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chapter_computational_complexity/time_complexity/index.html
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T(n) = 3 + 2n
\]</div>
<p><span class="arithmatex">\(T(n)\)</span> 是一次函数，说明其运行时间的增长趋势是线性的，因此它的时间复杂度是线性阶。</p>
<p>我们将线性阶的时间复杂度记为 <span class="arithmatex">\(O(n)\)</span> ，这个数学符号称为<u>大（<span class="arithmatex">\(O\)</span> 记号 big-<span class="arithmatex">\(O\)</span> notation）</u>，表示函数 <span class="arithmatex">\(T(n)\)</span> 的<u>渐近上界（asymptotic upper bound）</u>。</p>
<p>我们将线性阶的时间复杂度记为 <span class="arithmatex">\(O(n)\)</span> ，这个数学符号称为<u>大 <span class="arithmatex">\(O\)</span> 记号（big-<span class="arithmatex">\(O\)</span> notation）</u>，表示函数 <span class="arithmatex">\(T(n)\)</span> 的<u>渐近上界（asymptotic upper bound）</u>。</p>
<p>时间复杂度分析本质上是计算“操作数量 <span class="arithmatex">\(T(n)\)</span>”的渐近上界，它具有明确的数学定义。</p>
<div class="admonition abstract">
<p class="admonition-title">函数渐近上界</p>