[LeetCode-455]Assign Cookies

本文介绍了一种解决分配饼干问题的方法,目标是最大化满足孩子的数量。通过排序和双指针技巧,实现高效匹配饼干与孩子的贪婪程度。

Assume you are an awesome parent and want to give your children some cookies. But, you should give each child at most one cookie. Each child i has a greed factor gi, which is the minimum size of a cookie that the child will be content with; and each cookie j has a size sj. If sj >= gi, we can assign the cookie j to the child i, and the child i will be content. Your goal is to maximize the number of your content children and output the maximum number.

Note:
You may assume the greed factor is always positive.
You cannot assign more than one cookie to one child.

Example 1:
Input: [1,2,3], [1,1]

Output: 1

Explanation: You have 3 children and 2 cookies. The greed factors of 3 children are 1, 2, 3.
And even though you have 2 cookies, since their size is both 1, you could only make the child whose greed factor is 1 content.
You need to output 1.
Example 2:
Input: [1,2], [1,2,3]

Output: 2

Explanation: You have 2 children and 3 cookies. The greed factors of 2 children are 1, 2.
You have 3 cookies and their sizes are big enough to gratify all of the children,
You need to output 2.

思路:首先将两个数组从小到大排序,设s数组中的最大元素为MaxS,然后在贪婪数组(g数组)中找到最后一个小于MaxS的元素的下标,记为x。

定义两个指针endg,ends分别指向g[x]和s[s.length - 1],比较g[endg]与s[ends]的大小:
若g[endg] <= s[ends],则结果 + 1,endg,ends分别前移一个单位;
若g[endg] > s[ends],则当前孩子不能满足,则endg向前移动一个单位;
条件是 endg >= 0 && ends >=0

public class AssignCookies455 {
    public static int findContentChildren(int[] g, int[] s) {
        if (g == null || s == null || g.length == 0 || s.length == 0)
            return 0;

        Arrays.sort(g);
        Arrays.sort(s);
        int glen = g.length, slen = s.length;
        int maxS = s[slen - 1];
        int begin = 0, end = glen;
        while (begin < end) {
            int mid = begin + ((end - begin) >> 1);
            if (g[mid] > maxS + 1) {
                end = mid;
            } else {
                begin = mid + 1;
            }
        }

        int ends = slen - 1, endg = begin - 1, res = 0;
        while (ends >= 0 && endg >= 0) {
            if (g[endg] <= s[ends]) {
                res++;
                ends--;
            }

            endg--;
        }

        return res;
    }
}
### LeetCode 刷题推荐列表与学习路径 在 LeetCode 上进行刷题时,制定一个合理的计划非常重要。以下是一个基于算法分类的学习路径和推荐题目列表[^1]: #### 学习路径 1. **基础算法理论** 在开始刷题之前,建议先通过视频或书籍了解基本的算法理论。例如,分治法、贪心算法、动态规划、二叉搜索树(BST)、图等概念[^1]。 2. **数据结构基础** 熟悉常见的数据结构,包括数组、链表、栈、队列、哈希表、树、图等。确保对这些数据结构的操作有深刻理解。 3. **分模块刷题** 按照以下顺序逐步深入: - 树:从简单的遍历问题(如前序、中序、后序遍历)开始,逐渐过渡到复杂问题(如二叉搜索树验证、平衡二叉树等)。 - 图与回溯算法:学习图的表示方法(邻接矩阵、邻接表),并练习深度优先搜索(DFS)和广度优先搜索(BFS)。结合回溯算法解决组合问题、排列问题等。 - 贪心算法:选择一些经典的贪心问题(如活动选择问题、区间覆盖问题)进行练习。 - 动态规划:从简单的 DP 问题(如爬楼梯、斐波那契数列)入手,逐步掌握状态转移方程的设计技巧。 4. **刷题策略** 刷题时优先选择简单或中等难度的题目,并关注通过率较高的题目。这有助于建立信心并巩固基础知识[^1]。 #### 推荐题目列表 以下是按算法分类的 LeetCode 题目推荐列表: 1. **树** - [104. 二叉树的最大深度](https://leetcode-cn.com/problems/maximum-depth-of-binary-tree/) - [94. 二叉树的中序遍历](https://leetcode-cn.com/problems/binary-tree-inorder-traversal/) - [236. 二叉树的最近公共祖先](https://leetcode-cn.com/problems/lowest-common-ancestor-of-a-binary-search-tree/) 2. **图与回溯** - [79. 单词搜索](https://leetcode-cn.com/problems/word-search/) - [51. N皇后](https://leetcode-cn.com/problems/n-queens/) - [78. 子集](https://leetcode-cn.com/problems/subsets/) 3. **贪心** - [455. 分发饼干](https://leetcode-cn.com/problems/assign-cookies/) - [135. 分发糖果](https://leetcode-cn.com/problems/candy/) - [406. 根据身高重建队列](https://leetcode-cn.com/problems/queue-reconstruction-by-height/) 4. **动态规划** - [70. 爬楼梯](https://leetcode-cn.com/problems/climbing-stairs/) - [53. 最大子数组和](https://leetcode-cn.com/problems/maximum-subarray/) - [300. 最长递增子序列](https://leetcode-cn.com/problems/longest-increasing-subsequence/) #### 示例代码 以下是一个简单的动态规划问题示例——“不同路径”[^3]: ```python def uniquePaths(m, n): dp = [[1] * n for _ in range(m)] for i in range(1, m): for j in range(1, n): dp[i][j] = dp[i-1][j] + dp[i][j-1] return dp[-1][-1] # 测试用例 print(uniquePaths(3, 2)) # 输出:3 ``` ###
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