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chapter_array_and_linkedlist/summary/index.html
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<li>缓存不友好：由于数据不是连续存放的，因此 <code>std::list</code> 对缓存的利用率较低。一般情况下，<code>std::vector</code> 的性能会更好。</li> <li>缓存不友好：由于数据不是连续存放的，因此 <code>std::list</code> 对缓存的利用率较低。一般情况下，<code>std::vector</code> 的性能会更好。</li>
</ul> </ul>
<p>另一方面，必要使用链表的情况主要是二叉树和图。栈和队列往往会使用编程语言提供的 <code>stack</code> 和 <code>queue</code> ，而非链表。</p> <p>另一方面，必要使用链表的情况主要是二叉树和图。栈和队列往往会使用编程语言提供的 <code>stack</code> 和 <code>queue</code> ，而非链表。</p>
<p><strong>Q</strong>：初始化列表 <code>res = [0] * self.size()</code> 操作，会导致 <code>res</code> 的每个元素引用相同的地址吗？</p> <p><strong>Q</strong>：操作 <code>res = [[0]] * n</code> 生成了一个二维列表，其中每一个 <code>[0]</code> 都是独立的吗？</p>
<p>不会。但二维数组会有这个问题，例如初始化二维列表 <code>res = [[0]] * self.size()</code> ，则多次引用了同一个列表 <code>[0]</code> 。</p> <p>不是独立的。此二维列表中，所有的 <code>[0]</code> 实际上是同一个对象的引用。如果我们修改其中一个元素，会发现所有的对应元素都会随之改变。</p>
<p>如果希望二维列表中的每个 <code>[0]</code> 都是独立的，可以使用 <code>res = [[0] for _ in range(n)]</code> 来实现。这种方式的原理是初始化了 <span class="arithmatex">\(n\)</span> 个独立的 <code>[0]</code> 列表对象。</p>
<p><strong>Q</strong>：操作 <code>res = [0] * n</code> 生成了一个列表，其中每一个整数 0 都是独立的吗？</p>
<p>在该列表中，所有整数 0 都是同一个对象的引用。这是因为 Python 对小整数（通常是 -5 到 256）采用了缓存池机制，以便最大化对象复用，从而提升性能。</p>
<p>虽然它们指向同一个对象，但我们仍然可以独立修改列表中的每个元素，这是因为 Python 的整数是“不可变对象”。当我们修改某个元素时，实际上是切换为另一个对象的引用，而不是改变原有对象本身。</p>
<p>然而，当列表元素是“可变对象”时（例如列表、字典或类实例等），修改某个元素会直接改变该对象本身，所有引用该对象的元素都会产生相同变化。</p>
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<li>整理扑克的过程与插入排序算法非常类似。插入排序算法适合排序小型数据集。</li> <li>整理扑克的过程与插入排序算法非常类似。插入排序算法适合排序小型数据集。</li>
<li>货币找零的步骤本质上是贪心算法，每一步都采取当前看来最好的选择。</li> <li>货币找零的步骤本质上是贪心算法，每一步都采取当前看来最好的选择。</li>
<li>算法是在有限时间内解决特定问题的一组指令或操作步骤，而数据结构是计算机中组织和存储数据的方式。</li> <li>算法是在有限时间内解决特定问题的一组指令或操作步骤，而数据结构是计算机中组织和存储数据的方式。</li>
<li>数据结构与算法紧密相连。数据结构是算法的基石，而算法是数据结构发挥作用的舞台。</li> <li>数据结构与算法紧密相连。数据结构是算法的基石，而算法为数据结构注入生命力。</li>
<li>我们可以将数据结构与算法类比为拼装积木，积木代表数据，积木的形状和连接方式等代表数据结构，拼装积木的步骤则对应算法。</li> <li>我们可以将数据结构与算法类比为拼装积木，积木代表数据，积木的形状和连接方式等代表数据结构，拼装积木的步骤则对应算法。</li>
</ul> </ul>
<h3 id="1-q-a">1. &nbsp; Q &amp; A<a class="headerlink" href="#1-q-a" title="Permanent link">&para;</a></h3> <h3 id="1-q-a">1. &nbsp; Q &amp; A<a class="headerlink" href="#1-q-a" title="Permanent link">&para;</a></h3>

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<p>如图 1-4 所示，数据结构与算法高度相关、紧密结合，具体表现在以下三个方面。</p> <p>如图 1-4 所示，数据结构与算法高度相关、紧密结合，具体表现在以下三个方面。</p>
<ul> <ul>
<li>数据结构是算法的基石。数据结构为算法提供了结构化存储的数据，以及操作数据的方法。</li> <li>数据结构是算法的基石。数据结构为算法提供了结构化存储的数据，以及操作数据的方法。</li>
<li>算法是数据结构发挥作用的舞台。数据结构本身仅存储数据信息，结合算法才能解决特定问题。</li> <li>算法为数据结构注入生命力。数据结构本身仅存储数据信息，结合算法才能解决特定问题。</li>
<li>算法通常可以基于不同的数据结构实现，但执行效率可能相差很大，选择合适的数据结构是关键。</li> <li>算法通常可以基于不同的数据结构实现，但执行效率可能相差很大，选择合适的数据结构是关键。</li>
</ul> </ul>
<p><a class="glightbox" href="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="数据结构与算法的关系" class="animation-figure" src="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" /></a></p> <p><a class="glightbox" href="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="数据结构与算法的关系" class="animation-figure" src="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" /></a></p>

