target$,则将$low=mid+1$的值。
+4. 当查找最后$low>high$则查找失败。
+
+$ASL$查找成功为$\dfrac{1+2+3+\cdots+n}{n}=\dfrac{n+1}{2}$,$ASL$查找失败为$n+1$,时间复杂度为$O(n)$。
+
+#### 折半公式优化
+
+在对$mid$进行取值时,如果数据量太大,查找到右侧时计算$mid$进行两数相加$low+high$可能会数值溢出。那么如何解决?
+
++ 变幻公式:$(low+high)\div2\rightarrow low\div2+high\div2$或$\rightarrow low-(low\div2-high\div2)\rightarrow low+(high-low)\div2$。
++ 无符号右移运算:$mid=(low+hight) >>> 1$。直接将除以$2$变为右移运算,速度更快,且舍去了小数位不需要进行取整运算。
+
+#### 折半查找判定树
+
+折半查找的过程可用二叉树来描述,称为判定树。树中每个圆形结点表示一个记录,结点中的值为该记录的关键字值;树中最下面的叶结点都是方形的,它表示查找不成功的情况。从判定树可以看出,查找成功时的查找长度为从根结点到目的结点的路径上的结点数,而查找不成功时的查找长度为从根结点到对应失败结点的父结点的路径上的结点数。
+
+构建判定树方式:
+
++ 若当前$low$和$high$有奇数个元素,则$mid$分割后左右两部分元素个数相等。
++ 若当前$low$和$high$有奇数个元素,则$mid$分割后左部分元素个数小于右部分一个。
++ 数值结点为圆形,末端结点后再加一层方形的叶子结点,表示查找失败。
+
+判定树性质:
+
++ 折半查找判定树一定是一个平衡二叉树。只有最下面一层不满,元素个数为$n$时树高与完全二叉树相等$h=\lceil\log_2(n+1)\rceil$。
++ 根据折半查找判定树可以计算对应的$ASL$:查找成功的$ASL$=($\sum\limits_{i=1}^n$第$i$层的成功结点数$\times i$)$\div$成功结点总数,查找失败的$ASL$=($\sum\limits_{i=1}^n$第$i$层的失败结点数$\times i$)$\div$失败结点总数。
++ 折半查找判定树也是一个二叉查找树,失败结点$=n+1$(成功结点的空链域结点数)。
++ 折半查找判定树的中序序列应该是一个有序序列。
+
+$ASL$查找成功查找失败都一定小于折半查找树的树高,时间复杂度为$O(\log_2n)$。
+
+查找的折半查找判定树和排序的二叉查找树都是同样的二叉逻辑结构,但是不同的是折半查找判定树是已知完整序列,所以总是从中间开始,时间性能为固定的$O(\log_2n)$,而二叉查找树的构造是根据输入来的,如果输入的序列正好是从中间切分的,则时间性能为最好的$O(\log_2n)$,如果输入的序列恰好有序,则为单枝树,时间性能为最坏的$O(n)$。
+
+### 分块查找
+
+分块查找又称为索引顺序查找,需要对数据进行一定的排序,不一定全部是顺序的,但是要求在一个区间内是满足一定条件的,即块内无序,块间有序。即$n$块内的元素全部小于$n+1$块内的任意元素。
+
+将查找表分割为若干子块,其中分割的块数和每块里的数据个数都是不定的。
+
+#### 分块查找结构
+
+除了保存数据的顺序表外还需要定义一个索引表,保存每个分块的**最大关键字**、每块的存储空间,第一个元素的地址。
+
+很明显这种定义方式定义了两个顺序结构,并且如果插入删除时需要大量移动元素,所以可以采用链表的形式。
+
+定义一种最大元素结点,包含数据、指向后继最大元素结点的指针、指向分块内元素的指针;定义一种块内元素结点,包含数据、指向后继分块内元素的指针。但是这时候就无法折半查找,只能顺序查找。
+
+所以总的来说分块查找还是一种静态查找,动态插入删除的效率较低。
+
+#### 分块查找过程
+
+在查找时先根据关键字遍历索引表,然后找到索引表的分块(可以顺序也可以折半),再到存储数据的顺序表的索引区间中查找。
+
+若适用折半查找查找索引表的分块,索引表中若不存在目标关键字,则折半查找索引表最终会停在$low>high$,要在$low$所指向分块中查找。
+
+#### 分块查找的效率
+
+$ASL$查找成功失败的情况都十分复杂,所以一般不会考。
+
+假设长度为$n$的查找表被均匀分为$b$块,每块$s$个元素,假设索引查找和块内查找的平均查找长度$ASL$分别为$L_I$和$L_S$,则分块查找的平均查找长度为$ASL=L_I+L_S$。
+
