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Java-based LeetCode algorithm problem solutions, regularly updated

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**Time Complexity (Big O Time):**

1. The program uses a recursive function, `combinationSumRec`, to generate combinations. In the worst case, it explores all possible combinations.

2. For each coin, it has two choices: either include the coin in the combination or exclude it. This results in a binary tree-like structure for the recursive calls, where each level of the tree represents a different coin and has two branches for including or excluding the coin.

3. In the worst case, the binary tree has a height of n (the number of coins). This means that the program makes 2^n recursive calls.

4. In each recursive call, the program performs constant-time operations to add or remove elements from the `subList`, which can be considered O(1).

5. Therefore, the overall time complexity is O(2^n), where n is the number of coins.

**Space Complexity (Big O Space):**

1. The space complexity of the program is determined by the space used for the call stack during the recursion and the space used for the `subList` and `ans` list.

2. The maximum depth of the recursion is n (the number of coins), which means the space used for the call stack is O(n).

3. The `subList` stores the current combination, and its size is bounded by the number of coins. In the worst case, it can have n elements.

4. The `ans` list stores all valid combinations, and its size is determined by the number of valid combinations. In the worst case, it can also have a size of 2^n, considering all possible combinations.

5. Therefore, the space complexity of the program is O(n + 2^n), where n is the number of coins.

In summary, the time complexity of the provided program is O(2^n), and the space complexity is O(n + 2^n), where n is the number of coins.