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第 6 章 &nbsp; 哈希表
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第 7 章 &nbsp;
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10.2 &nbsp; 二分查找插入点
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10.3 &nbsp; 二分查找边界
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10.4 &nbsp; 哈希优化策略
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第 11 章 &nbsp; 排序
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11.2 &nbsp; 选择排序
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11.4 &nbsp; 插入排序
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11.7 &nbsp; 堆排序
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11.8 &nbsp; 桶排序
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11.9 &nbsp; 计数排序
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11.10 &nbsp; 基数排序
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第 12 章 &nbsp; 分治
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12.1 &nbsp; 分治算法
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第 15 章 &nbsp; 贪心
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第 16 章 &nbsp; 附录
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><!--! Font Awesome Free 6.5.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M441 58.9 453.1 71c9.4 9.4 9.4 24.6 0 33.9L424 134.1 377.9 88 407 58.9c9.4-9.4 24.6-9.4 33.9 0zM209.8 256.2 344 121.9l46.1 46.1-134.3 134.2c-2.9 2.9-6.5 5-10.4 6.1L186.9 325l16.7-58.5c1.1-3.9 3.2-7.5 6.1-10.4zM373.1 25 175.8 222.2c-8.7 8.7-15 19.4-18.3 31.1l-28.6 100c-2.4 8.4-.1 17.4 6.1 23.6s15.2 8.5 23.6 6.1l100-28.6c11.8-3.4 22.5-9.7 31.1-18.3L487 138.9c28.1-28.1 28.1-73.7 0-101.8L474.9 25c-28.1-28.1-73.7-28.1-101.8 0zM88 64c-48.6 0-88 39.4-88 88v272c0 48.6 39.4 88 88 88h272c48.6 0 88-39.4 88-88V312c0-13.3-10.7-24-24-24s-24 10.7-24 24v112c0 22.1-17.9 40-40 40H88c-22.1 0-40-17.9-40-40V152c0-22.1 17.9-40 40-40h112c13.3 0 24-10.7 24-24s-10.7-24-24-24H88z"/></svg>
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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">1. &nbsp; 重点回顾<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
<p><strong>算法效率评估</strong></p>
<ul>
<li>时间效率和空间效率是衡量算法优劣的两个主要评价指标。</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>复杂度分析可以消除实际测试的弊端,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,如在输入的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,对应函数渐近上界,反映当 <span class="arithmatex">\(n\)</span> 趋向正无穷时,操作数量 <span class="arithmatex">\(T(n)\)</span> 的增长级别。</li>
<li>推算时间复杂度分为两步,首先统计操作数量,然后判断渐近上界。</li>
<li>常见时间复杂度从低到高排列有 <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>空间复杂度的作用类似于时间复杂度,用于衡量算法占用内存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,输入空间不纳入空间复杂度计算。暂存空间可分为暂存数据、栈帧空间和指令空间,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刻下的空间复杂度。</li>
<li>常见空间复杂度从低到高排列有 <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>
<h3 id="2-q-a">2. &nbsp; Q &amp; A<a class="headerlink" href="#2-q-a" title="Permanent link">&para;</a></h3>
<p><strong>Q</strong>: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)不支持自动优化尾递归,因此通常认为空间复杂度是 <span class="arithmatex">\(O(n)\)</span></p>
<p><strong>Q</strong>:函数和方法这两个术语的区别是什么?</p>
<p>「函数 function」可以被独立执行所有参数都以显式传递。「方法 method」与一个对象关联被隐式传递给调用它的对象能够对类的实例中包含的数据进行操作。</p>
<p>下面以几种常见的编程语言为例来说明。</p>
<ul>
<li>C 语言是过程式编程语言没有面向对象的概念所以只有函数。但我们可以通过创建结构体struct来模拟面向对象编程与结构体相关联的函数就相当于其他编程语言中的方法。</li>
<li>Java 和 C# 是面向对象的编程语言,代码块(方法)通常作为某个类的一部分。静态方法的行为类似于函数,因为它被绑定在类上,不能访问特定的实例变量。</li>
<li>C++ 和 Python 既支持过程式编程(函数),也支持面向对象编程(方法)。</li>
</ul>
<p><strong>Q</strong>:图解“常见的空间复杂度类型”反映的是否是占用空间的绝对大小?</p>
<p>不是,该图展示的是空间复杂度,其反映的是增长趋势,而不是占用空间的绝对大小。</p>
<p>假设取 <span class="arithmatex">\(n = 8\)</span> ,你可能会发现每条曲线的值与函数对应不上。这是因为每条曲线都包含一个常数项,用于将取值范围压缩到一个视觉舒适的范围内。</p>
<p>在实际中,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。但对于 <span class="arithmatex">\(n = 8^5\)</span> 就很好选了,这时增长趋势已经占主导了。</p>
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