+使用顺序查找索引表,则$L_I=\dfrac{(1+2+\cdots+b)}{b}=\dfrac{b+1}{2}$,$L_S=\dfrac{(1+2+\cdots+s)}{s}=\dfrac{s+1}{2}$。所以$ASL=\dfrac{b+1}{2}+\dfrac{s+1}{2}=\dfrac{s^2+2s+n}{2s}$,当$s=\sqrt{n}$时,$ASL_{min}=\sqrt{n}+1$。
+
+使用折半查找索引表,则$L_I=\lceil\log_2(b+1)\rceil$,$L_S=\dfrac{(1+2+\cdots+s)}{s}=\dfrac{s+1}{2}$。所以$ASL=\lceil\log_2(b+1)\rceil+\dfrac{s+1}{2}$。
+
+## 二叉查找
+
+### 二叉查找树
+
+即$BST$,是一种用于排序的二叉树。
+
+#### 二叉查找树定义
+
+二叉查找树也是二叉排序树。左子树上所有结点的关键字均小于根结点的关键字;右子树上所有结点的关键字均大于根结点的关键字;左右子树又各是一棵二叉查找树。
+
+中序遍历二叉查找树会得到一个递增的有序序列。
+
+#### 二叉查找树查找
+
+1. 若树非空,目标值与根结点的值比较。
+2. 若相等则查找成功,返回结点指针。
+3. 若小于根结点,则在左子树上查找,否则在右子树上查找。
+4. 遍历结束后仍没有找到则返回$NULL$。
+
+遍历查找的时间复杂度是$O(\log_2n)$,则递归查找的时间复杂度是$O(\lceil\log_2(n+1)\rceil)$,其中$\lceil\log_2(n+1)\rceil$代表二叉树的高度。
+
+查找成功的平均查找长度$ASL$,二叉树的平均查找长度为$O(\log_2n)$,最坏情况是每个结点只有一个分支,平均查找长度为$O(n)$。
+
+#### 二叉查找树插入
+
++ 若原二叉查找树为空,就直接插入结点。
++ 否则,若关键字小于根结点值,插入左结点树。
++ 若关键字大于根结点值,插入右结点树。
+
+#### 二叉查找树删除
+
++ 搜索到对应值的目标结点。
++ 若被删除结点$p$是叶子结点,则直接删除,不会破坏二叉查找树的结构。
++ 若被删除结点只有一棵左子树或右子树,则让该结点的子树称为该结点父结点的子树,来代替其的位置。
++ 若被删除结点有左子树和右子树,则让其结点的直接后继(中序排序该结点后一个结点,其右子树的最左下角结点,不一定是叶子结点)或直接前驱(中序排序该结点前一个结点,其左子树的最右下角结点,不一定是叶子结点)替代该结点,并从二叉查找树中删除该的结点直接后继、直接前驱,这就变成了第一种或第二种情况。
+
+二叉查找树删除或插入时得到的二叉查找树往往与原来的不同。
+
+#### 二叉查找树查找效率
+
+二叉查找树的查找效率主要取决于树的高度,若是平衡二叉树,则平均查找长度为$O(\log_2n)$,若最坏情况下只有一个单枝树,则平均查找长度为$O(n)$。
+
+若按顺序输入关键字则会得到单枝树。
+
+查找过程来看,二叉查找树和二分查找类似。但是二分查找的判定树唯一,而二叉查找树的查找不唯一,相关关键字插入顺序会极大影响到查找效率。
+
+从维护来看,二叉查找树插入删除操作只需要移动指针,时间代价为$O(\log_2n)$,而二分查找的对象是有序顺序表,代价是$O(n)$。
+
+所以静态查找时使用顺序表进行二分查找,而动态查找时使用二叉查找树。
+
+### 平衡二叉树
+
+为了限制判定树高增长过快,降低二叉查找树的性能,规定插入时要保证平衡。
+
+即$AVL$树,树上任意一结点的左子树和右子树的高度之差不超过$1$。
+
+树高即树的深度。
+
+结点的平衡因子=左子树高-右子树高。
+
+即平衡二叉树的平衡因子只可能为$-1,0,1$。
+
+在插入一个结点时,查找路径上的所有结点都可能收到影响。
+
+从插入点往回(从下往上)找到第一个不平衡的结点,调整以该结点为根的子树。每次调整的对象都是最小不平衡树。
+
+#### 平衡二叉树结点
+
+$h$为平衡二叉树高度,$n_h$为构造此高度的平衡二叉树所需的最少结点数。
+
++ 平衡二叉树最少结点数(所有非叶结点的平衡因子均为$1$的)的递推公式为$n_0=0$,$n_1=1$,$n_2=2$,$n_h=1+n_{h-1}+n_{h-2}$。此时所有非叶结点的平衡因子均为$1$。
+
+假设$T$为高度为$h$的平衡二叉树,其需要最少的结点数目为$F(h)$。
+
+又假设$TL$,$TR$为$T$的左右子树,因此$TL$,$TR$也为平衡二叉树。
+
+假设$FL$、$FR$为$TL$,$TR$的最少结点数,则,$F(h)=FL+FR+1$。那么$FL$、$FR$到底等于多少呢?
