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chapter_dynamic_programming/intro_to_dynamic_programming/index.html
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@@ -4959,7 +4959,7 @@ 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>根据以上内容，我们可以总结出动态规划的常用术语。</p>
<ul>
<li>将数组 <code>dp</code> 称为<u>dp 表</u>，<span class="arithmatex">\(dp[i]\)</span> 表示状态 <span class="arithmatex">\(i\)</span> 对应子问题的解。</li>
<li>将数组 <code>dp</code> 称为 <u>dp 表</u>，<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（第 <span class="arithmatex">\(1\)</span> 阶和第 <span class="arithmatex">\(2\)</span> 阶楼梯）称为<u>初始状态</u>。</li>
<li>将递推公式 <span class="arithmatex">\(dp[i] = dp[i-1] + dp[i-2]\)</span> 称为<u>状态转移方程</u>。</li>
</ul>
@@ -5387,7 +5387,7 @@ aria-label="页脚"
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