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<h1 id="141">14.1 &nbsp; 初探动态规划<a class="headerlink" href="#141" title="Permanent link">&para;</a></h1>
<p>「动态规划 dynamic programming」是一个重要的算法范式它将一个问题分解为一系列更小的子问题并通过存储子问题的解来避免重复计算从而大幅提升时间效率。</p>
<p>在本节中,我们从一个经典例题入手,先给出它的暴力回溯解法,观察其中包含的重叠子问题,再逐步导出更高效的动态规划解法。</p>
@ -4703,11 +4735,73 @@ dp[i] = dp[i-1] + dp[i-2]
<p>观察以上代码,由于省去了数组 <code>dp</code> 占用的空间,因此空间复杂度从 <span class="arithmatex">\(O(n)\)</span> 降低至 <span class="arithmatex">\(O(1)\)</span></p>
<p>在动态规划问题中,当前状态往往仅与前面有限个状态有关,这时我们可以只保留必要的状态,通过“降维”来节省内存空间。<strong>这种空间优化技巧被称为“滚动变量”或“滚动数组”</strong></p>
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