+
+由于$TL$,$TR$与$T$一样是平衡二叉树,又由于我们知道$T$的最少结点数是$F(h)$,其中$h$为$T$的高度,因此如果我们知道$TL$,$TR$的高度就可以知道$FL$、$FR$的值了。
+
+由平衡二叉树的定义可以知道,$TL$和$TR$的高度要么相同,要么相差$1$,而当$TL$与$TR$高度相同(即都等于$h-1$)时,我们算出来的$F(h)$并不能保证最小,两边都是$h-1$明显比只有一边$h-2$的结点数更多。
+
+因此只有当$TL$与$TR$高度相差一,即一个高度为$h-1$,一个高度为$h-2$时,计算出来的$F(h)$才能最小。
+
+此时我们假设$TL$比$TR$高度要高$1$,即$TL$高度为$h-1$,$TR$高度为$h-2$,则有$F1=F(h-1)$,$F2=F(h-2)$。
+
+因此得到结论:$F(h)=F(h-1)+F(h-2)+1$。
+
++ 平衡二叉树最多结点数$2^n-1$。即该二叉树为满二叉树。
+
+#### 平衡二叉树插入
+
+最重要的是根据二叉查找树的大小关系算出从大到小的序列,然后把最中间的作为新根,向两侧作为左右子树。
+
+即先找到插入路径上离插入结点最近的平衡因子的绝对值大于$1$的结点$A$,再对以$A$为根的子树,在保持二叉查找树特性的前提下调整各结点位置。
+
+每次调整的都是最小不平衡子树。
+
+#### 平衡二叉树删除
+
+与插入操作类似,都是需要从下往上进行调整。
+
+不同的是插入操作只对子树进行调整,而删除操作可能要对整个树进行调整。
+
+在任意一棵非空二叉查找树$T_1$中,删除某结点$v$之后形成二叉查找树$T_2$,再将$v$插入$T_2$,形成二叉查找树$T_3$。
+
++ 若$v$是$T_1$的叶结点,则$T_1$与$T_3$相同。
++ 若$v$不是$T_1$的叶结点,则$T_1$与$T_3$不同(如果是一定不同则是错误的)。
+
+#### 右单旋转
+
+从结点的左孩子的左子树中插入导致不平衡:
+
+```txt
+ A(2)
+ |
+ ------------
+ | |
+ B(1) AR(H)
+ |
+ ------------
+ | |
+BL(H+1) BR(H)
+```
+
+$BL注意:散列表长度$m$必须是一个可以表示为$4j+3$的素数才能探测到所有位置。
++ 伪随机探测法:定义$d_i$是一个伪随机数。
++ 再散列法:$d_i=Hash_2(key)$,又称双散列法。需要使用两个散列函数,当通过第一个散列函数$H(key)$得到的地址发生冲突时,则利用第二个散列函数$Hash_2(key)$计算该关键字的地址增量。它的具体散列函数形式:$H_i=(H(key)+i\times Hash_2(key))\%m$。初始探测位置$H=H(key)\%m$。$i$是冲突的次数,初始为$0$。在再散列法中,最多经过$m-1$次探测就会遍历表中所有位置,回到$H_0$位置。
+
+**聚集**:同义和非同义关键字都堆积到一起。原因是选取不当的处理冲突的方法。对查找长度有直接的影响。
+
+注意:在开放定址的情形下,不能随便物理删除表中的已有元素,因为若删除元素,则会截断其他具有相同散列地址的元素的查找地址。因此,要删除一个元素时,可给它做一个删除标记,进行逻辑删除。但这样做的副作用是:执行多次删除后,表面上看起来散列表很满,实际上有许多位置未利用,因此需要定期维护散列表,要把删除标记的元素物理删除。
+
+#### 链地址法
+
+又称为拉链法或链接法,是把相互发生冲突的同义词用一个单链表链接起来,若干组同义词可以组成若干个单链表。思想类似于邻接表的基本思想,且这种方法适合于冲突比较严重的情况。
+
+指针需要额外的空间,故当结点规模较小时,开放定址法较为节省空间,而若将节省的指针空间用来扩大散列表的规模,可使装填因子变小,这又减少了开放定址法中的冲突,从而提高平均查找速度。
+
+每次冲突都要重新哈希,计算时间增加。
+
+#### 公共溢出区法
+
+为所有冲突的关键字记录建立一个公共的溢出区来存放。在查找时,对给定关键字通过散列函数计算出散列地址后,先与基本表的相应位置进行比对,如果相等,则查找成功;如果不相等,则到溢出表进行顺序查找。如果相对于基本表而言,在有冲突的数据很少的情况下,公共溢出区的结构对查找性能来说还是非常高的。
+
+### 散列查找
+
+先通过散列函数计算目标元素存储地址,然后根据解决冲突的方法进行下一步的查询。
+
+如果使用拉链法通过散列函数计算得到存储地址为空,则可以直接代表查找失败,这时候一般定义查找长度这里不算。
+
+而如果使用开放地址法计算得到空位置的时候,代表查找失败,但是这时候需要定义查找长度要算这个地址。
+
+若散列函数设计得足够好,散列查找时间复杂度可以达到$O(1)$,即不存在冲突。
+
+散列表的查找效率取决于三个因素:散列函数、处理冲突的方法和装填因子。
