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第 8 章 &nbsp; 堆積
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第 9 章 &nbsp;
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9.2 &nbsp; 圖基礎操作
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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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11.2 &nbsp; 選擇排序
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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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11.10 &nbsp; 基數排序
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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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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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<h1 id="25">2.5 &nbsp; 小結<a class="headerlink" href="#25" title="Permanent link">&para;</a></h1>
<h3 id="1">1. &nbsp; 重點回顧<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
<p><strong>演算法效率評估</strong></p>
<ul>
<li>時間效率和空間效率是衡量演算法優劣的兩個主要評價指標。</li>
<li>我們可以透過實際測試來評估演算法效率,但難以消除測試環境的影響,且會耗費大量計算資源。</li>
<li>複雜度分析可以消除實際測試的弊端,分析結果適用於所有執行平臺,並且能夠揭示演算法在不同資料規模下的效率。</li>
</ul>
<p><strong>時間複雜度</strong></p>
<ul>
<li>時間複雜度用於衡量演算法執行時間隨資料量增長的趨勢,可以有效評估演算法效率,但在某些情況下可能失效,如在輸入的資料量較小或時間複雜度相同時,無法精確對比演算法效率的優劣。</li>
<li>最差時間複雜度使用大 <span class="arithmatex">\(O\)</span> 符號表示,對應函式漸近上界,反映當 <span class="arithmatex">\(n\)</span> 趨向正無窮時,操作數量 <span class="arithmatex">\(T(n)\)</span> 的增長級別。</li>
<li>推算時間複雜度分為兩步,首先統計操作數量,然後判斷漸近上界。</li>
<li>常見時間複雜度從低到高排列有 <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>某些演算法的時間複雜度非固定,而是與輸入資料的分佈有關。時間複雜度分為最差、最佳、平均時間複雜度,最佳時間複雜度幾乎不用,因為輸入資料一般需要滿足嚴格條件才能達到最佳情況。</li>
<li>平均時間複雜度反映演算法在隨機資料輸入下的執行效率,最接近實際應用中的演算法效能。計算平均時間複雜度需要統計輸入資料分佈以及綜合後的數學期望。</li>
</ul>
<p><strong>空間複雜度</strong></p>
<ul>
<li>空間複雜度的作用類似於時間複雜度,用於衡量演算法佔用記憶體空間隨資料量增長的趨勢。</li>
<li>演算法執行過程中的相關記憶體空間可分為輸入空間、暫存空間、輸出空間。通常情況下,輸入空間不納入空間複雜度計算。暫存空間可分為暫存資料、堆疊幀空間和指令空間,其中堆疊幀空間通常僅在遞迴函式中影響空間複雜度。</li>
<li>我們通常只關注最差空間複雜度,即統計演算法在最差輸入資料和最差執行時刻下的空間複雜度。</li>
<li>常見空間複雜度從低到高排列有 <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>:尾遞迴的空間複雜度是 <span class="arithmatex">\(O(1)\)</span> 嗎?</p>
<p>理論上,尾遞迴函式的空間複雜度可以最佳化至 <span class="arithmatex">\(O(1)\)</span> 。不過絕大多數程式語言(例如 Java、Python、C++、Go、C# 等)不支持自動最佳化尾遞迴,因此通常認為空間複雜度是 <span class="arithmatex">\(O(n)\)</span></p>
<p><strong>Q</strong>:函式和方法這兩個術語的區別是什麼?</p>
<p><u>函式function</u>可以被獨立執行,所有參數都以顯式傳遞。<u>方法method</u>與一個物件關聯,被隱式傳遞給呼叫它的物件,能夠對類別的例項中包含的資料進行操作。</p>
<p>下面以幾種常見的程式語言為例來說明。</p>
<ul>
<li>C 語言是程序式程式設計語言沒有物件導向的概念所以只有函式。但我們可以透過建立結構體struct來模擬物件導向程式設計與結構體相關聯的函式就相當於其他程式語言中的方法。</li>
<li>Java 和 C# 是物件導向的程式語言,程式碼塊(方法)通常作為某個類別的一部分。靜態方法的行為類似於函式,因為它被繫結在類別上,不能訪問特定的例項變數。</li>
<li>C++ 和 Python 既支持程序式程式設計(函式),也支持物件導向程式設計(方法)。</li>
</ul>
<p><strong>Q</strong>:圖解“常見的空間複雜度型別”反映的是否是佔用空間的絕對大小?</p>
<p>不是,該圖展示的是空間複雜度,其反映的是增長趨勢,而不是佔用空間的絕對大小。</p>
<p>假設取 <span class="arithmatex">\(n = 8\)</span> ,你可能會發現每條曲線的值與函式對應不上。這是因為每條曲線都包含一個常數項,用於將取值範圍壓縮到一個視覺舒適的範圍內。</p>
<p>在實際中,因為我們通常不知道每個方法的“常數項”複雜度是多少,所以一般無法僅憑複雜度來選擇 <span class="arithmatex">\(n = 8\)</span> 之下的最優解法。但對於 <span class="arithmatex">\(n = 8^5\)</span> 就很好選了,這時增長趨勢已經佔主導了。</p>
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