Adding approach change

exercises/practice/change/.approaches/config.json

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+{
+  "introduction": {
+    "authors": [
+      "jagdish-15"
+    ]
+  },
+  "approaches": [
+    {
+      "uuid": "d0b615ca-3a02-4d66-ad10-e0c513062189",
+      "slug": "dynamic-programming",
+      "title": "Dynamic Programming Approach",
+      "blurb": "Use dynamic programming to find the most efficient change combination.",
+      "authors": [
+        "jagdish-15"
+      ],
+      "contributors": [
+        "kagoh"
+      ]
+    }
+  ]
+}

exercises/practice/change/.approaches/dynamic-programming/content.md

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+# Dynamic Programming Approach
+
+The **Dynamic Programming (DP)** approach is an efficient way to solve the problem of making change for a given total using a list of available coin denominations. It minimizes the number of coins needed by breaking down the problem into smaller subproblems and solving them progressively.
+
+This approach ensures that we find the most efficient way to make change and handles edge cases where no solution exists.
+
+## Explanation
+
+1. **Initialize Coins Usage Tracker**:
+
+   - We create a list `coinsUsed`, where each index `i` stores the most efficient combination of coins that sum up to the value `i`.
+   - The list is initialized with an empty list at index `0`, as no coins are needed to achieve a total of zero.
+
+2. **Iterative Dynamic Programming**:
+
+   - For each value `i` from 1 to `grandTotal`, we explore all available coin denominations to find the best combination that can achieve the total `i`.
+   - For each coin, we check if it can be part of the solution (i.e., if `coin <= i` and `coinsUsed[i - coin]` is a valid combination).
+   - If so, we generate a new combination by adding the current coin to the solution for `i - coin`. We then compare the size of this new combination with the existing best combination and keep the one with fewer coins.
+
+3. **Result**:
+
+   - After processing all values up to `grandTotal`, the combination at `coinsUsed[grandTotal]` will represent the most efficient solution.
+   - If no valid combination exists for `grandTotal`, an exception is thrown.
+
+```java
+import java.util.List;
+import java.util.ArrayList;
+
+class ChangeCalculator {
+    private final List<Integer> currencyCoins;
+
+    ChangeCalculator(List<Integer> currencyCoins) {
+        this.currencyCoins = currencyCoins;
+    }
+
+    List<Integer> computeMostEfficientChange(int grandTotal) {
+        if (grandTotal < 0) 
+            throw new IllegalArgumentException("Negative totals are not allowed.");
+
+        List<List<Integer>> coinsUsed = new ArrayList<>(grandTotal + 1);
+        coinsUsed.add(new ArrayList<Integer>());
+
+        for (int i = 1; i <= grandTotal; i++) {
+            List<Integer> bestCombination = null;
+            for (int coin: currencyCoins) {
+                if (coin <= i && coinsUsed.get(i - coin) != null) {
+                    List<Integer> currentCombination = new ArrayList<>(coinsUsed.get(i - coin));
+                    currentCombination.add(0, coin);
+                    if (bestCombination == null || currentCombination.size() < bestCombination.size())
+                        bestCombination = currentCombination;
+                }
+            }
+            coinsUsed.add(bestCombination);
+        }
+
+        if (coinsUsed.get(grandTotal) == null)
+            throw new IllegalArgumentException("The total " + grandTotal + " cannot be represented in the given currency.");
+
+        return coinsUsed.get(grandTotal);
+    }
+}
+```
+
+## Key Points
+
+- **Time Complexity**: The time complexity of this approach is **O(n * m)**, where `n` is the `grandTotal` and `m` is the number of available coin denominations. This is because we iterate over all coin denominations for each amount up to `grandTotal`.
+
+- **Space Complexity**: The space complexity is **O(n)** due to the list `coinsUsed`, which stores the most efficient coin combination for each total up to `grandTotal`.
+
+- **Edge Cases**:
+
+  - If the `grandTotal` is negative, an exception is thrown immediately.
+  - If there is no way to make the exact total with the given denominations, an exception is thrown with a descriptive message.
+
+## Trade-offs and Considerations
+
+- **Efficiency**: This approach is highly efficient in terms of minimizing the number of coins, but it might require significant memory for larger `grandTotal` values, as the space complexity grows linearly with `grandTotal`.
+
+- **Alternative Approaches**:
+
+  - A **Greedy Approach** could be faster for some cases but does not always guarantee the minimum number of coins.
+  - This dynamic programming approach is best when the goal is to guarantee the fewest coins possible, especially when no simple greedy solution exists.
+
+## Conclusion
+
+The dynamic programming approach provides an optimal solution for the change-making problem, ensuring that we minimize the number of coins used while efficiently solving the problem for any `grandTotal`. However, it’s essential to consider the trade-offs in terms of memory usage and the time complexity when dealing with very large inputs.

exercises/practice/change/.approaches/dynamic-programming/snippet.txt

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+class ChangeCalculator {
+    private final List<Integer> currencyCoins;
+
+    ChangeCalculator(List<Integer> currencyCoins) {
+        this.currencyCoins = currencyCoins;
+    }
+    // computeMostEfficientChange method
+}

exercises/practice/change/.approaches/introduction.md

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+# Introduction to Change Calculator
+
+In the "Change Calculator" exercise, the goal is to determine the minimum number of coins needed to reach a given total using a specific set of coin denominations. This is a classic problem in dynamic programming, where efficient change-making is essential, especially when there are constraints on coin types or large totals.
+
+## Problem Overview
+
+Given:
+
+- A list of coin denominations, each representing an available currency unit.
+- A total amount (`grandTotal`) we want to reach using the fewest possible coins from the given denominations.
+
+The solution should find the optimal combination of coins to match the total. If it's impossible to match the total exactly, the solution should indicate this by throwing an exception.
+
+## Approach Overview
+
+Our solution uses a **dynamic programming approach**, where we systematically build up the optimal combinations for all totals from `0` up to the target amount (`grandTotal`). For each total, we track the fewest coins needed to make that total, reusing previous results to make the solution efficient.
+
+This approach ensures that we find the minimum number of coins required in a structured, repeatable way, avoiding the need for complex recursive calls or excessive backtracking.
+
+## Key Features of the Approach
+
+- **Efficiency**: By building solutions for each increment up to `grandTotal`, this approach minimizes redundant calculations.
+- **Flexibility**: Handles cases where exact change is impossible by checking at each step.
+- **Scalability**: Works for various coin denominations and totals, though large inputs may impact performance.
+
+For a detailed look at the code and logic, see the full explanation in the [approach file](https://exercism.org/tracks/java/exercises/change/approaches/dynamic-programming).