+
+**装填因子**=表中记录数÷散列表长度。装填因子代表一个散列表中的满余情况,越大则查找效率越低。
+
+若只给出了装填因子$\alpha$,则此时平均查找长度为:$ASL=\dfrac{1}{2}(1+\dfrac{1}{1-\alpha})$。
+
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
+
+
+
+
+
+9-sort
+排序
+基本概念
+
+- 排序:将一个数据元素的任意序列重新排列成一个按关键字有序的序列。
+- 内部排序:待排序的记录存放在计算机的内存中所进行的排序操作称为内部排序。
+- 外部排序:待排序的记录数量很大,以致内存一次不能容纳全部记录,在排序过程中需要访问外存的排序过程称为外部排序。
+- 稳定的排序:比如一个序列是“”,按从小到大排序后变成“”,就叫做稳定排序,即3和3*相对顺序不变。如果相同关键字的顺序发生了改变,则是不稳定的排序。稳定性的需要看具体的应用场景。
+- 内部排序的算法性能取决于算法的时间复杂度和空间复杂度,而时间复杂度一般是由比较和移动次数决定的。外部排序除此之外还要考虑磁盘读写速度和次数。
+- 大部分排序算法都仅适用于顺序存储的线性表。
+- 大部分排序需要经过比较和移动两个过程。(基数排序不需要比较)排序时至少需要比较次。每次比较两个关键字后,仅出现两种可能的转移。假设整个排序过程至少需要做次比较。则显然会有种情况。由于个记录共有种不同的排列,因而必须有种不同的比较路径,于是有,所以得到不等式。
+
+
+插入排序
+插入排序的排序序列分为非排序和已排序序列。插入排序就是将选定的目标值插入到对应的位置。
+直接插入排序
+直接插入排序过程
+每次将一个待排序的记录按其关键字大小插入到前面已排好序的子序列中,直到全部记录插入完成为止。
+直接插入排序性能
+空间复杂度为。
+时间复杂度主要来自对比关键字,移动元素,若有个元素,则需要趟处理。
+最好情况是原本的序列就是有序的,需要趟处理,每次只需要对比一次关键字,不用移动元素,时间复杂度为。
+最坏情况是原本的序列是逆序的,需要趟处理,第趟处理需要对比关键字次,移动元素次,时间复杂度是。
+所以平均时间复杂度是。
+直接插入排序算法是稳定的。
+如果使用链表实现直接插入排序,移动元素的次数变少了,但是关键字对比次数仍然时,从而整体时间复杂度依然是。
+直接插入排序特性
+
+- 在待排序的元素序列基本有序的前提下,直接插入排序效率最高的。
+- 直接插入排序进行躺后能保证前个元素是有序的,但是不能保证其都在最终的位置上(如最小的元素在最后一位,这是插入排序的共性)。
+
+
+二分插入排序
+二分插入排序过程
+也称为折半插入排序,是对直接插入排序的优化,在寻找插入位置时使用二分查找的方式。
+当时,为了保证算法的稳定性,会继续在所指位置右边寻找插入位置。
+当时停止折半查找,并将内的元素全部右移,并把元素值赋值到所指的位置。
+折半插入排序是找到位置了后一起移动元素,而直接插入排序是边查找边排序。
+二分插入排序性能
+空间复杂度为。
+二分插入排序是稳定的。
+比起直接插入排序,比较关键字的次数减少为,移动元素的次数没变,所以总体时间复杂度为。
+二分插入排序特性
+
+- 对于直接插入的优化仅在于二分查找的比较次数。
+- 二分插入排序进行躺后能保证前个元素是有序的,但是不能保证其都在最终的位置上(如最小的元素在最后一位,这是插入排序的共性)。
+
+
+希尔排序
+又称缩小增量排序。
+希尔排序过程
+希尔排序也是对直接插入排序的优化。直接插入排序对于基本有序的序列排序效果较好,所以就希望序列能尽可能基本有序。从而希尔排序的思想就是先追求表中元素部分有序,然后逐渐逼近全局有序。
+先将整个待排序元素序列分割成若干个子序列(由相隔某个“增量”的元素组成的),分别进行直接插入排序,然后缩小增量重复上述过程,直到增量为。每次对比只对比两个以上的个元素进行插入交换。
+增量序列的选择建议是第一趟选择元素个数的一半,后面不断缩小到原来的一半。
+希尔排序性能
+空间复杂度为。
+而时间复杂度和增量序列的选择有关,目前无法使用属性手段证明确切的时间复杂度。最坏时间复杂度为,在某个范围内可以达到。
+希尔排序是不稳定的。(因为相同的元素可能分到不同的子序列中进行重排打乱原有顺序)
+希尔排序只适用于顺序表而不适合用于链表,无法快速进行增量的访问。
+希尔排序特性
+
+- 希尔排序在最后一趟前都不能保证元素在最后的位置上。
+- 希尔排序在最后一趟前都不能保证元素是有序的。
+
+
+交换排序
+交换排序即根据序列中两个元素关键的比较结构然后交换这两个记录在序列中的位置。
+冒泡排序
+重点就是相邻两两比较。
+冒泡排序过程
+从后往前或从前往后两两比较相邻元素的值,若逆序则交换这两个值,如果相等也不交换,直到序列比较完。这个过程是一趟冒泡排序,第趟后第个元素会已经排序完成。每一趟都会让关键字最小或最大的一个元素到未排序队列的第一个或最后一个。一共需要趟排序。
