Subsets Generation Using Bitmask in DSA Javascript - Time & Space Complexity

Time Complexity: Subsets Generation Using Bitmask

O(n * 2^n)

Understanding Time Complexity

We want to understand how the time needed to generate all subsets grows as the input list gets bigger.

How does the number of steps change when we have more items to consider?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

const subsets = (nums) => {
  const result = [];
  const n = nums.length;
  for (let mask = 0; mask < (1 << n); mask++) {
    const subset = [];
    for (let i = 0; i < n; i++) {
      if (mask & (1 << i)) subset.push(nums[i]);
    }
    result.push(subset);
  }
  return result;
};
    

This code creates all possible subsets of an array by using a bitmask to decide which elements to include.

Identify Repeating Operations

• Primary operation: Two nested loops: outer loop runs through all bitmasks, inner loop checks each element for inclusion.
• How many times: Outer loop runs 2^n times, inner loop runs n times for each outer loop.

How Execution Grows With Input

As the input size grows, the number of subsets doubles for each new element, and for each subset, we check all elements.

Input Size (n)	Approx. Operations
10	About 10 * 2^10 = 10,240
100	About 100 * 2^100 (a very large number)
1000	About 1000 * 2^1000 (extremely large, not practical)

Pattern observation: The operations grow very fast, doubling with each added element and multiplying by the number of elements checked.

Final Time Complexity

Time Complexity: O(n * 2^n)

This means the time needed grows very fast as the input size increases, because we generate all subsets and check each element for each subset.

Common Mistake

[X] Wrong: "Generating subsets is just O(2^n) because there are 2^n subsets."

[OK] Correct: We also spend time checking each element for every subset, so the total work multiplies by n, making it O(n * 2^n).

Interview Connect

Understanding this time complexity helps you explain why generating all subsets is expensive and shows you can analyze nested loops with exponential growth clearly.

Self-Check

"What if we only generated subsets of a fixed size k instead of all sizes? How would the time complexity change?"

Practice

1. What does the bitmask technique help generate when applied to a set of elements?

easy

A. Only subsets with even number of elements

B. Only the largest subset

C. All possible subsets of the set

D. Only subsets with odd number of elements

Solution

1. Step 1: Understand bitmask representation

   Each bit in a bitmask represents whether an element is included (1) or excluded (0) from a subset.

2. Step 2: Generate subsets using bitmask

   By iterating through all bitmasks from 0 to 2^n - 1, we get all possible combinations of elements, i.e., all subsets.

3. Final Answer:

   All possible subsets of the set -> Option C

4. Quick Check:

   Bitmask = subsets [OK]

Hint: Bitmask bits map directly to subset inclusion [OK]

Common Mistakes:

• Thinking bitmask generates only largest subsets
• Confusing subsets with permutations
• Assuming bitmask filters subsets by size

2. Which of the following is the correct way to start a loop for generating subsets using bitmask for an array of length n in JavaScript?

easy

A. for (let mask = 0; mask < n; mask++)

B. for (let mask = 0; mask < (1 << n); mask++)

C. for (let mask = 0; mask <= n; mask++)

D. for (let mask = 1; mask <= n; mask++)

Solution

1. Step 1: Understand bitmask range

   Bitmask must cover all numbers from 0 to 2^n - 1, which is (1 << n) in JavaScript.

2. Step 2: Correct loop syntax

   The loop should start at 0 and run while mask is less than (1 << n), incrementing mask by 1 each time.

3. Final Answer:

   for (let mask = 0; mask < (1 << n); mask++) -> Option B

4. Quick Check:

   Loop from 0 to 2^n-1 [OK]

Hint: Use (1 << n) for 2^n subsets [OK]

Common Mistakes:

• Starting loop at 1 instead of 0
• Using mask <= n instead of mask < 2^n
• Confusing n with 2^n in loop condition

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

const arr = ["a", "b"];
const subsets = [];
for (let mask = 0; mask < (1 << arr.length); mask++) {
  let subset = [];
  for (let i = 0; i < arr.length; i++) {
    if ((mask & (1 << i)) !== 0) {
      subset.push(arr[i]);
    }
  }
  subsets.push(subset);
}
console.log(subsets);

medium

A. [[], ["a"], ["b"], ["a", "b"]]

B. [["a"], ["b"], ["a", "b"]]

C. [[], ["b"], ["a"], ["a", "b"]]

D. [["a", "b"]]

Solution

1. Step 1: Understand bitmask iterations

   For arr length 2, mask runs from 0 to 3 (0b00 to 0b11), generating 4 subsets.

2. Step 2: Map mask bits to elements

   mask=0 (0b00) -> [], mask=1 (0b01) -> ["a"], mask=2 (0b10) -> ["b"], mask=3 (0b11) -> ["a", "b"]

3. Final Answer:

   [[], ["a"], ["b"], ["a", "b"]] -> Option A

4. Quick Check:

   All subsets of ["a", "b"] listed [OK]

Hint: Check bit positions for element inclusion [OK]

Common Mistakes:

• Missing empty subset []
• Swapping order of subsets
• Ignoring bitmask 0 producing empty subset

4. Identify the error in the following code snippet for generating subsets using bitmask:

const arr = [1, 2, 3];
const subsets = [];
for (let mask = 0; mask <= (1 << arr.length); mask++) {
  let subset = [];
  for (let i = 0; i < arr.length; i++) {
    if (mask & (1 << i)) {
      subset.push(arr[i]);
    }
  }
  subsets.push(subset);
}
console.log(subsets);

medium

A. The subsets array should be cleared inside the outer loop.

B. The inner loop should run until i <= arr.length.

C. The bitwise AND condition should be (mask | (1 << i)) instead of &.

D. The outer loop condition should be mask < (1 << arr.length), not <=.

Solution

1. Step 1: Check outer loop range

   The outer loop runs from 0 to (1 << arr.length) inclusive, which is one too many iterations (should be less than).

2. Step 2: Correct loop condition

   It should be mask < (1 << arr.length) to avoid an extra invalid subset iteration.

3. Final Answer:

   The outer loop condition should be mask < (1 << arr.length), not <=. -> Option D

4. Quick Check:

   Loop runs 2^n times, not 2^n+1 [OK]

Hint: Use <, not <=, for bitmask loop end [OK]

Common Mistakes:

• Using <= instead of < in loop condition
• Confusing bitwise AND (&) with OR (|)
• Extending inner loop beyond array length

5. Given an array arr = [1, 2, 3, 4], how many subsets will be generated by the bitmask method, and what is the subset generated when mask = 9? (mask is in decimal)

hard

A. 16 subsets; subset for mask 9 is [1, 4]

B. 8 subsets; subset for mask 9 is [2, 3]

C. 16 subsets; subset for mask 9 is [2, 4]

D. 15 subsets; subset for mask 9 is [1, 3, 4]

Solution

1. Step 1: Calculate total subsets for array length 4

   Total subsets = 2^4 = 16.

2. Step 2: Decode mask 9 in binary and map to elements

   9 in binary is 1001. Bits set at positions 0 and 3 correspond to arr[0] = 1 and arr[3] = 4.

3. Final Answer:

   16 subsets; subset for mask 9 is [1, 4] -> Option A

4. Quick Check:

   2^4 = 16 subsets; mask 9 = 1001 binary [OK]

Hint: Convert mask to binary, map bits to elements [OK]

Common Mistakes:

• Miscounting total subsets as 15
• Misinterpreting binary mask bits
• Confusing element indices with bit positions