FAANMG | DP-1

[sandy7907]

No description provided.

[super30admin]

Your solution is well-structured and uses dynamic programming correctly. The recurrence relation is appropriate for the unbounded knapsack nature of the problem. However, there are a few areas for improvement:

1. Magic Number: Avoid using magic numbers like 99999. Instead, use a value that is derived from the problem constraints. For example, you can initialize the unreachable states to amount + 1 (since the maximum number of coins needed cannot exceed amount). This makes the code more readable and robust.

2. Space Optimization: The current solution uses O(n * amount) space, which is acceptable given the constraints, but can be optimized to O(amount) by using a 1D DP array. This is a common optimization for the coin change problem and reduces the space complexity without affecting the time complexity.

3. Edge Case Handling: Although your code handles amount=0 correctly (returning 0), it is good practice to explicitly check for such edge cases at the beginning for clarity.

4. Variable Naming: While r and c are concise, using more descriptive names like nCoins and nAmount might improve readability.

Here is a revised version of your code with these improvements:

class CoinChange {
    public int coinChange(int[] coins, int amount) {
        if (amount == 0) return 0;
        int n = coins.length;
        int[] dp = new int[amount + 1];
        Arrays.fill(dp, amount + 1);
        dp[0] = 0;
        
        for (int coin : coins) {
            for (int j = coin; j <= amount; j++) {
                dp[j] = Math.min(dp[j], 1 + dp[j - coin]);
            }
        }
        
        return dp[amount] > amount ? -1 : dp[amount];
    }
}

This version uses a 1D array, initializes with amount+1, and iterates over coins and then amounts. Note that the inner loop starts from the coin value to avoid negative indices.

Your current solution is correct and efficient, but with these changes, it becomes more robust and efficient in space.