+冒泡排序中所产生的有序子序列一定是全局有序的(不同于直接插入排序),也就是说,有序子序列中的所有元素的关键字一定小于或大于无序子序列中所有元素的关键字,这样每趟排序都会将一个元素放置到其最终的位置上。
+冒泡排序性能
+空间复杂度为。
+最好情况下即本身序列有序,则比较次数是,交换次数是,从而时间复杂度是。
+最坏情况是逆序情况,比较次数和交换次数都是,所以时间复杂度是。
+从而平均时间复杂度是。
+冒泡排序是稳定的。
+冒泡排序可以用于链表。
+冒泡优化
+对于每行冒泡进行优化:如果发现排序前几轮就已经实现了排序成功,那么后面的排序岂不是都浪费了时间进行比较?可以在第一轮循环中设置一个布尔值为,如果在这一轮发生排序交换就设置为,如果一轮结束后发现这个值还是,说明这一轮没有进行交换,表示已经排序成功,就直接所有退出循环。
+对于每列冒泡进行优化:默认每一轮冒泡是从结束,如一共个元素排序,需要轮排序,第二轮冒泡排序应该从开始,到结束,因为最后一个元素4已经在第一轮排序成功。但是如果在第二轮发现,已经排序成功了不需要交换,那么默认排序方法第三轮还是要从到进行排序,还要比较一次和位置的数据,这就造成了浪费,那么如何解决?记录每一轮发生比较的元素的最大索引值,下一轮比较到这个索引值直接结束,不需要继续比较后面的元素。如果最大索引值为则直接退出。这就进一步优化了上面一种策略。
+void Bubble(int[] a) {
+ int n = a.length - 1;
+ while (true)
+ {
+ //表示最后一次交换元素位置
+ int last = 0;
+ for (int i = 0; i < n; i++)
+ {
+ if (a[i] > a[i + 1]) {
+ swap(a, i, i + 1);
+ last = i;
+ }
+ }
+ n = last;
+ if (n == 0)
+ {
+ return;
+ }
+ }
+}
+
+冒泡排序特性
+
+- 冒泡排序产生的序列全局有序,趟排序后第个元素到达最终的位置上,前个或后个位置的元素确定。
+
+
+快速排序
+排序过程类似于构建二叉排序树。基于分治法。
+快速排序过程
+取待排序序列中的某个元素作为基准(一般取第一个元素),通过一趟排序,将待排元素分为左右两个子序列,左子序列元素的关键字均小于或等于基准元素的关键字,右子序列的关键字则大于基准元素的关键字,称进行了一趟快速排序(一次划分),这个已经成功排序。然后分别对两个子序列继续进行快速排序,直至整个序列有序。
+单边循环快排
+即洛穆托分区方案。
+
+- 选择最右边的元素值做标杆,把标杆值放入变量中。
+- 初始时,令和都指向最左边的元素。其中用于被动向右移动,维护小于标杆值的元素的边界,即每次交换的目标索引,一旦交换就向右移动一个;用于主动向右移动,寻找比标杆值小的元素,一旦找到就与指向元素进行交换。
+- 然后开始移动,判断指向的元素值是否小于值,如果不小于就继续向右移动。
+- 当遇到比标杆小的值,指向的值就和指向的值进行交换,如果和指向的值为同一个则不进行交换,然后右移一个,继续右移查找。
+- 继续移动,最后时将基准点元素值与指向值进行交换,该轮排序结束。此时指向的位置就是值所应该在的位置。
+- 返回基准点元素所在索引,从而确定排序上下边界,递归继续执行排序。
+
+
+双边循环快排
+分为普通分区方案和霍尔分区方案。逻辑基本上一样,只是边界选择方式不同。
+
+- 先选择个值做标杆,一般为最左边值,把标杆值放入变量中。
+- 初始时,令指向序列最右边的值,指向序列最左边的值。
+- 然后从开始不断左移,当遇到比标杆大或等于的值时。
+- 如果发现比标杆小的值,即,需要交换,然后不动,开始移动去找要交换的大于标杆的值。
+- 若所指向的值比标杆小或等于,则进行寻找。(为什么要加上一个等于标杆值可以继续向右移动的条件?因为标杆值默认是第一个值,即初始化跟指向同一个值,如果只能小于标杆值才能继续移动不交换,则在第一个元素时,由于标杆值被初始化赋值为第一个值,则标杆值等于值,从而导致值跟值进行交换,导致标杆值被交换走了,标杆值变为了最开始最右边的值,导致了排序有问题)
+- 若所指向的值比标杆大,则值与值进行交换。
+- 交换后不动,开始移动。回到步骤三开始执行。
+- 当时表示和之前的元素都比基准小,和之后的元素都比基准大,完成了一次划分。然后把基准元素放入指向的位置。
+- 不断交替使用和指针进行对比。对左右子序列进行同样的递归操作即可,从步骤三开始。若左右两个子序列的元素数量小等于一,则无需再划分。
+
+
+即对序列进行比较,有头尾两个指针,尾指针开始比较向前移动,若指向值比对比值小则要交换,交替让头指针开始移动,否则不改变指针则尾指针继续向前;同理头指针向后移动,若指向值比对比值大则交换,交替让尾指针移动,否则不改变指针则头指针继续向后。最后头尾指针指向一个位置,将对比值插入到当前值,此时一趟完成。
