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chapter_computational_complexity/time_complexity/index.html
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@@ -5883,7 +5883,7 @@ O(1) < O(\log n) < O(n) < O(n \log n) < O(n^2) < O(2^n) < O(n!
</div>
<p>对数阶常出现于基于分治策略的算法中，体现了“一分为多”和“化繁为简”的算法思想。它增长缓慢，是仅次于常数阶的理想的时间复杂度。</p>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p class="admonition-title"><span class="arithmatex">\(O(\log n)\)</span> 的底数是多少？</p>
<p>准确来说，“一分为 <span class="arithmatex">\(m\)</span>”对应的时间复杂度是 <span class="arithmatex">\(O(\log_m n)\)</span> 。而通过对数换底公式，我们可以得到具有不同底数的、相等的时间复杂度：</p>
<div class="arithmatex">\[
O(\log_m n) = O(\log_k n / \log_k m) = O(\log_k n)