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@ -3825,8 +3825,8 @@ dp[i] = dp[i-1] + dp[i-2]
<h2 id="1412">14.1.2. &nbsp; 方法二：记忆化搜索<a class="headerlink" href="#1412" title="Permanent link">&para;</a></h2>
<p>为了提升算法效率，<strong>我们希望所有的重叠子问题都只被计算一次</strong>。为此，我们声明一个数组 <code>mem</code> 来记录每个子问题的解，并在搜索过程中这样做：</p>
<ol>
<li>当首次计算 <span class="arithmatex">\(dp[i]\)</span> 时，我们将其记录至 <code>mem[i]</code> ，以便之后使用；</li>
<li>当再次需要计算 <span class="arithmatex">\(dp[i]\)</span> 时，我们便可直接从 <code>mem[i]</code> 中获取结果，从而将重叠子问题剪枝；</li>
<li>当首次计算 <span class="arithmatex">\(dp[i]\)</span> 时，我们将其记录至 <code>mem[i]</code> ，以便之后使用。</li>
<li>当再次需要计算 <span class="arithmatex">\(dp[i]\)</span> 时，我们便可直接从 <code>mem[i]</code> 中获取结果，从而将重叠子问题剪枝。</li>
</ol>
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@ -4191,9 +4191,9 @@ dp[i] = dp[i-1] + dp[i-2]
<p>与回溯算法一样，动态规划也使用“状态”概念来表示问题求解的某个特定阶段，每个状态都对应一个子问题以及相应的局部最优解。例如，爬楼梯问题的状态定义为当前所在楼梯阶数 <span class="arithmatex">\(i\)</span> 。</p>
<p>总结以上，动态规划的常用术语包括：</p>
<ul>
<li>将数组 <code>dp</code> 称为「<span class="arithmatex">\(dp\)</span> 表」，<span class="arithmatex">\(dp[i]\)</span> 表示状态 <span class="arithmatex">\(i\)</span> 对应子问题的解；</li>
<li>将最小子问题对应的状态（即第 <span class="arithmatex">\(1\)</span> , <span class="arithmatex">\(2\)</span> 阶楼梯）称为「初始状态」；</li>
<li>将递推公式 <span class="arithmatex">\(dp[i] = dp[i-1] + dp[i-2]\)</span> 称为「状态转移方程」；</li>
<li>将数组 <code>dp</code> 称为「<span class="arithmatex">\(dp\)</span> 表」，<span class="arithmatex">\(dp[i]\)</span> 表示状态 <span class="arithmatex">\(i\)</span> 对应子问题的解。</li>
<li>将最小子问题对应的状态（即第 <span class="arithmatex">\(1\)</span> , <span class="arithmatex">\(2\)</span> 阶楼梯）称为「初始状态」。</li>
<li>将递推公式 <span class="arithmatex">\(dp[i] = dp[i-1] + dp[i-2]\)</span> 称为「状态转移方程」。</li>
</ul>
<p><img alt="爬楼梯的动态规划过程" src="../intro_to_dynamic_programming.assets/climbing_stairs_dp.png" /></p>
<p align="center"> Fig. 爬楼梯的动态规划过程 </p>