From e35e2e31eb31cebc9082687ad39bd50dda5200b5 Mon Sep 17 00:00:00 2001 From: krahets Date: Wed, 31 May 2023 01:04:40 +0800 Subject: [PATCH] Add the figure of assembling blocks. Update some texts. --- .../chapter_searching/binary_search_edge.py | 6 ++---- docs/chapter_array_and_linkedlist/summary.md | 2 ++ .../assembling_blocks.jpg | Bin 0 -> 97494 bytes .../algorithms_are_everywhere.md | 2 ++ docs/chapter_sorting/heap_sort.md | 2 +- 5 files changed, 7 insertions(+), 5 deletions(-) create mode 100644 docs/chapter_introduction/algorithms_are_everywhere.assets/assembling_blocks.jpg diff --git a/codes/python/chapter_searching/binary_search_edge.py b/codes/python/chapter_searching/binary_search_edge.py index fb232f375..98fcd9582 100644 --- a/codes/python/chapter_searching/binary_search_edge.py +++ b/codes/python/chapter_searching/binary_search_edge.py @@ -7,8 +7,7 @@ Author: Krahets (krahets@163.com) def binary_search_left_edge(nums: list[int], target: int) -> int: """二分查找最左一个元素""" - # 初始化双闭区间 [0, n-1] ,即 i, j 分别指向数组首元素、尾元素 - i, j = 0, len(nums) - 1 + i, j = 0, len(nums) - 1 # 初始化双闭区间 [0, n-1] while i <= j: m = (i + j) // 2 # 计算中点索引 m if nums[m] < target: @@ -24,8 +23,7 @@ def binary_search_left_edge(nums: list[int], target: int) -> int: def binary_search_right_edge(nums: list[int], target: int) -> int: """二分查找最右一个元素""" - # 初始化双闭区间 [0, n-1] ,即 i, j 分别指向数组首元素、尾元素 - i, j = 0, len(nums) - 1 + i, j = 0, len(nums) - 1 # 初始化双闭区间 [0, n-1] while i <= j: m = (i + j) // 2 # 计算中点索引 m if nums[m] < target: diff --git a/docs/chapter_array_and_linkedlist/summary.md b/docs/chapter_array_and_linkedlist/summary.md index 6e955a04c..b621998e3 100644 --- a/docs/chapter_array_and_linkedlist/summary.md +++ b/docs/chapter_array_and_linkedlist/summary.md @@ -58,6 +58,8 @@ 不修改 `P.next` 也可以。从该链表的角度看,从头结点遍历到尾结点已经遇不到 `P` 了。这意味着结点 `P` 已经从链表中删除了,此时结点 `P` 指向哪里都不会对这条链表产生影响了。 + 从垃圾回收的角度看,对于 Java, Python, Go 等拥有自动垃圾回收的语言来说,节点 `P` 是否被回收取决于是否有仍存在指向它的引用,而不是 `P.next` 的值。在 C, C++ 等语言中,我们需要手动释放节点内存。 + !!! question "在链表中插入和删除操作的时间复杂度是 $O(1)$ 。但是增删之前都需要 $O(n)$ 查找元素,那为什么时间复杂度不是 $O(n)$ 呢?" 如果是先查找元素、再删除元素,确实是 $O(n)$ 。然而,链表的 $O(1)$ 增删的优势可以在其他应用上得到体现。例如,双向队列适合使用链表实现,我们维护一个指针变量始终指向头结点、尾结点,每次插入与删除操作都是 $O(1)$ 。 diff --git a/docs/chapter_introduction/algorithms_are_everywhere.assets/assembling_blocks.jpg b/docs/chapter_introduction/algorithms_are_everywhere.assets/assembling_blocks.jpg new file mode 100644 index 0000000000000000000000000000000000000000..08cb3494ef2b246c3bf3eca81053839e3406506a GIT binary patch literal 97494 zcmbTc1yoy07w8|{-L<$waS2i!f&_=+#R)+}aOf>w9D++I1PBhrAxI0w-5uKE-cq1K zZ}0un@4oeY-+ODl_kZinS!?n;d-lxPXV0F=oH^(3^}qK>pMo?r9F0wlv_S@104yv3 zfK=B2;_HV^2>|%`!eOR5>Ksq4Z8!*K0oVW{06sth0B~{%^fxrqHU~T;kd_8V;Dgd{ z{!jY43V3J<09X>$HRj;>kNp3Ns9gMEZ~y?y_(5CB)h*EFf#V+7I|T0kTmJchX`P|J z85{RE!yXKLV8-9<@ehmqOXnX}{mrgEzOD~Czisw+^>O{p!w>v2ILPgRaSR?fAsFHo z^1#av%;O#819{-@4@~Rh<`f73;FA89!`)mwA6Wc>Nnz%u8V{@l01%RT{1-d_7sK5` zAM6AGH2nO-U=R;aIER3XAcus4f;@+=TZp$C94>0?i$ROhXMfP z?;d=U`5&2cApp=74*)Q3{zt}B1^`gR0RWwI|8+m~zvIOd4)+H>ejE}KA_j4D5&P}X zf2aRng@0@Q@8Q3$C-(b#|Mne+rklG{kT;y;w^LpGy#0b;9D)8$E^Zv6|7#HcKUe%O 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--git a/docs/chapter_introduction/algorithms_are_everywhere.md b/docs/chapter_introduction/algorithms_are_everywhere.md index cd11dba81..239148dd4 100644 --- a/docs/chapter_introduction/algorithms_are_everywhere.md +++ b/docs/chapter_introduction/algorithms_are_everywhere.md @@ -8,6 +8,8 @@ 从数据结构与算法的角度来看,积木的各种形状和连接方式代表数据结构,而组装说明书上的一系列步骤则是算法。 +![拼装积木](algorithms_are_everywhere.assets/assembling_blocks.jpg) + **例二:查阅字典**。在字典里,每个汉字都对应一个拼音,而字典是按照拼音的英文字母顺序排列的。假设我们需要查找一个拼音首字母为 $r$ 的字,通常会这样操作: 1. 翻开字典约一半的页数,查看该页首字母是什么(假设为 $m$ ); diff --git a/docs/chapter_sorting/heap_sort.md b/docs/chapter_sorting/heap_sort.md index 6544a4c1e..88fe9e794 100644 --- a/docs/chapter_sorting/heap_sort.md +++ b/docs/chapter_sorting/heap_sort.md @@ -2,7 +2,7 @@ !!! tip - 阅读本节前,请确保已完成堆章节的学习。 + 阅读本节前,请确保已学完「堆」章节。 「堆排序 Heap Sort」是一种基于堆数据结构实现的高效排序算法。我们可以利用已经学过的“建堆操作”和“元素出堆操作”实现堆排序: