C++：分别使用贪心算法和动态规划算法写出代码和写出算法伪码：有n个集装箱要装上一搜重量为w的轮船，其中集装箱i(1<=i<=n)的重量为w，不考虑集装箱限制，现在要尽可能多的集装箱装上轮船，使得它们的重量之和不超过w

首先，让我们先看贪心算法的伪代码和C++代码。贪心策略是每次都选择当前剩余容量能容纳的最大重量的集装箱。

**贪心算法伪代码：**

function greedyCargoDistribution(weights, shipWeight):
    sorted_weights = sort(weights, descending order)
    total_weight = 0
    result = []
    
    for weight in sorted_weights:
        if total_weight + weight <= shipWeight:
            total_weight += weight
            result.append(weight)

    return result

**C++代码实现：**

#include <vector>
#include <algorithm>

std::vector<int> greedyCargoDistribution(std::vector<int>& weights, int shipWeight) {
    std::sort(weights.rbegin(), weights.rend()); // 从大到小排序
    int total_weight = 0;
    std::vector<int> result;

    for (int weight : weights) {
        if (total_weight + weight <= shipWeight) {
            total_weight += weight;
            result.push_back(weight);
        }
    }

    return result;
}

然后，我们来看动态规划算法。这里我们可以创建一个二维数组dp，表示前i个箱子能分配的最大总重量，对于每个箱子，我们需要判断它是否应该加入到当前组合中以最大化总重量。

**动态规划算法伪代码：**

function dpCargoDistribution(weights, shipWeight):
    dp = array of zeros with size (n+1) x (shipWeight+1)
    dp[0][0] = 0
    
    for i from 1 to n:
        for j from 0 to shipWeight:
            if weights[i-1] <= j:
                dp[i][j] = max(dp[i-1][j], dp[i-1][j-weights[i-1]] + weights[i-1])
            else:
                dp[i][j] = dp[i-1][j]

    return dp[n][shipWeight]

**C++代码实现：**

#include <vector>

std::vector<int> dpCargoDistribution(std::vector<int>& weights, int shipWeight) {
    std::vector<std::vector<int>> dp(weights.size() + 1, std::vector<int>(shipWeight + 1, 0));
    dp[0][0] = 0;

    for (size_t i = 1; i <= weights.size(); ++i) {
        for (size_t j = 0; j <= shipWeight; ++j) {
            if (weights[i - 1] <= j) {
                dp[i][j] = std::max(dp[i - 1][j], dp[i - 1][j - weights[i - 1]] + weights[i - 1]);
            } else {
                dp[i][j] = dp[i - 1][j];
            }
        }
    }

    return {dp[weights.size()][shipWeight]};
}