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Fix some figures.

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krahets 2023-08-30 02:52:50 +08:00
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docs/chapter_divide_and_conquer/build_binary_tree_problem.assets/built_tree_step10.png → docs/chapter_divide_and_conquer/build_binary_tree_problem.assets/built_tree_overall.png
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docs/chapter_divide_and_conquer/build_binary_tree_problem.md
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@ -182,8 +182,9 @@
=== "<9>" === "<9>"
![built_tree_step9](build_binary_tree_problem.assets/built_tree_step9.png) ![built_tree_step9](build_binary_tree_problem.assets/built_tree_step9.png)
=== "<10>" 每个递归函数内的前序遍历 `preorder` 和中序遍历 `inorder` 的划分结果如下图所示。
![built_tree_step10](build_binary_tree_problem.assets/built_tree_step10.png)
![每个递归函数中的划分结果](build_binary_tree_problem.assets/built_tree_overall.png)
设树的节点数量为 $n$ ，初始化每一个节点（执行一个递归函数 `dfs()` ）使用 $O(1)$ 时间。**因此总体时间复杂度为 $O(n)$** 。 设树的节点数量为 $n$ ，初始化每一个节点（执行一个递归函数 `dfs()` ）使用 $O(1)$ 时间。**因此总体时间复杂度为 $O(n)$** 。

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docs/chapter_greedy/max_product_cutting_problem.assets/max_product_cutting_greedy_infer2.png
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docs/chapter_greedy/max_product_cutting_problem.md
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@ -44,7 +44,7 @@ $$
**贪心策略二**：在切分方案中，最多只应存在两个 $2$ 。因为三个 $2$ 总是可以被替换为两个 $3$ ，从而获得更大乘积。 **贪心策略二**：在切分方案中，最多只应存在两个 $2$ 。因为三个 $2$ 总是可以被替换为两个 $3$ ，从而获得更大乘积。
![最优切分因子](max_product_cutting_problem.assets/max_product_cutting_greedy_infer3.png) ![最优切分因子](max_product_cutting_problem.assets/max_product_cutting_greedy_infer2.png)
总结以上，可推出以下贪心策略。 总结以上，可推出以下贪心策略。

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docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step_1.png → docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step1.png
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docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step_2.png → docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step2.png
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docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step_3.png → docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step3.png
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docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step_4.png → docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step4.png
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docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step_5.png → docs/chapter_introduction/algorithms_are_everywhere.assets/binary_search_dictionary_step5.png
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docs/chapter_introduction/algorithms_are_everywhere.md
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3. 不断重复步骤 `1.` 和 步骤 `2.` ，直至找到拼音首字母为 $r$ 的页码为止。 3. 不断重复步骤 `1.` 和 步骤 `2.` ，直至找到拼音首字母为 $r$ 的页码为止。
=== "<1>" === "<1>"
![查字典步骤](algorithms_are_everywhere.assets/binary_search_dictionary_step_1.png) ![查字典步骤](algorithms_are_everywhere.assets/binary_search_dictionary_step1.png)
=== "<2>" === "<2>"
![binary_search_dictionary_step_2](algorithms_are_everywhere.assets/binary_search_dictionary_step_2.png) ![binary_search_dictionary_step2](algorithms_are_everywhere.assets/binary_search_dictionary_step2.png)
=== "<3>" === "<3>"
![binary_search_dictionary_step_3](algorithms_are_everywhere.assets/binary_search_dictionary_step_3.png) ![binary_search_dictionary_step3](algorithms_are_everywhere.assets/binary_search_dictionary_step3.png)
=== "<4>" === "<4>"
![binary_search_dictionary_step_4](algorithms_are_everywhere.assets/binary_search_dictionary_step_4.png) ![binary_search_dictionary_step4](algorithms_are_everywhere.assets/binary_search_dictionary_step4.png)
=== "<5>" === "<5>"
![binary_search_dictionary_step_5](algorithms_are_everywhere.assets/binary_search_dictionary_step_5.png) ![binary_search_dictionary_step5](algorithms_are_everywhere.assets/binary_search_dictionary_step5.png)
查阅字典这个小学生必备技能，实际上就是著名的二分查找算法。从数据结构的角度，我们可以把字典视为一个已排序的“数组”；从算法的角度，我们可以将上述查字典的一系列操作看作是“二分查找”。 查阅字典这个小学生必备技能，实际上就是著名的二分查找算法。从数据结构的角度，我们可以把字典视为一个已排序的“数组”；从算法的角度，我们可以将上述查字典的一系列操作看作是“二分查找”。