+洛穆托分区与霍尔分区比较。
+快速排序性能
+由于快速排序使用了递归,所以需要递归工作栈,空间复杂度与递归层数相关,所以为递归层数。
+每一层划分只需要处理剩余的待排序元素,时间复杂度不超过,所以时间复杂度为归层数。
+而快速排序会将所有元素组织成为二叉树,二叉树的层数就是递归调用的层数。所以对于个结点的二叉树,最小高度为,最大高度为。
+从而最好时间复杂度为,最坏时间复杂度为,平均时间复杂度为;最好空间复杂度为,最坏空间复杂度为,平均空间复杂度为。
+所以如果初始序列是有序的或逆序的,则快速排序性能最差(速度最慢)。若每一次选中的基准能均匀划分,尽量让数轴元素平分,则效率最高(速度最快)。性能与分区处理顺序无关。
+所以对于快速排序性能优化是选择尽可能能中分的基准元素,入选头中尾三个位置的元素,选择中间值作为基准元素,或随机选择一个元素作为基准元素。
+最好使用顺序存储,这样找到数轴元素与遍历时比较简单。
+快速排序算法是不稳定的。
+快速排序特性
+
+- 快速排序不产生有序子序列。
+- 枢轴元素到达的位置是不确定的,但是每次都会到其最终的位置上。第趟有个元素到最终位置上。
+- 求快速排序趟数就是找到符合这种性质的元素个数。
+- 快速排序在内部排序中的表现最好。
+- 对于基本有序或倒序的序列,快速排序速度最慢。
+- 对于每次的数轴元素能尽量将表分为长度相同的子表,快速排序速度最快。
+
+
+选择排序
+分为已排序和未排序序列。选择排序就是每一趟在待排序元素中选取关键字最小或最大的元素加入有序子序列。
+选择排序也需要交换,但是与交换排序的不断交换不同的是选择排序时选择出一个最后进行交换,一趟只交换一次。
+选择排序也需要插入,且也分为已排序和未排序序列,但是插入排序不需要选择,且元素移动方式是插入而不是交换。
+选择排序算法的比较次数始终为,与序列状态无关。
+简单选择排序
+简单选择排序过程
+即每一趟在待排序元素中选取关键字最小的元素加入有序序列。交换发生在选出最值后,在每趟的尾部。经过趟就可以完成。
+简单选择排序性能
+空间复杂度为。
+时间复杂度为。
+简单选择排序是不稳定的。因为选择后会进行交换,影响顺序。
+简单选择排序也可以适用于链表。
+直接插入排序与简单选择排序
+插入排序和选择排序都是分为未排序和已排序两个部分,那么其中有什么区别?
+如、、、、、进行排序。
+18 23 19 9 23* 15
+插入排序:
+18 23 19 9 23* 15
+18 19 23 9 23* 15
+9 18 19 23 23* 15
+9 18 19 23 23* 15
+9 15 18 19 23 23*
+
+选择排序:
+9 23 19 18 23* 15
+9 15 19 18 23* 23
+9 15 18 19 23* 23
+9 15 18 19 23* 23
+9 15 18 19 23* 23
+9 15 18 19 23* 23
+
+堆排序
+堆的定义
+若个关键字序列满足下面某一条性质,则就是堆:
+
+- 若满足且则是大根堆或大顶堆。
+- 若满足且则是小根堆或小顶堆。
+
+
+所以堆就是用顺序存储的完全二叉树。
+堆的叶子结点范围是。
+堆的建立
+其实堆就是层序存储的完全二叉树。其中:
+
+- 的结点都是非终端结点。
+- 的左孩子是。
+- 的右孩子是。
+- 的父结点是。
+
+
+所以建立根堆过程是:
+
+- 按照关键字序列依次添加关键字到二叉树,按照层次遍历顺序添加。
+- 初始化成功后再从下往上、从左至右按逆层次遍历顺序不断调整位置。
+- 如果是大根堆则大元素往上,且当前结点与更大的孩子结点互换;如果是小根堆则小元素往上,且当前结点与更小的孩子结点互换。
+- 递归往上时父子结点不断交换位置。
+- 如果元素互换破坏了调整好的下一级的堆,则使用同样的方法对下一层递归调整。
+
+
+如用堆排序对建立小根堆堆。首先将这组数据按层序初始化为无序堆,然后从最后向前开始调整:
+
+<!-- 1. 从的结点开始往前遍历。
+
+检查当前结点与左孩子和右孩子是否满足根堆条件,若不满足则交换。
+
+- 若是建立大根堆,检查是否满足根大于等于左、右结点,若不满足,则当前结点与更大的一个孩子互换。
+- 若是建立小根堆,检查是否满足根小于等于左、右结点,若不满足,则当前结点与更小的一个孩子互换。
+
+
+
+若元素互换破坏了下一级的堆,则采用同样的方法继续向下调整。
+
+- 若是建立大根堆,则小的元素不断下坠。
+- 若是建立小根堆,则大的元素不断下坠。 -->
+
+
+
+
+
+
+堆排序过程
+由于选择排序是在每一趟都选择最大或最小的值进行排序,所以堆排序中就通过堆这个存储结构来完成对最值的选取——直接选择堆顶元素。
