剖析 gh_mirrors/leet 项目：字符串重排题解的算法优化逻辑（836）

# 🔍 剖析 gh_mirrors/leet 项目：字符串重排题解的算法优化逻辑

在 gh_mirrors/leet 项目中，字符串重排问题是一个经典算法挑战，其优化过程展现了算法设计的精妙之处。让我们深入分析其优化逻辑！💡

## 🧩 问题本质
字符串重排问题要求判断两个字符串是否能通过字符重新排列变成彼此。最直观的解法是排序后比较，时间复杂度为 O(n log n)。但项目中的优化方案将其降到了 O(n)！🚀

## 🔑 优化核心：哈希计数法
项目采用了哈希表统计字符出现次数的策略：
1️⃣ 遍历第一个字符串，记录每个字符出现次数
2️⃣ 遍历第二个字符串，减少对应字符计数
3️⃣ 检查所有计数是否归零

```python
def is_anagram(s: str, t: str) -> bool:
  if len(s) != len(t):
    return False
  count = [0] * 26 # 假设只包含小写字母
  for char in s:
    count[ord(char) - ord('a')] += 1
  for char in t:
    count[ord(char) - ord('a')] -= 1
    if count[ord(char) - ord('a')] < 0:
      return False
  return True
```

## ⚡ 优化亮点
- **空间换时间**：使用固定大小的数组替代哈希表，减少哈希计算开销
- **提前终止**：在第二个遍历中一旦发现负数立即返回，避免完整遍历
- **边界处理**：先比较长度，快速排除明显不匹配的情况

## 🎯 性能对比
| 方法 | 时间复杂度 | 空间复杂度 |
|------|------------|------------|
| 排序法 | O(n log n) | O(1)    |
| 哈希法 | O(n)    | O(1)    |

这种优化思路在字符串处理类问题中具有普适性，值得掌握！🌟 理解这种空间换时间的权衡，是算法优化的重要思维模式。
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