Count Set Bits Brian Kernighan Algorithm in DSA C# - Time & Space Complexity

Time Complexity: Count Set Bits Brian Kernighan Algorithm

O(k)

Understanding Time Complexity

We want to understand how fast the Brian Kernighan algorithm counts the number of 1s in a number's binary form.

How does the time taken change as the number gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

int CountSetBits(int n) {
    int count = 0;
    while (n > 0) {
        n = n & (n - 1);
        count++;
    }
    return count;
}
    

This code counts how many bits are set to 1 in the binary form of the number n.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The while loop runs once for each set bit in n.
• How many times: Equal to the number of 1s in the binary representation of n.

How Execution Grows With Input

Each loop removes one set bit, so the number of loops depends on how many 1s are in n.

Input Size (n)	Approx. Operations (set bits)
10 (binary 1010)	2
15 (binary 1111)	4
1023 (binary 1111111111)	10

Pattern observation: The time grows with the number of 1 bits, not the total bits or the value of n.

Final Time Complexity

Time Complexity: O(k) where k is the number of set bits in n.

This means the algorithm runs faster if there are fewer 1s, and slower if there are many 1s.

Common Mistake

[X] Wrong: "The loop always runs for all bits in the number (like 32 or 64 times)."

[OK] Correct: The loop only runs as many times as there are 1s, so it can be much faster than checking every bit.

Interview Connect

Understanding this algorithm shows you can find efficient ways to solve problems by focusing on what really matters, like the number of set bits instead of total bits.

Self-Check

"What if we changed the input to always have only one set bit? How would the time complexity change?"

Practice

1. What does the Brian Kernighan algorithm do when counting set bits in an integer?

easy

A. It converts the number to a string and counts characters.

B. It counts the total number of digits in the integer.

C. It shifts the bits to the right one by one and counts zeros.

D. It repeatedly clears the least significant set bit until the number becomes zero.

Solution

1. Step 1: Understand the algorithm's main operation

   The Brian Kernighan algorithm works by clearing the least significant set bit in each iteration.

2. Step 2: Effect of clearing bits on counting set bits

   Each time it clears a set bit, it increments the count until the number becomes zero.

3. Final Answer:

   It repeatedly clears the least significant set bit until the number becomes zero. -> Option D

4. Quick Check:

   Clearing bits counts set bits [OK]

Hint: Clear least set bit repeatedly to count ones [OK]

Common Mistakes:

• Counting zeros instead of ones
• Using string conversion to count bits
• Shifting bits without clearing

2. Which of the following is the correct syntax to clear the least significant set bit in C# using Brian Kernighan's method?

easy

A. n = n & (n - 1);

B. n = n | (n - 1);

C. n = n ^ (n + 1);

D. n = n << 1;

Solution

1. Step 1: Identify the bit clearing operation

   The expression n = n & (n - 1) clears the least significant set bit in n.

2. Step 2: Verify other options

   Other options perform different bitwise operations that do not clear the least significant set bit.

3. Final Answer:

   n = n & (n - 1); -> Option A

4. Quick Check:

   Use n & (n-1) to clear least set bit [OK]

Hint: Use n & (n-1) to clear least set bit [OK]

Common Mistakes:

• Using OR instead of AND
• Using XOR with n+1 incorrectly
• Using left shift which moves bits

3. What is the output of the following C# code snippet?

int countSetBits(int n) {
    int count = 0;
    while (n > 0) {
        n = n & (n - 1);
        count++;
    }
    return count;
}

Console.WriteLine(countSetBits(13));

medium

A. 3

B. 4

C. 2

D. 5

Solution

1. Step 1: Convert 13 to binary and count set bits

   13 in binary is 1101, which has three set bits (1s).

2. Step 2: Apply Brian Kernighan's algorithm

   Each iteration clears one set bit, so the loop runs 3 times, incrementing count to 3.

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Binary 1101 has 3 ones [OK]

Hint: Count 1s in binary of number [OK]

Common Mistakes:

• Counting total bits instead of set bits
• Misunderstanding bitwise AND operation
• Off-by-one errors in loop

4. Identify the error in the following C# code that attempts to count set bits using Brian Kernighan's algorithm:

int countSetBits(int n) {
    int count = 0;
    while (n != 0) {
        n = n & (n + 1);
        count++;
    }
    return count;
}

medium

A. Missing return statement

B. Using n & (n + 1) instead of n & (n - 1)

C. Count variable not initialized

D. Incorrect loop condition; should be n > 0

Solution

1. Step 1: Check the bit clearing expression

   The code uses n & (n + 1), which does not clear the least significant set bit correctly.

2. Step 2: Correct expression for clearing bits

   The correct expression is n & (n - 1) to clear the least significant set bit.

3. Final Answer:

   Using n & (n + 1) instead of n & (n - 1) -> Option B

4. Quick Check:

   Use n & (n-1) to clear bits [OK]

Hint: Always use n & (n-1) to clear least set bit [OK]

Common Mistakes:

• Using n & (n + 1) which is incorrect
• Confusing loop conditions
• Forgetting to initialize count

5. You want to count set bits in a large list of integers efficiently using Brian Kernighan's algorithm in C#. Which approach is best to optimize performance?

hard

A. Convert each integer to binary string and count '1' characters sequentially.

B. Use a simple for loop shifting bits right and counting set bits sequentially.

C. Use Brian Kernighan's algorithm inside a parallel loop to count bits for each integer concurrently.

D. Store all integers in a list and count set bits only for the first element.

Solution

1. Step 1: Understand the need for efficiency

   Counting set bits for many integers can be slow if done sequentially.

2. Step 2: Use parallel processing with Brian Kernighan's algorithm

   Running the efficient bit counting algorithm concurrently on multiple integers speeds up processing.

3. Final Answer:

   Use Brian Kernighan's algorithm inside a parallel loop to count bits for each integer concurrently. -> Option C

4. Quick Check:

   Parallel + efficient algorithm = best performance [OK]

Hint: Combine parallelism with Brian Kernighan's method [OK]

Common Mistakes:

• Using string conversion which is slow
• Sequential bit shifting which is less efficient
• Ignoring concurrency for large data