+堆排序即每次将堆顶元素与堆底元素(堆最底层最右元素)进行交换,表示这个部分已经排序完成了不需要进行调整,第趟表示倒数个元素已经有序,所以无序的元素就是堆前面的元素。
+
+- 每一趟将堆顶元素加入子序列(堆顶元素与待排序序列中的最后一个元素交换)。此时后面的这个元素就排序好了。最右下的元素作为堆顶元素。
+- 此时待排序序列已经不是堆了(堆顶不能保证是最小或最大的元素),需要将其再次调整为堆(小元素或大元素不断下坠)。
+- 重复步骤一二。
+- 直到趟处理后得到有序序列。基于大根堆的堆排序会得到递增序列,而基于小根堆的堆排序会得到递减序列。
+
+
+调整堆从右边即序列末尾开始。
+堆排序性能
+堆排序的存储就是它本身,不需要额外的存储空间,要么只需要一个用于交换或临时存放元素的辅助空间。所以空间复杂度为。
+若树高为,某结点在第层,则将这个结点向下调整最多只需要下坠层,关键字对比次数不超过次。
+第层最多个结点,而只有第层的结点才可能需要下坠调整。所以调整时关键字对比次数不超过。
+所以建堆过程中,关键字对比次数不超过,建堆的时间复杂度为。
+堆排序中处理时根结点最多下坠层,而每下坠一层,最多对比关键字两次,所以每一趟排序的时间复杂度不超过,一共趟,所以时间复杂度为。所以总的时间复杂度也是。
+堆排序是不稳定的。
+堆排序适合关键字较多的情况。创如,在亿个数中选出前个最大值,首先使用一个大小为的数组,读入前个数,建立小顶堆,而后依次读入余下的教,若小于堆顶则舍弃,否则用该数取代堆顶并重新调整堆,待数据读取完毕,堆中个数即为所求。
+堆的插入
+新元素放到表尾(即最右下角元素),并与其的父结点进行对比,若新元素比父元素更大(大根堆)或更小(小根堆),则二者互换,并保持上升,直到无法上升为止。时间复杂度为树高。
+堆的删除
+被删除的元素用堆底元素(即最右下角元素)代替,然后让这个元素不断下坠,直到无法下坠为止。时间复杂度为树高。
+堆排序特性
+
+- 适合大量数据进行排序。
+- 在含有个关键字的小根堆中,关键字最大的记录存储范围为。这是小根堆,关键字最大的记录一定存储在这个堆所对应的完全二叉树的叶子结点中;又因为二叉树中的最后一个非叶子结点存储在中,所以得到范围。
+
+
+归并排序
+归并是指把两个(二路归并)或多个(多路归并)已经有序的序列合并为一个。
+该算法是采用分治法的一个非常典型的应用。将已有序的子序列合并,得到完全有序的序列;即先使每个子序列有序,再使子序列段间有序。
+在较大数据进行排序时为了加快速度使用归并排序,用空间换时间。
+二路归并排序
+二路归并排序比较常用,且基本上用于内部排序,多路排序多用于外部排序。
+二路归并排序过程
+
+- 把长度为的输入序列分成两个长度为的子序列。
+- 对这两个子序列分别采用归并排序。
+- 将两个排序好的子序列合并成一个最终的排序序列。
+
+
+归并排序趟数为。
+二路归并排序性能
+二路归并排序是一棵倒立的二叉树。
+空间复杂度主要来自辅助数组,所以为,而递归调用的调用栈的空间复杂度为,总的空间复杂度就是为,无论平均还是最坏,所以这个算法在内部排序算法中空间消耗最大。
+个元素二路归并排序,归并一共要趟,每次归并时间复杂度为,则算法时间复杂度为
+归并排序是稳定的。
+分配排序
+分配排序过程无须比较关键字,而是通过用额外的空间来“分配”和“收集”来实现排序,它们的时间复杂度可达到线性阶。简言之就是:用空间换时间,所以性能与基于比较的排序才有数量级的提高。
+基数排序
+基数排序不是基于比较的排序算法,其核心在于将输入的数据值转化为键存储在额外开辟的数组空间中。作为一种线性时间复杂度的排序,计数排序要求输入的数据必须是有确定范围的整数。
+
+- 只能对整数进行排序。
+- 元素的移动次数与关键字的初始排列次序无关。
+
+
+基数的定义
+假设长度为的线性表中每个结点的关键字由元组组成,其中,其中就是基数。
+基数排序过程
+有最高位优先和最低位优先两种方法。
+若是要得到递减序列:
+
+- 初始化:设置个空辅助队列。
+- 按照每个关键字位权重递增的次序(个、十、百),对个关键字位分别做分配和收集。
+- 分配就是顺序扫描各个元素,若当前处理的关键字位为,就将元素插入队尾。
+- 收集就是把各个队列的结点依次出队并链接在一起。
+
+
+基数排序性能
+基数排序基本上使用链式存储而不是一般的顺序存储。
+需要个辅助队列,所以空间复杂度为。
+一趟分配,一趟收集,一共有趟分配收集,所以总的时间复杂度为。与序列初始状态无关。
+基数排序是稳定的。
+基数排序的应用
+对于一般的整数排序是可以按位排序的,也可以处理一些实际问题,如根据人的年龄排序,需要从年月日三个维度分别设置年份的队列、月份的队列(到)、日的队列(到)。
+所以基数排序擅长解决的问题:
+
+- 数据元素的关键字可以方便地拆分为组,且较小。
+- 每组关键字的取值范围不大,即较小。
+- 数据元素个数较大。
+
+
+计数排序
