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算法效率评估
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时间复杂度
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空间复杂度
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<h1 id="25">2.5. &nbsp; 小结<a class="headerlink" href="#25" title="Permanent link">&para;</a></h1>
<h3 id="_1">算法效率评估<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h3>
<ul>
<li>时间效率和空间效率是评价算法性能的两个关键维度。</li>
<li>我们可以通过实际测试来评估算法效率,但难以消除测试环境的影响,且会耗费大量计算资源。</li>
<li>复杂度分析可以克服实际测试的弊端,分析结果适用于所有运行平台,并且能够揭示算法在不同数据规模下的效率。</li>
</ul>
<h3 id="_2">时间复杂度<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h3>
<ul>
<li>时间复杂度用于衡量算法运行时间随数据量增长的趋势,可以有效评估算法效率,但在某些情况下可能失效,如在输入数据量较小或时间复杂度相同时,无法精确对比算法效率的优劣。</li>
<li>最差时间复杂度使用大 <span class="arithmatex">\(O\)</span> 符号表示,即函数渐近上界,反映当 <span class="arithmatex">\(n\)</span> 趋向正无穷时,<span class="arithmatex">\(T(n)\)</span> 的增长级别。</li>
<li>推算时间复杂度分为两步,首先统计计算操作数量,然后判断渐近上界。</li>
<li>常见时间复杂度从小到大排列有 <span class="arithmatex">\(O(1)\)</span> , <span class="arithmatex">\(O(\log n)\)</span> , <span class="arithmatex">\(O(n)\)</span> , <span class="arithmatex">\(O(n \log n)\)</span> , <span class="arithmatex">\(O(n^2)\)</span> , <span class="arithmatex">\(O(2^n)\)</span> , <span class="arithmatex">\(O(n!)\)</span> 等。</li>
<li>某些算法的时间复杂度非固定,而是与输入数据的分布有关。时间复杂度分为最差、最佳、平均时间复杂度,最佳时间复杂度几乎不用,因为输入数据一般需要满足严格条件才能达到最佳情况。</li>
<li>平均时间复杂度反映算法在随机数据输入下的运行效率,最接近实际应用中的算法性能。计算平均时间复杂度需要统计输入数据分布以及综合后的数学期望。</li>
</ul>
<h3 id="_3">空间复杂度<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h3>
<ul>
<li>类似于时间复杂度,空间复杂度用于衡量算法占用空间随数据量增长的趋势。</li>
<li>算法运行过程中的相关内存空间可分为输入空间、暂存空间、输出空间。通常情况下,输入空间不计入空间复杂度计算。暂存空间可分为指令空间、数据空间、栈帧空间,其中栈帧空间通常仅在递归函数中影响空间复杂度。</li>
<li>我们通常只关注最差空间复杂度,即统计算法在最差输入数据和最差运行时间点下的空间复杂度。</li>
<li>常见空间复杂度从小到大排列有 <span class="arithmatex">\(O(1)\)</span> , <span class="arithmatex">\(O(\log n)\)</span> , <span class="arithmatex">\(O(n)\)</span> , <span class="arithmatex">\(O(n^2)\)</span> , <span class="arithmatex">\(O(2^n)\)</span> 等。</li>
</ul>
<h2 id="__comments">评论</h2>
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