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<h1 id="141">14.1 初探动态规划<a class="headerlink" href="#141" title="Permanent link">¶</a></h1>
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<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>
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<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>
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@ -4703,11 +4735,73 @@ dp[i] = dp[i-1] + dp[i-2]
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<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>
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<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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