🚀Day 12. Coin Change (Minimum Coins) 🧠

The problem can be found at the following link: Question Link

💡 Problem Description:

Given an array coins[] of distinct denominations and an integer sum, determine the minimum number of coins required to make up the given sum. You have an infinite supply of each type of coin. If the sum cannot be formed, return -1.

🔍 Example Walkthrough:

Example 1:

Input:

coins[] = [25, 10, 5]
sum = 30

Output:

2

Explanation:

The minimum number of coins required is 2 (25 + 5).

Example 2:

Input:

coins[] = [9, 6, 5, 1]
sum = 19

Output:

3

Explanation:

The minimum number of coins required is 3 (9 + 9 + 1).

Example 3:

Input:

coins[] = [5, 1]
sum = 0

Output:

0

Explanation:

For a target sum of 0, no coins are needed.

Example 4:

Input:

coins[] = [4, 6, 2]
sum = 5

Output:

-1

Explanation:

It's not possible to obtain a sum of 5 using the given coins.

Constraints:

• $(1 \leq \text{sum} \times \text{coins.size()} \leq 10^6)$

• $(0 \leq \text{sum} \leq 10^4)$

• $(1 \leq \text{coins}[i] \leq 10^4)$

• $(1 \leq \text{coins.size()} \leq 10^3)$

🎯 My Approach:

Optimized Dynamic Programming

Algorithm Steps:

1. Define dp[j] as the minimum number of coins needed to obtain sum = j.

2. Base Case: dp[0] = 0 (zero sum requires zero coins).

3. Transition:

   • For each coin, update dp[j] for all j >= coin using: $[ dp[j] = \min(dp[j], dp[j - coin] + 1) $]

4. Final Check: If dp[sum] == ∞, return -1 (sum cannot be formed).

🕒 Time and Auxiliary Space Complexity

• Expected Time Complexity: O(N × sum), as we iterate over each coin and process all sums up to sum.

• Expected Auxiliary Space Complexity: O(sum), as we maintain a 1D DP array of size sum + 1.

📝 Solution Code

Code (C++)

class Solution {
  public:
    int minCoins(vector<int>& coins, int sum) {
        vector<int> dp(sum + 1, INT_MAX);
        dp[0] = 0;
        for (int c : coins)
            for (int j = c; j <= sum; j++)
                if (dp[j - c] != INT_MAX)
                    dp[j] = min(dp[j], dp[j - c] + 1);
        return dp[sum] == INT_MAX ? -1 : dp[sum];
    }
};

⚡ Alternative Approaches

2️⃣ Dynamic Programming (O(N×sum) Time, O(N×sum) Space) — 2D DP

Algorithm Steps:

1. Use a 2D DP table where dp[i][j] represents the minimum coins needed to make sum j using the first i coins.

2. Base Case:

   • dp[i][0] = 0 for all i (zero sum requires zero coins).

   • dp[0][j] = ∞ for all j > 0 (zero coins can't form positive sum).

3. Recurrence Relation: $[ dp[i][j] = \min(dp[i-1][j], 1 + dp[i][j - coins[i-1]]) $]

   • Exclude the coin (dp[i-1][j]).

   • Include the coin (dp[i][j - coins[i-1]] + 1).

class Solution {
  public:
    int minCoins(vector<int>& coins, int sum) {
        int n = coins.size();
        vector<vector<int>> dp(n + 1, vector<int>(sum + 1, INT_MAX));
        for (int i = 0; i <= n; i++) dp[i][0] = 0;
        for (int i = 1; i <= n; i++) {
            for (int j = 1; j <= sum; j++) {
                dp[i][j] = dp[i - 1][j];
                if (j >= coins[i - 1] && dp[i][j - coins[i - 1]] != INT_MAX)
                    dp[i][j] = min(dp[i][j], dp[i][j - coins[i - 1]] + 1);
            }
        }
        return dp[n][sum] == INT_MAX ? -1 : dp[n][sum];
    }
};

✅ Time Complexity: O(N × sum) ✅ Space Complexity: O(N × sum)

3️⃣ Recursive + Memoization (O(N×sum) Time, O(N×sum) Space)

Algorithm Steps:

1. Recursive function minCoins(index, sum) calculates the minimum coins needed using coins up to index.

2. Base Case:

   • If sum == 0, return 0 (no coins needed).

   • If index < 0 or sum < 0, return ∞ (not possible).

3. Recurrence Relation: $[ minCoins(index, sum) = \min(minCoins(index - 1, sum), 1 + minCoins(index, sum - coins[index]]) $]

   • Exclude the current coin.

   • Include the current coin.

4. Use memoization (dp[index][sum]) to avoid redundant calculations.

class Solution {
  public:
    vector<vector<int>> dp;
    int solve(vector<int>& coins, int i, int sum) {
        if (sum == 0) return 0;
        if (i < 0 || sum < 0) return INT_MAX;
        if (dp[i][sum] != -1) return dp[i][sum];
        int exclude = solve(coins, i - 1, sum);
        int include = solve(coins, i, sum - coins[i]);
        if (include != INT_MAX) include += 1;
        return dp[i][sum] = min(exclude, include);
    }

    int minCoins(vector<int>& coins, int sum) {
        int n = coins.size();
        dp.assign(n, vector<int>(sum + 1, -1));
        int res = solve(coins, n - 1, sum);
        return res == INT_MAX ? -1 : res;
    }
};

✅ Time Complexity: O(N × sum) ✅ Space Complexity: O(N × sum)

Comparison of Approaches

Approach	⏱️ Time Complexity	🗂️ Space Complexity	✅ Pros	⚠️ Cons
1D Space Optimized DP	🟡 O(N × sum)	🟢 O(sum)	Most efficient space-wise	Requires careful indexing
2D DP (Tabulation)	🟡 O(N × sum)	🔴 O(N × sum)	Easy to implement, intuitive	High space usage
Recursive + Memoization	🟡 O(N × sum)	🔴 O(N × sum)	Natural recursion flow	Stack overhead

✅ Best Choice?

• If optimizing space: Use 1D DP (Space-Optimized).

• If space is not a concern: Use 2D DP (Tabulation) for easy understanding.

• For recursion lovers: Use Recursive + Memoization.

Code (Java)

class Solution {
    public int minCoins(int[] coins, int sum) {
        int[] dp = new int[sum + 1];
        Arrays.fill(dp, Integer.MAX_VALUE);
        dp[0] = 0;
        for (int c : coins)
            for (int j = c; j <= sum; j++)
                if (dp[j - c] != Integer.MAX_VALUE)
                    dp[j] = Math.min(dp[j], dp[j - c] + 1);
        return dp[sum] == Integer.MAX_VALUE ? -1 : dp[sum];
    }
}

Code (Python)

class Solution:
    def minCoins(self, coins, sum):
        dp = [float('inf')] * (sum + 1)
        dp[0] = 0
        for c in coins:
            for j in range(c, sum + 1):
                dp[j] = min(dp[j], dp[j - c] + 1)
        return -1 if dp[sum] == float('inf') else dp[sum]

🎯 Contribution and Support:

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