function(nums) {
return nums.map(num => {
if (num === 2) return -1;
let leadingOne = 1;
// Find the first 0-bit to identify the end of the trailing 1s
while ((num & leadingOne) > 0) {
leadingOne <<= 1;
}
// Subtract half of the leadingOne value to flip the correct bit
return num - (leadingOne >> 1);
});
};
Key Takeaways
- Bitwise Carry-over: Adding 1 to a binary number affects all trailing 1s, turning them into 0s and making the first 0-bit a 1.
- Minimalist Logic: To minimize the answer, we target the highest possible bit within the lowest contiguous block of 1s to flip.
- Edge Case Handling: Recognizing that parity (even vs. odd) matters in bitwise operations, especially with the unique property of the prime number 2.
Final Thoughts
This problem is an excellent example of how bitwise manipulation is used in low-level systems programming and cryptography. In real-world scenarios, these types of operations are used to manage memory flags, optimize search algorithms, or even handle permissions in a file system. Understanding how bits flip during arithmetic operations like addition is a fundamental skill for high-performance computing.
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