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分数背包问题

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from bisect import bisect
from itertools import accumulate


def frac_knapsack(vl, wt, w, n):
    """
    >>> frac_knapsack([60, 100, 120], [10, 20, 30], 50, 3)
    240.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 10, 4)
    105.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 4)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6], 8, 4)
    60.0
    >>> frac_knapsack([10, 40, 30], [5, 4, 6, 3], 8, 4)
    60.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 0, 4)
    0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 0)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], -8, 4)
    0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, -4)
    95.0
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 800, 4)
    130
    >>> frac_knapsack([10, 40, 30, 50], [5, 4, 6, 3], 8, 400)
    95.0
    >>> frac_knapsack("ABCD", [5, 4, 6, 3], 8, 400)
    Traceback (most recent call last):
        ...
    TypeError: unsupported operand type(s) for /: 'str' and 'int'
    """

    r = sorted(zip(vl, wt), key=lambda x: x[0] / x[1], reverse=True)
    vl, wt = [i[0] for i in r], [i[1] for i in r]
    acc = list(accumulate(wt))
    k = bisect(acc, w)
    return (
        0
        if k == 0
        else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k])
    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
关于此算法

问题陈述

给定一组物品,每个物品都有重量和价值,确定每个物品包含在集合中的数量,以便总重量小于或等于给定的限制,并且总价值尽可能大。

贪心算法总是可以为分数背包问题提供最优解。

时间复杂度

O(nlog n) 最坏情况

示例

Lets assume the capacity of knapsack, W = 60
value = [280, 100, 120, 120]  
weight = [40, 10, 20, 24]

Ratio(V/W) = 7,10,6,5
Say those items as A,B,C,D
next, the items should be sorted in descending order based on the ratio of value by weight to get maximum profit
First and foremost, B was picked since its weight is smaller than the knapsack's capacity. The next item, A, is chosen since the knapsack's available capacity is more than A's weight. C is now the next item on the list. However, the entire item cannot be chosen because the knapsack's remaining capacity is less than C's weight.
As a result, the C proportion (6050)/20)
The knapsack's capacity is now equal to the specified items.
As a result, no more items can be chosen.

10 + 40 + 20*(10/20) = 60 is the total weight of the chosen goods.

100+280+120*(10/20)=380+60=440 is the total profit.

This is the most suitable option.

We won't be able to make more money by combining diverse things.

视频讲解

一个 CS50 视频解释贪心算法