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让关于“最差时间复杂度”的描述更加清晰和准确 #1774

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让关于“最差时间复杂度”的描述更加清晰和准确 #1774

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2 changes: 1 addition & 1 deletion docs/chapter_computational_complexity/time_complexity.md
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Expand Up @@ -1211,7 +1211,7 @@ $$
[file]{worst_best_time_complexity}-[class]{}-[func]{find_one}
```

值得说明的是，我们在实际中很少使用最佳时间复杂度，因为通常只有在很小概率下才能达到，可能会带来一定的误导性。**而最差时间复杂度更为实用，因为它给出了一个效率安全值**，让我们可以放心地使用算法。
值得说明的是，我们在实际中很少使用最佳时间复杂度，因为通常只有在很小概率下才能达到，可能会带来一定的误导性。**而最差时间复杂度更为实用，因为它给出了算法运行时间的上界保证**，让我们可以放心地使用算法。

从上述示例可以看出，最差时间复杂度和最佳时间复杂度只出现于“特殊的数据分布”，这些情况的出现概率可能很小，并不能真实地反映算法运行效率。相比之下，**平均时间复杂度可以体现算法在随机输入数据下的运行效率**，用 $\Theta$ 记号来表示。

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