hello-algo/chapter_computational_complexity/performance_evaluation/index.html
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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<h1 id="_1">算法效率评估<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h1>
<h2 id="_2">算法评价维度<a class="headerlink" href="#_2" title="Permanent link">&para;</a></h2>
<p>在开始学习算法之前,我们首先要想清楚算法的设计目标是什么,或者说,如何来评判算法的好与坏。整体上看,我们设计算法时追求两个层面的目标。</p>
<ol>
<li><strong>找到问题解法。</strong> 算法需要能够在规定的输入范围下,可靠地求得问题的正确解。</li>
<li><strong>寻求最优解法。</strong> 同一个问题可能存在多种解法,而我们希望算法效率尽可能的高。</li>
</ol>
<p>换言之,在可以解决问题的前提下,算法效率则是主要评价维度,包括:</p>
<ul>
<li><strong>时间效率</strong> ,即算法的运行速度的快慢。</li>
<li><strong>空间效率</strong> ,即算法占用的内存空间大小。</li>
</ul>
<p>数据结构与算法追求 “运行得快、内存占用少” ,而如何去评价算法效率则是非常重要的问题。</p>
<h2 id="_3">效率评估方法<a class="headerlink" href="#_3" title="Permanent link">&para;</a></h2>
<h3 id="_4">实际测试<a class="headerlink" href="#_4" title="Permanent link">&para;</a></h3>
<p>假设我们现在有算法 A 和 算法 B ,都能够解决同一问题,现在需要对比两个算法之间的效率。我们能够想到的最直接的方式,就是找一台计算机,把两个算法都完整跑一遍,并监控记录运行时间和内存占用情况。这种评估方式能够反映真实情况,但是也存在很大的硬伤。</p>
<p><strong>难以排除测试环境的干扰因素。</strong> 硬件配置会影响到算法的性能表现。例如,在某台计算机中,算法 A 比算法 B 运行时间更短;但换到另一台配置不同的计算机中,可能会得到相反的测试结果。这意味着我们需要在各种机器上展开测试,而这是不现实的。</p>
<p><strong>展开完整测试非常耗费资源。</strong> 随着输入数据量的大小变化,算法会呈现出不同的效率表现。比如,有可能输入数据量较小时,算法 A 运行时间短于算法 B ,而在输入数据量较大时,测试结果截然相反。因此,若想要达到具有说服力的对比结果,那么需要输入各种体量数据,这样的测试需要占用大量计算资源。</p>
<h3 id="_5">理论估算<a class="headerlink" href="#_5" title="Permanent link">&para;</a></h3>
<p>既然实际测试具有很大的局限性,那么我们是否可以仅通过一些计算,就获知算法的效率水平呢?答案是肯定的,我们将此估算方法称为「复杂度分析 Complexity Analysis」或「渐近复杂度分析 Asymptotic Complexity Analysis」。</p>
<p><strong>复杂度分析评估随着输入数据量的增长,算法的运行时间和占用空间的增长趋势</strong> 。根据时间和空间两方面,复杂度可分为「时间复杂度 Time Complexity」和「空间复杂度 Space Complexity」。</p>
<p><strong>复杂度分析克服了实际测试方法的弊端。</strong> 一是独立于测试环境,分析结果适用于所有运行平台。二是可以体现不同数据量下的算法效率,尤其是可以反映大数据量下的算法性能。</p>
<h2 id="_6">复杂度分析的重要性<a class="headerlink" href="#_6" title="Permanent link">&para;</a></h2>
<p>复杂度分析给出一把评价算法效率的 “标尺” ,告诉我们执行某个算法需要多少时间和空间资源,也让我们可以开展不同算法之间的效率对比。</p>
<p>计算复杂度是个数学概念,对于初学者可能比较抽象,学习难度相对较高。从这个角度出发,其并不适合作为第一章内容。但是,当我们讨论某个数据结构或者算法的特点时,难以避免需要分析它的运行速度和空间使用情况。<strong>因此,在展开学习数据结构与算法之前,建议读者先对计算复杂度建立起初步的了解,并且能够完成简单案例的复杂度分析</strong></p>
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