hello-algo/en/docs/chapter_greedy/fractional_knapsack_problem.md

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# 15.2   Fractional knapsack problem

!!! question

    Given $n$ items, the weight of the $i$-th item is $wgt[i-1]$ and its value is $val[i-1]$, and a knapsack with a capacity of $cap$. Each item can be chosen only once, **but a part of the item can be selected, with its value calculated based on the proportion of the weight chosen**, what is the maximum value of the items in the knapsack under the limited capacity? An example is shown in Figure 15-3.

![Example data of the fractional knapsack problem](fractional_knapsack_problem.assets/fractional_knapsack_example.png){ class="animation-figure" }

<p align="center"> Figure 15-3 &nbsp; Example data of the fractional knapsack problem </p>

The fractional knapsack problem is very similar overall to the 0-1 knapsack problem, involving the current item $i$ and capacity $c$, aiming to maximize the value within the limited capacity of the knapsack.

The difference is that, in this problem, only a part of an item can be chosen. As shown in Figure 15-4, **we can arbitrarily split the items and calculate the corresponding value based on the weight proportion**.

1. For item $i$, its value per unit weight is $val[i-1] / wgt[i-1]$, referred to as the unit value.
2. Suppose we put a part of item $i$ with weight $w$ into the knapsack, then the value added to the knapsack is $w \times val[i-1] / wgt[i-1]$.

![Value per unit weight of the item](fractional_knapsack_problem.assets/fractional_knapsack_unit_value.png){ class="animation-figure" }

<p align="center"> Figure 15-4 &nbsp; Value per unit weight of the item </p>

### 1. &nbsp; Greedy strategy determination

Maximizing the total value of the items in the knapsack **essentially means maximizing the value per unit weight**. From this, the greedy strategy shown in Figure 15-5 can be deduced.

1. Sort the items by their unit value from high to low.
2. Iterate over all items, **greedily choosing the item with the highest unit value in each round**.
3. If the remaining capacity of the knapsack is insufficient, use part of the current item to fill the knapsack.

![Greedy strategy of the fractional knapsack problem](fractional_knapsack_problem.assets/fractional_knapsack_greedy_strategy.png){ class="animation-figure" }

<p align="center"> Figure 15-5 &nbsp; Greedy strategy of the fractional knapsack problem </p>

### 2. &nbsp; Code implementation

We have created an `Item` class in order to sort the items by their unit value. We loop and make greedy choices until the knapsack is full, then exit and return the solution:

=== "Python"

    ```python title="fractional_knapsack.py"
    class Item:
        """Item"""

        def __init__(self, w: int, v: int):
            self.w = w  # Item weight
            self.v = v  # Item value

    def fractional_knapsack(wgt: list[int], val: list[int], cap: int) -> int:
        """Fractional knapsack: Greedy"""
        # Create an item list, containing two properties: weight, value
        items = [Item(w, v) for w, v in zip(wgt, val)]
        # Sort by unit value item.v / item.w from high to low
        items.sort(key=lambda item: item.v / item.w, reverse=True)
        # Loop for greedy selection
        res = 0
        for item in items:
            if item.w <= cap:
                # If the remaining capacity is sufficient, put the entire item into the knapsack
                res += item.v
                cap -= item.w
            else:
                # If the remaining capacity is insufficient, put part of the item into the knapsack
                res += (item.v / item.w) * cap
                # No remaining capacity left, thus break the loop
                break
        return res
    ```

=== "C++"

    ```cpp title="fractional_knapsack.cpp"
    /* Item */
    class Item {
      public:
        int w; // Item weight
        int v; // Item value

        Item(int w, int v) : w(w), v(v) {
        }
    };

