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<h1 id="24">2.4. &nbsp; 小结<a class="headerlink" href="#24" title="Permanent link">&para;</a></h1>
<p><strong>算法效率评估</strong></p>
<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>
<p><strong>时间复杂度</strong></p>
<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>
<p><strong>空间复杂度</strong></p>
<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="241-q-a">2.4.1. &nbsp; Q &amp; A<a class="headerlink" href="#241-q-a" title="Permanent link">&para;</a></h2>
<div class="admonition question">
<p class="admonition-title">尾递归的空间复杂度是 <span class="arithmatex">\(O(1)\)</span> 吗?</p>
<p>理论上,尾递归函数的空间复杂度可以被优化至 <span class="arithmatex">\(O(1)\)</span> 。不过绝大多数编程语言(例如 Java, Python, C++, Go, C# 等)
都不支持自动优化尾递归,因此一般来说空间复杂度是 <span class="arithmatex">\(O(n)\)</span></p>
</div>
<div class="admonition question">
<p class="admonition-title">函数和方法这两个术语的区别是什么?</p>
<p>函数function可以独立被执行所有参数都以显式传递。
方法method与一个对象关联方法被隐式传递给调用它的对象方法能够对类的实例中包含的数据进行操作。</p>
<p>因此C 和 Go 只有函数Java 和 C# 只有方法,在 C++, Python 中取决于它是否属于一个类。</p>
</div>
<div class="admonition question">
<p class="admonition-title">图片“空间复杂度的常见类型”反映的是否是占用空间的绝对大小?</p>
<p>不是,该图片展示的是空间复杂度(即增长趋势),而不是占用空间的绝对大小。每个曲线都包含一个常数项,用来把所有曲线的取值范围压缩到一个视觉舒适的范围内。
实际中,因为我们通常不知道每个方法的“常数项”复杂度是多少,所以一般无法仅凭复杂度来选择 <span class="arithmatex">\(n = 8\)</span> 之下的最优解法;但相对地 <span class="arithmatex">\(n = 8^5\)</span> 就很好选了,这是复杂度占主导的情况。</p>
</div>
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