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<h1 id="64">6.4 &nbsp; 小结<a class="headerlink" href="#64" 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>
<ul>
<li>输入 <code>key</code> ,哈希表能够在 <span class="arithmatex">\(O(1)\)</span> 时间内查询到 <code>value</code> ,效率非常高。</li>
<li>常见的哈希表操作包括查询、添加键值对、删除键值对和遍历哈希表等。</li>
<li>哈希函数将 <code>key</code> 映射为数组索引,从而访问对应桶并获取 <code>value</code></li>
<li>两个不同的 <code>key</code> 可能在经过哈希函数后得到相同的数组索引,导致查询结果出错,这种现象被称为哈希冲突。</li>
<li>哈希表容量越大,哈希冲突的概率就越低。因此可以通过扩容哈希表来缓解哈希冲突。与数组扩容类似,哈希表扩容操作的开销很大。</li>
<li>负载因子定义为哈希表中元素数量除以桶数量,反映了哈希冲突的严重程度,常用作触发哈希表扩容的条件。</li>
<li>链式地址通过将单个元素转化为链表,将所有冲突元素存储在同一个链表中。然而,链表过长会降低查询效率,可以进一步将链表转换为红黑树来提高效率。</li>
<li>开放寻址通过多次探测来处理哈希冲突。线性探测使用固定步长,缺点是不能删除元素,且容易产生聚集。多次哈希使用多个哈希函数进行探测,相较线性探测更不易产生聚集,但多个哈希函数增加了计算量。</li>
<li>不同编程语言采取了不同的哈希表实现。例如Java 的 <code>HashMap</code> 使用链式地址,而 Python 的 <code>Dict</code> 采用开放寻址。</li>
<li>在哈希表中,我们希望哈希算法具有确定性、高效率和均匀分布的特点。在密码学中,哈希算法还应该具备抗碰撞性和雪崩效应。</li>
<li>哈希算法通常采用大质数作为模数,以最大化地保证哈希值的均匀分布,减少哈希冲突。</li>
<li>常见的哈希算法包括 MD5、SHA-1、SHA-2 和 SHA3 等。MD5 常用于校验文件完整性SHA-2 常用于安全应用与协议。</li>
<li>编程语言通常会为数据类型提供内置哈希算法,用于计算哈希表中的桶索引。通常情况下,只有不可变对象是可哈希的。</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>
<div class="admonition question">
<p class="admonition-title">哈希表的时间复杂度为什么不是 <span class="arithmatex">\(O(n)\)</span> </p>
<p>当哈希冲突比较严重时,哈希表的时间复杂度会退化至 <span class="arithmatex">\(O(n)\)</span> 。当哈希函数设计的比较好、容量设置比较合理、冲突比较平均时,时间复杂度是 <span class="arithmatex">\(O(1)\)</span> 。我们使用编程语言内置的哈希表时,通常认为时间复杂度是 <span class="arithmatex">\(O(1)\)</span></p>
</div>
<div class="admonition question">
<p class="admonition-title">为什么不使用哈希函数 <span class="arithmatex">\(f(x) = x\)</span> 呢?这样就不会有冲突了</p>
<p><span class="arithmatex">\(f(x) = x\)</span> 哈希函数下,每个元素对应唯一的桶索引,这与数组等价。然而,输入空间通常远大于输出空间(数组长度),因此哈希函数的最后一步往往是对数组长度取模。换句话说,哈希表的目标是将一个较大的状态空间映射到一个较小的空间,并提供 <span class="arithmatex">\(O(1)\)</span> 的查询效率。</p>
</div>
<div class="admonition question">
<p class="admonition-title">哈希表底层实现是数组、链表、二叉树,但为什么效率可以比他们更高呢?</p>
<p>首先,哈希表的时间效率变高,但空间效率变低了。哈希表有相当一部分的内存是未使用的,</p>
<p>其次,只是在特定使用场景下时间效率变高了。如果一个功能能够在相同的时间复杂度下使用数组或链表实现,那么通常比哈希表更快。这是因为哈希函数计算需要开销,时间复杂度的常数项更大。</p>
<p>最后,哈希表的时间复杂度可能发生劣化。例如在链式地址中,我们采取在链表或红黑树中执行查找操作,仍然有退化至 <span class="arithmatex">\(O(n)\)</span> 时间的风险。</p>
</div>
<div class="admonition question">
<p class="admonition-title">多次哈希有不能直接删除元素的缺陷吗?对于标记已删除的空间,这个空间还能再次使用吗?</p>
<p>多次哈希是开放寻址的一种,开放寻址法都有不能直接删除元素的缺陷,需要通过标记删除。被标记为已删除的空间是可以再次被使用的。当将新元素插入哈希表,并且通过哈希函数找到了被标记为已删除的位置时,该位置可以被新的元素使用。这样做既能保持哈希表的探测序列不变,又能保证哈希表的空间使用率。</p>
</div>
<div class="admonition question">
<p class="admonition-title">为什么在线性探测中,查找元素的时候会出现哈希冲突呢?</p>
<p>查找的时候通过哈希函数找到对应的桶和键值对,发现 <code>key</code> 不匹配,这就代表有哈希冲突。因此,线性探测法会根据预先设定的步长依次向下查找,直至找到正确的键值对或无法找到跳出为止。</p>
</div>
<div class="admonition question">
<p class="admonition-title">为什么哈希表扩容能够缓解哈希冲突?</p>
<p>哈希函数的最后一步往往是对数组长度 <span class="arithmatex">\(n\)</span> 取余,让输出值落入在数组索引范围;在扩容后,数组长度 <span class="arithmatex">\(n\)</span> 发生变化,而 <code>key</code> 对应的索引也可能发生变化。原先落在同一个桶的多个 <code>key</code> ,在扩容后可能会被分配到多个桶中,从而实现哈希冲突的缓解。</p>
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