LeetCode, ๐ƒ๐š๐ฒ-16/365-DSA-Coding ๐‰๐จ๐ฎ๐ซ๐ง๐ž๐ฒโ€ฆGoogle Prepโ€ฆ

Aaqib Ali
2 min readJan 15, 2025

--

Today I Learned This Problem: Minimize XOR

Problem Overview
The problem 2429. Minimize XOR involves modifying an integer num1 so that it has the same number of set bits (1s in binary) as num2, while keeping the XOR value between num1 and the result as small as possible. This problem is excellent for practicing bit manipulation and understanding binary operations.

Challenges Involved

  1. Bit Manipulation: Efficiently flipping bits in num1 to match the set bit count of num2.
  2. Minimizing XOR: Strategically choosing which bits to flip to ensure the result remains as close as possible to num1.
  3. Balancing Set Bits: Ensuring the number of set bits in the result matches that of num2.

Explanation

The solution uses bitwise operations to adjust num1:

  • Counting Set Bits: Determine the number of set bits in both num1 and num2.
  • Bit Flipping: If num1 has more set bits, flip bits from 1 to 0; if fewer, flip bits from 0 to 1.
  • Result Tracking: Return the modified num1 after balancing the set bits.

My Solution Approach

  1. Initialization:
  • Count set bits in num1 and num2.
  • Initialize res with num1.
  1. Bit Adjustment:
  • Iterate through 32 bits.
  • Flip bits in res to match the set bit count of num2.
  1. Return the Result:
  • Return res after adjusting the bits.

Python Code

class Solution:
def minimizeXor(self, num1: int, num2: int) -> int:
a = bin(num1).count('1')
b = bin(num2).count('1')
res = num1
for i in range(32):
if a > b and (num1 & (1 << i)):
res ^= (1 << i)
a -= 1
elif a < b and not (num1 & (1 << i)):
res ^= (1 << i)
a += 1
return res
LeetCode.

Other Possible Solutions

  1. Greedy Bit Setting: Set the most significant bits first to minimize the XOR result.
  2. Priority Queue: Use a priority queue to flip bits with the least impact on XOR.

Learnings

  • Gained deeper insights into bitwise operations and manipulation.
  • Learned strategies for balancing set bits efficiently.
  • Improved understanding of minimizing XOR through selective bit flipping.

Real-Life Usage

  • Data Compression: Balancing data representations to optimize storage.
  • Error Detection: Adjusting binary patterns for minimal error correction.

To sum up..
Thank you for reading! If this explanation helped clarify the problem, feel free to share your thoughts or alternative solutions. Keep learning, coding, sharing, and growing!

--

--

No responses yet