Combinations - Practice Coding Problems

Combinations - Problem

Backtracking Medium

Imagine you're organizing a team selection process where you need to pick exactly k people from a group of n candidates numbered from 1 to n. Your task is to generate all possible team combinations that can be formed.

Given two integers n and k, return all possible combinations of k numbers chosen from the range [1, n]. The order of combinations in your result doesn't matter, but each combination should contain unique numbers.

Example: If n=4 and k=2, you could form teams like [1,2], [1,3], [1,4], [2,3], [2,4], [3,4] - that's 6 different combinations!

Input & Output

example_1.py — Basic Combination

$ Input: n = 4, k = 2

› Output: [[1,2],[1,3],[1,4],[2,3],[2,4],[3,4]]

💡 Note: All possible ways to choose 2 numbers from [1,2,3,4]. We get C(4,2) = 6 combinations total.

example_2.py — Single Element

$ Input: n = 1, k = 1

› Output: [[1]]

💡 Note: Only one way to choose 1 number from [1] - the combination [1] itself.

example_3.py — Larger Set

$ Input: n = 5, k = 3

› Output: [[1,2,3],[1,2,4],[1,2,5],[1,3,4],[1,3,5],[1,4,5],[2,3,4],[2,3,5],[2,4,5],[3,4,5]]

💡 Note: All possible ways to choose 3 numbers from [1,2,3,4,5]. We get C(5,3) = 10 combinations total.

Visualization

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Understanding the Visualization

Start Empty

Begin with an empty team and list of all available players [1,2,3,4]

Fill Positions

For position 1, try player 1, then explore all possibilities for position 2

Complete & Backtrack

When team is full (k players), save it. Then backtrack and try next player

Systematic Exploration

Continue until all possible team combinations are found

Key Takeaway

🎯 Key Insight: Backtracking with ordered selection ensures we generate all unique combinations exactly once, while pruning optimizations eliminate impossible paths early for better performance.

Time & Space Complexity

Time Complexity

⏱️

O(C(n,k) * k)

We generate exactly C(n,k) combinations, and each takes O(k) time to construct. The pruning optimization doesn't change worst-case but improves average performance.

✓ Linear Growth

Space Complexity

O(C(n,k) * k + k)

We store C(n,k) combinations each of size k, plus O(k) space for recursion stack depth

⚡ Linearithmic Space

Constraints

1 ≤ k ≤ n ≤ 20
k cannot exceed n (you can't choose more items than available)
Output size grows exponentially - C(n,k) can be up to C(20,10) = 184,756

Asked in

G Google 45 a Amazon 38 ⊞ Microsoft 32 f Meta 28

The optimal solution uses backtracking with pruning to generate all combinations efficiently. Key insights: build combinations incrementally, maintain sorted order to avoid duplicates, and prune branches early when it's impossible to complete a valid combination. Time complexity is O(C(n,k) × k) which is optimal since we must generate all C(n,k) combinations.

Common Approaches

Approach	Time	Space	Notes
✓ Optimized Backtracking	O(C(n,k) * k)	O(C(n,k) * k + k)	Efficient backtracking with early termination and pruning optimizations

Optimized Backtracking — Algorithm Steps

Start building combination from number 1
For each position, try numbers from current position to n
Prune early: if remaining numbers + current combination < k, skip
When combination size equals k, add to result
Backtrack by removing last number and continue with next option
Use optimizations to reduce unnecessary recursive calls

Visualization

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Step-by-Step Walkthrough

Start & Prune

Begin with [], check if we can reach k=2 from each starting position

Build Systematically

For [1], we need 1 more number from {2,3,4} - all possible

Early Termination

For [2], we need 1 more from {3,4} - still possible

Pruning in Action

For [4], we need 1 more but no numbers left - prune this branch!

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_RESULTS 10000
#define MAX_K 20

int results[MAX_RESULTS][MAX_K];
int resultSizes[MAX_RESULTS];
int resultCount = 0;
int current[MAX_K];
int currentSize = 0;

void backtrack(int start, int n, int k) {
    // Pruning: if we can't complete combination even with all remaining numbers
    if (currentSize + (n - start + 1) < k) {
        return;
    }
    
    // Base case: found a valid combination
    if (currentSize == k) {
        for (int i = 0; i < k; i++) {
            results[resultCount][i] = current[i];
        }
        resultSizes[resultCount] = k;
        resultCount++;
        return;
    }
    
    // Try each number from start to n
    for (int i = start; i <= n; i++) {
        current[currentSize] = i;
        currentSize++;
        backtrack(i + 1, n, k);
        currentSize--;
    }
}

int main() {
    int n, k;
    char input[100];
    fgets(input, sizeof(input), stdin);
    sscanf(input, "{\"n\":%d,\"k\":%d}", &n, &k);
    
    resultCount = 0;
    currentSize = 0;
    backtrack(1, n, k);
    
    printf("[");
    for (int i = 0; i < resultCount; i++) {
        printf("[");
        for (int j = 0; j < resultSizes[i]; j++) {
            printf("%d", results[i][j]);
            if (j < resultSizes[i] - 1) printf(",");
        }
        printf("]");
        if (i < resultCount - 1) printf(",");
    }
    printf("]\n");
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(C(n,k) * k)

We generate exactly C(n,k) combinations, and each takes O(k) time to construct. The pruning optimization doesn't change worst-case but improves average performance.

✓ Linear Growth

Space Complexity

O(C(n,k) * k + k)

We store C(n,k) combinations each of size k, plus O(k) space for recursion stack depth

⚡ Linearithmic Space

Constraints

1 ≤ k ≤ n ≤ 20
k cannot exceed n (you can't choose more items than available)
Output size grows exponentially - C(n,k) can be up to C(20,10) = 184,756

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Smart Actions

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Optimized Backtracking — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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