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@ -3438,7 +3438,32 @@
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<h1 id="142">14.2 动态规划问题特性<a class="headerlink" href="#142" title="Permanent link">¶</a></h1>
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<p>在上节中，我们学习了动态规划是如何通过子问题分解来求解问题的。实际上，子问题分解是一种通用的算法思路，在分治、动态规划、回溯中的侧重点不同。</p>
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@ -4165,11 +4197,73 @@ dp[i, 2] = dp[i-2, 1] + dp[i-2, 2]
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<p>在这个问题中，下次跳跃依赖于过去所有的状态，因为每一次跳跃都会在更高的阶梯上设置障碍，并影响未来的跳跃。对于这类问题，动态规划往往难以解决。</p>
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<p>实际上，许多复杂的组合优化问题（例如旅行商问题）都不满足无后效性。对于这类问题，我们通常会选择使用其他方法，例如启发式搜索、遗传算法、强化学习等，从而在有限时间内得到可用的局部最优解。</p>
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@ -4235,48 +4329,31 @@ dp[i, 2] = dp[i-2, 1] + dp[i-2, 2]
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Permission is hereby granted, free of charge, to any person obtaining a copy
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deal in the Software without restriction, including without limitation the
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The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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