    /* Fractional knapsack: Greedy */
    double fractionalKnapsack(vector<int> &wgt, vector<int> &val, int cap) {
        // Create an item list, containing two properties: weight, value
        vector<Item> items;
        for (int i = 0; i < wgt.size(); i++) {
            items.push_back(Item(wgt[i], val[i]));
        }
        // Sort by unit value item.v / item.w from high to low
        sort(items.begin(), items.end(), [](Item &a, Item &b) { return (double)a.v / a.w > (double)b.v / b.w; });
        // Loop for greedy selection
        double res = 0;
        for (auto &item : items) {
            if (item.w <= cap) {
                // If the remaining capacity is sufficient, put the entire item into the knapsack
                res += item.v;
                cap -= item.w;
            } else {
                // If the remaining capacity is insufficient, put part of the item into the knapsack
                res += (double)item.v / item.w * cap;
                // No remaining capacity left, thus break the loop
                break;
            }
        }
        return res;
    }
    ```

=== "Java"

    ```java title="fractional_knapsack.java"
    /* Item */
    class Item {
        int w; // Item weight
        int v; // Item value

        public Item(int w, int v) {
            this.w = w;
            this.v = v;
        }
    }

    /* Fractional knapsack: Greedy */
    double fractionalKnapsack(int[] wgt, int[] val, int cap) {
        // Create an item list, containing two properties: weight, value
        Item[] items = new Item[wgt.length];
        for (int i = 0; i < wgt.length; i++) {
            items[i] = new Item(wgt[i], val[i]);
        }
        // Sort by unit value item.v / item.w from high to low
        Arrays.sort(items, Comparator.comparingDouble(item -> -((double) item.v / item.w)));
        // Loop for greedy selection
        double res = 0;
        for (Item item : items) {
            if (item.w <= cap) {
                // If the remaining capacity is sufficient, put the entire item into the knapsack
                res += item.v;
                cap -= item.w;
            } else {
                // If the remaining capacity is insufficient, put part of the item into the knapsack
                res += (double) item.v / item.w * cap;
                // No remaining capacity left, thus break the loop
                break;
            }
        }
        return res;
    }
    ```

=== "C#"

    ```csharp title="fractional_knapsack.cs"
    [class]{Item}-[func]{}

    [class]{fractional_knapsack}-[func]{FractionalKnapsack}
    ```

=== "Go"

    ```go title="fractional_knapsack.go"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "Swift"

    ```swift title="fractional_knapsack.swift"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "JS"

    ```javascript title="fractional_knapsack.js"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "TS"

    ```typescript title="fractional_knapsack.ts"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "Dart"

    ```dart title="fractional_knapsack.dart"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "Rust"

    ```rust title="fractional_knapsack.rs"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractional_knapsack}
    ```

=== "C"

    ```c title="fractional_knapsack.c"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "Kotlin"

    ```kotlin title="fractional_knapsack.kt"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

=== "Ruby"

    ```ruby title="fractional_knapsack.rb"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractional_knapsack}
    ```

=== "Zig"

    ```zig title="fractional_knapsack.zig"
    [class]{Item}-[func]{}

    [class]{}-[func]{fractionalKnapsack}
    ```

Apart from sorting, in the worst case, the entire list of items needs to be traversed, **hence the time complexity is $O(n)$**, where $n$ is the number of items.

Since an `Item` object list is initialized, **the space complexity is $O(n)$**.

### 3. &nbsp; Correctness proof

Using proof by contradiction. Suppose item $x$ has the highest unit value, and some algorithm yields a maximum value `res`, but the solution does not include item $x$.

Now remove a unit weight of any item from the knapsack and replace it with a unit weight of item $x$. Since the unit value of item $x$ is the highest, the total value after replacement will definitely be greater than `res`. **This contradicts the assumption that `res` is the optimal solution, proving that the optimal solution must include item $x$**.

For other items in this solution, we can also construct the above contradiction. Overall, **items with greater unit value are always better choices**, proving that the greedy strategy is effective.

As shown in Figure 15-6, if the item weight and unit value are viewed as the horizontal and vertical axes of a two-dimensional chart respectively, the fractional knapsack problem can be transformed into "seeking the largest area enclosed within a limited horizontal axis range". This analogy can help us understand the effectiveness of the greedy strategy from a geometric perspective.

![Geometric representation of the fractional knapsack problem](fractional_knapsack_problem.assets/fractional_knapsack_area_chart.png){ class="animation-figure" }

<p align="center"> Figure 15-6 &nbsp; Geometric representation of the fractional knapsack problem </p>