Generate All Permutations of Array in DSA C - Time & Space Complexity

Time Complexity: Generate All Permutations of Array

O(n!)

Understanding Time Complexity

When we generate all permutations of an array, we want to know how the time needed grows as the array gets bigger.

We ask: How many steps does it take to list every possible order of the array elements?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

void permute(int *arr, int start, int end) {
    if (start == end) {
        // print or store the permutation
        return;
    }
    for (int i = start; i <= end; i++) {
        swap(&arr[start], &arr[i]);
        permute(arr, start + 1, end);
        swap(&arr[start], &arr[i]);
    }
}
    

This code generates all possible orders (permutations) of the array elements by swapping and recursively exploring each choice.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive calls combined with a loop that swaps elements.
• How many times: The function calls itself for every possible arrangement, which grows very fast as the array size increases.

How Execution Grows With Input

Each time we add one more element, the number of possible orders multiplies by that new number.

Input Size (n)	Approx. Operations
3	6 (3 x 2 x 1)
5	120 (5 x 4 x 3 x 2 x 1)
7	5040 (7 x 6 x 5 x 4 x 3 x 2 x 1)

Pattern observation: The number of operations grows by multiplying all numbers from 1 up to n, which is very fast growth.

Final Time Complexity

Time Complexity: O(n!)

This means the time needed grows very fast, multiplying by a bigger number each time we add an element.

Common Mistake

[X] Wrong: "The time grows like n squared or n cubed because of the loops."

[OK] Correct: The loops are inside recursive calls that multiply the work many times, so the growth is much faster than simple loops.

Interview Connect

Understanding this growth helps you explain why generating all permutations is expensive and when it's practical to do so.

Self-Check

"What if we only wanted permutations of length k from n elements? How would the time complexity change?"

Practice

1. What does the process of generating all permutations of an array mean?

easy

A. Finding the maximum element in the array

B. Sorting the array in ascending order

C. Listing all possible orders of the array elements

D. Removing duplicate elements from the array

Solution

1. Step 1: Understand permutations

   Permutations are all possible ways to arrange elements in order.

2. Step 2: Apply to array elements

   Generating all permutations means listing every possible order of the array elements.

3. Final Answer:

   Listing all possible orders of the array elements -> Option C

4. Quick Check:

   Permutations = all orders [OK]

Hint: Permutations = all ways to arrange elements [OK]

Common Mistakes:

• Confusing permutations with sorting
• Thinking permutations remove duplicates
• Mixing permutations with finding max/min

2. Which of the following is the correct base case for a recursive function generating all permutations of an array arr of size n?

easy

A. if (start > n) { print(arr); return; }

B. if (start == n) { print(arr); return; }

C. if (n == 0) { return; }

D. if (start == 0) { print(arr); return; }

Solution

1. Step 1: Understand recursion base case

   The base case occurs when the start index reaches the array size, meaning a full permutation is formed.

2. Step 2: Match base case condition

   Condition start == n means all positions are fixed, so print the permutation and return.

3. Final Answer:

   if (start == n) { print(arr); return; } -> Option B

4. Quick Check:

   Base case when start equals array size [OK]

Hint: Base case: start index equals array length [OK]

Common Mistakes:

• Using start > n instead of start == n
• Printing when start == 0 (start of recursion)
• Missing base case leading to infinite recursion

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

void permute(int *arr, int start, int n) {
    if (start == n) {
        for (int i = 0; i < n; i++) printf("%d ", arr[i]);
        printf("\n");
        return;
    }
    for (int i = start; i < n; i++) {
        int temp = arr[start];
        arr[start] = arr[i];
        arr[i] = temp;
        permute(arr, start + 1, n);
        temp = arr[start];
        arr[start] = arr[i];
        arr[i] = temp;
    }
}

int main() {
    int arr[] = {1, 2};
    permute(arr, 0, 2);
    return 0;
}

medium

A. 1 2 \n 2 1 \n

B. 1 2 \n 1 2 \n

C. 2 1 \n 1 2 \n

D. No output

Solution

1. Step 1: Trace first call permute(arr, 0, 2)

   Start=0, loop i=0 to 1. Swap arr[0] with arr[0], call permute(arr,1,2).

2. Step 2: Trace second call permute(arr, 1, 2)

   Start=1, loop i=1 to 1. Swap arr[1] with arr[1], call permute(arr,2,2). Base case reached, print "1 2".

3. Step 3: Backtrack and swap arr[0] with arr[1]

   Now arr = {2,1}. Call permute(arr,1,2) again, print "2 1".

4. Final Answer:

   1 2 2 1 -> Option A

5. Quick Check:

   All permutations of [1,2] printed [OK]

Hint: Swap and backtrack to generate permutations [OK]

Common Mistakes:

• Not swapping back after recursive call
• Printing incomplete permutations
• Confusing loop indices

4. Identify the error in the following code snippet that generates permutations of an array arr of size n:

void permute(int *arr, int start, int n) {
    if (start == n) {
        for (int i = 0; i < n; i++) printf("%d ", arr[i]);
        printf("\n");
        return;
    }
    for (int i = start; i < n; i++) {
        int temp = arr[start];
        arr[start] = arr[i];
        arr[i] = temp;
        permute(arr, start + 1, n);
        // Missing swap back here
    }
}

medium

A. Missing swap back after recursive call causes incorrect permutations

B. Base case condition should be start > n

C. Loop should run from i = 0 to n

D. Printing inside base case is incorrect

Solution

1. Step 1: Understand swap and backtrack

   After swapping and recursive call, we must swap back to restore original array for next iteration.

2. Step 2: Identify missing swap back

   The comment shows swap back is missing, so array stays changed, causing wrong permutations.

3. Final Answer:

   Missing swap back after recursive call causes incorrect permutations -> Option A

4. Quick Check:

   Swap back needed to restore array [OK]

Hint: Always swap back after recursion to restore array [OK]

Common Mistakes:

• Forgetting to swap back after recursion
• Changing loop range incorrectly
• Wrong base case condition

5. Given an array arr = {1, 2, 2}, how many unique permutations will a correct algorithm generate that avoids duplicates?

hard

A. 6

B. 2

C. 4

D. 3

Solution

1. Step 1: Calculate total permutations without duplicates

   For 3 elements with two '2's, total permutations = 3! / 2! = 3.

2. Step 2: List unique permutations

   Unique permutations are: [1,2,2], [2,1,2], [2,2,1]. But careful with counting duplicates in generation.

3. Step 3: Consider correct algorithm avoiding duplicates

   Some algorithms generate 4 permutations including one duplicate if not careful, but correct unique count is 3.

4. Final Answer:

   3 -> Option D

5. Quick Check:

   Unique permutations = 3 [OK]

Hint: Use factorial formula with duplicates: n!/k! [OK]

Common Mistakes:

• Counting duplicates as unique permutations
• Ignoring duplicate elements in calculation
• Assuming all permutations are unique