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7.1 &nbsp; 二元樹
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7.2 &nbsp; 二元樹走訪
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7.3 &nbsp; 二元樹陣列表示
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7.4 &nbsp; 二元搜尋樹
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7.5 &nbsp; AVL *
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第 8 章 &nbsp; 堆積
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第 9 章 &nbsp;
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第 9 章 &nbsp;
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9.2 &nbsp; 圖基礎操作
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9.3 &nbsp; 圖的走訪
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第 10 章 &nbsp; 搜尋
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10.2 &nbsp; 二分搜尋插入點
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10.3 &nbsp; 二分搜尋邊界
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10.4 &nbsp; 雜湊最佳化策略
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10.5 &nbsp; 重識搜尋演算法
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第 11 章 &nbsp; 排序
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11.1 &nbsp; 排序演算法
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11.2 &nbsp; 選擇排序
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11.3 &nbsp; 泡沫排序
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11.4 &nbsp; 插入排序
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11.6 &nbsp; 合併排序
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11.7 &nbsp; 堆積排序
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11.8 &nbsp; 桶排序
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11.9 &nbsp; 計數排序
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11.10 &nbsp; 基數排序
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11.11 &nbsp; 小結
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第 12 章 &nbsp; 分治
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12.1 &nbsp; 分治演算法
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12.3 &nbsp; 構建樹問題
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第 13 章 &nbsp; 回溯
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第 14 章 &nbsp; 動態規劃
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14.1 &nbsp; 初探動態規劃
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14.2 &nbsp; DP 問題特性
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14.3 &nbsp; DP 解題思路
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<h1 id="12">1.2 &nbsp; 演算法是什麼<a class="headerlink" href="#12" title="Permanent link">&para;</a></h1>
<h2 id="121">1.2.1 &nbsp; 演算法定義<a class="headerlink" href="#121" title="Permanent link">&para;</a></h2>
<p><u>演算法algorithm</u>是在有限時間內解決特定問題的一組指令或操作步驟,它具有以下特性。</p>
<ul>
<li>問題是明確的,包含清晰的輸入和輸出定義。</li>
<li>具有可行性,能夠在有限步驟、時間和記憶體空間下完成。</li>
<li>各步驟都有確定的含義,在相同的輸入和執行條件下,輸出始終相同。</li>
</ul>
<h2 id="122">1.2.2 &nbsp; 資料結構定義<a class="headerlink" href="#122" title="Permanent link">&para;</a></h2>
<p><u>資料結構data structure</u>是計算機中組織和儲存資料的方式,具有以下設計目標。</p>
<ul>
<li>空間佔用儘量少,以節省計算機記憶體。</li>
<li>資料操作儘可能快速,涵蓋資料訪問、新增、刪除、更新等。</li>
<li>提供簡潔的資料表示和邏輯資訊,以便演算法高效執行。</li>
</ul>
<p><strong>資料結構設計是一個充滿權衡的過程</strong>。如果想在某方面取得提升,往往需要在另一方面作出妥協。下面舉兩個例子。</p>
<ul>
<li>鏈結串列相較於陣列,在資料新增和刪除操作上更加便捷,但犧牲了資料訪問速度。</li>
<li>圖相較於鏈結串列,提供了更豐富的邏輯資訊,但需要佔用更大的記憶體空間。</li>
</ul>
<h2 id="123">1.2.3 &nbsp; 資料結構與演算法的關係<a class="headerlink" href="#123" title="Permanent link">&para;</a></h2>
<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>
<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>
<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 align="center"> 圖 1-4 &nbsp; 資料結構與演算法的關係 </p>
<p>資料結構與演算法猶如圖 1-5 所示的拼裝積木。一套積木,除了包含許多零件之外,還附有詳細的組裝說明書。我們按照說明書一步步操作,就能組裝出精美的積木模型。</p>
<p><a class="glightbox" href="../what_is_dsa.assets/assembling_blocks.png" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="拼裝積木" class="animation-figure" src="../what_is_dsa.assets/assembling_blocks.png" /></a></p>
<p align="center"> 圖 1-5 &nbsp; 拼裝積木 </p>
<p>兩者的詳細對應關係如表 1-1 所示。</p>
<p align="center"> 表 1-1 &nbsp; 將資料結構與演算法類比為拼裝積木 </p>
<div class="center-table">
<table>
<thead>
<tr>
<th>資料結構與演算法</th>
<th>拼裝積木</th>
</tr>
</thead>
<tbody>
<tr>
<td>輸入資料</td>
<td>未拼裝的積木</td>
</tr>
<tr>
<td>資料結構</td>
<td>積木組織形式,包括形狀、大小、連線方式等</td>
</tr>
<tr>
<td>演算法</td>
<td>把積木拼成目標形態的一系列操作步驟</td>
</tr>
<tr>
<td>輸出資料</td>
<td>積木模型</td>
</tr>
</tbody>
</table>
</div>
<p>值得說明的是,資料結構與演算法是獨立於程式語言的。正因如此,本書得以提供基於多種程式語言的實現。</p>
<div class="admonition tip">
<p class="admonition-title">約定俗成的簡稱</p>
<p>在實際討論時,我們通常會將“資料結構與演算法”簡稱為“演算法”。比如眾所周知的 LeetCode 演算法題目,實際上同時考查資料結構和演算法兩方面的知識。</p>
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