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<li>快取不友好：由於資料不是連續存放的，因此 <code>std::list</code> 對快取的利用率較低。一般情況下，<code>std::vector</code> 的效能會更好。</li> <li>快取不友好：由於資料不是連續存放的，因此 <code>std::list</code> 對快取的利用率較低。一般情況下，<code>std::vector</code> 的效能會更好。</li>
</ul> </ul>
<p>另一方面，必要使用鏈結串列的情況主要是二元樹和圖。堆疊和佇列往往會使用程式語言提供的 <code>stack</code> 和 <code>queue</code> ，而非鏈結串列。</p> <p>另一方面，必要使用鏈結串列的情況主要是二元樹和圖。堆疊和佇列往往會使用程式語言提供的 <code>stack</code> 和 <code>queue</code> ，而非鏈結串列。</p>
<p><strong>Q</strong>：初始化串列 <code>res = [0] * self.size()</code> 操作，會導致 <code>res</code> 的每個元素引用相同的位址嗎？</p> <p><strong>Q</strong>：操作 <code>res = [[0]] * n</code> 生成了一個二維串列，其中每一個 <code>[0]</code> 都是獨立的嗎？</p>
<p>不會。但二維陣列會有這個問題，例如初始化二維串列 <code>res = [[0]] * self.size()</code> ，則多次引用了同一個串列 <code>[0]</code> 。</p> <p>不是獨立的。此二維串列中，所有的 <code>[0]</code> 實際上是同一個物件的引用。如果我們修改其中一個元素，會發現所有的對應元素都會隨之改變。</p>
<p>如果希望二維串列中的每個 <code>[0]</code> 都是獨立的，可以使用 <code>res = [[0] for _ in range(n)]</code> 來實現。這種方式的原理是初始化了 <span class="arithmatex">\(n\)</span> 個獨立的 <code>[0]</code> 串列物件。</p>
<p><strong>Q</strong>：操作 <code>res = [0] * n</code> 生成了一個串列，其中每一個整數 0 都是獨立的嗎？</p>
<p>在該串列中，所有整數 0 都是同一個物件的引用。這是因為 Python 對小整數（通常是 -5 到 256）採用了快取池機制，以便最大化物件複用，從而提升效能。</p>
<p>雖然它們指向同一個物件，但我們仍然可以獨立修改串列中的每個元素，這是因為 Python 的整數是“不可變物件”。當我們修改某個元素時，實際上是切換為另一個物件的引用，而不是改變原有物件本身。</p>
<p>然而，當串列元素是“可變物件”時（例如串列、字典或類別例項等），修改某個元素會直接改變該物件本身，所有引用該物件的元素都會產生相同變化。</p>
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<li>整理撲克的過程與插入排序演算法非常類似。插入排序演算法適合排序小型資料集。</li> <li>整理撲克的過程與插入排序演算法非常類似。插入排序演算法適合排序小型資料集。</li>
<li>貨幣找零的步驟本質上是貪婪演算法，每一步都採取當前看來最好的選擇。</li> <li>貨幣找零的步驟本質上是貪婪演算法，每一步都採取當前看來最好的選擇。</li>
<li>演算法是在有限時間內解決特定問題的一組指令或操作步驟，而資料結構是計算機中組織和儲存資料的方式。</li> <li>演算法是在有限時間內解決特定問題的一組指令或操作步驟，而資料結構是計算機中組織和儲存資料的方式。</li>
<li>資料結構與演算法緊密相連。資料結構是演算法的基石，而演算法是資料結構發揮作用的舞臺。</li> <li>資料結構與演算法緊密相連。資料結構是演算法的基石，而演算法為資料結構注入生命力。</li>
<li>我們可以將資料結構與演算法類比為拼裝積木，積木代表資料，積木的形狀和連線方式等代表資料結構，拼裝積木的步驟則對應演算法。</li> <li>我們可以將資料結構與演算法類比為拼裝積木，積木代表資料，積木的形狀和連線方式等代表資料結構，拼裝積木的步驟則對應演算法。</li>
</ul> </ul>
<h3 id="1-q-a">1. &nbsp; Q &amp; A<a class="headerlink" href="#1-q-a" title="Permanent link">&para;</a></h3> <h3 id="1-q-a">1. &nbsp; Q &amp; A<a class="headerlink" href="#1-q-a" title="Permanent link">&para;</a></h3>

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<p>如圖 1-4 所示，資料結構與演算法高度相關、緊密結合，具體表現在以下三個方面。</p> <p>如圖 1-4 所示，資料結構與演算法高度相關、緊密結合，具體表現在以下三個方面。</p>
<ul> <ul>
<li>資料結構是演算法的基石。資料結構為演算法提供了結構化儲存的資料，以及操作資料的方法。</li> <li>資料結構是演算法的基石。資料結構為演算法提供了結構化儲存的資料，以及操作資料的方法。</li>
<li>演算法是資料結構發揮作用的舞臺。資料結構本身僅儲存資料資訊，結合演算法才能解決特定問題。</li> <li>演算法為資料結構注入生命力。資料結構本身僅儲存資料資訊，結合演算法才能解決特定問題。</li>
<li>演算法通常可以基於不同的資料結構實現，但執行效率可能相差很大，選擇合適的資料結構是關鍵。</li> <li>演算法通常可以基於不同的資料結構實現，但執行效率可能相差很大，選擇合適的資料結構是關鍵。</li>
</ul> </ul>
<p><a class="glightbox" href="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="資料結構與演算法的關係" class="animation-figure" src="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" /></a></p> <p><a class="glightbox" href="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="資料結構與演算法的關係" class="animation-figure" src="../what_is_dsa.assets/relationship_between_data_structure_and_algorithm.png" /></a></p>

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