+作为一种线性时间复杂度的排序,计数排序要求输入的数据必须是有确定范围的整数。
+计数排序过程
+
+- 找出待排序的数组中最大和最小的元素。
+- 统计数组中每个值为i的元素出现的次数,存入数组C的第i项。
+- 对所有的计数累加(从C中的第一个元素开始,每一项和前一项相加)。
+- 反向填充目标数组:将每个元素i放在新数组的第C(i)项,每放一个元素就将C(i)减去1。
+
+
+当输入的元素是个属于的整数时,时间复杂度是,空间复杂度也是,其排序速度快于任何比较排序算法。
+当不是很大并且序列比较集中时,计数排序是一个很有效的排序算法。
+计数排序是稳定的。
+桶排序
+桶排序是计数排序的升级版。它利用了函数的映射关系,高效与否的关键就在于这个映射函数的确定。桶排序的工作的原理:假设输入数据服从均匀分布,将数据分到有限数量的桶里,每个桶再分别排序(有可能再使用别的排序算法或是以递归方式继续使用桶排序进行排)。
+桶排序过程
+
+- 设置一个定量的数组当作空桶。
+- 遍历输入数据,并且把数据一个一个放到对应的桶里去。
+- 对每个不是空的桶进行排序。
+- 从不是空的桶里把排好序的数据拼接起来。
+
+
+桶排序性能
+桶排序最好情况下使用线性时间,桶排序的时间复杂度,取决与对各个桶之间数据进行排序的时间复杂度,因为其它部分的时间复杂度都为。桶排序的平均时间复杂度为线性的,其中,其中代表桶划分的数量。
+很显然,桶划分的越小,各个桶之间的数据越少,排序所用的时间也会越少。但相应的空间消耗就会增大。
+桶排序是稳定的。
+内部排序
+指在排序期间元素全部存放在内存中的排序。除了分配排序,其他的内部排序往往要经过比较和移动。
+
+
+| 算法种类 | 最好时间复杂度 | 平均时间复杂度 | 最好时间复杂度 | 空间复杂度 | 是否稳定 | 趟数 |
+| 直接插入排序 | | | | | 是 | |
| 希尔排序 | | | | | 否 | |
| 简单选择排序 | | | | | 否 | |
| 快速排序 | | | | | 否 | 初始序列 |
| 冒泡排序 | | | | | 是 | 初始序列 |
| 堆排序 | | | | | 否 | 初始序列 |
| 二路归并排序 | | | | | 是 | |
| 基数排序 | | | | | 是 | |
+
+
+- 每趟排序结束都至少能够确定一个元素最终位置的方法:选择、交换。(插入和归并则不行)
+
+
+外部排序
+指在排序期间元素无法全部同时存放在内存中,必须在排序过程中根据要求不断地在内、外存之间移动的排序。
+外部排序的原理
+外部排序过程
+磁盘的读写是以块为单位,数据读入内存后才能被修改,修改完成后还需要写回磁盘。
+外部排序就是针对数据元素太多,无法一次性全部读入内存进行排序而进行处理的在外部磁盘进行的排序处理方式。
+使用归并排序的方式,最少只用在内存分配三块大小的缓冲区(两个输入缓冲一个输出缓冲)即可堆任意一个大文件进行排序。然后对缓冲区里的数据进行内部排序。
+外部排序过程:
+
+- 生成初始归并段(大小为输入缓冲区的总大小),需要读写并进行内部排序。
+- 重复读写,进行内部归并排序。填满输出缓冲就可以输出。输入缓冲空就可以输入新数据。
+
+
+外部排序时间开销=读写外存时间(最大的时间开销)+内部排序所需时间+内部归并所需时间。
+外部排序的优化方法
+优化方法就是使用更多路的多路归并,减少归并趟数。
+路平衡归并:最多只能有个段归并为一个,需要一个输出缓冲区和个输入缓冲区;每一趟归并中,若有个归并段参与归并,则经过这一趟处理得到个新的归并段。
+对个初始归并段,使用路归并,则归并树可以使用叉树表示,若树高为,则归并趟数最小为。
+但是多路归并会带来负面影响:
+
+- 路归并时,需要开辟个输入缓冲区,内存开销增加。
+- 每挑选一个关键字需要对比关键字次,内部归并时间增加。
+
+
+同时,若能增加初始归并段的长度,也可以减少初始归并段数量从而进行优化。
+败者树
+用于通过过去归并的经历减少归并次数。败者树可以看作一棵多了一个单个的根的完全二叉树。个叶结点分别是当前参加比较的元素,非叶子结点用来记忆左右子树中的失败者,而让胜者往上继续比较,一直到根结点。
+如要用败者树排序,格式:元素值归并段索引值:
+ 1(3)
+ |
+ 2(5)
+ |
+ ----------------------
+ | |
+ 12(2) 4(8)
+ | |
+ ------------ ------------
+ | | | |
+ 27(1) 17(4) 9(6) 11(7)
+ | | | |
+ ------ ------ ------ ------
+ | | | | | | | |
+27 12 1 17 2 9 11 4
+
+传统方法从个归并段选出一个最大或最小元素需要对比关键字次,而使用路归并的败者树只需要对比关键字次(败者树层数,不包括成功结点)。
+构建败者树还是需要次对比。
+置换选择排序
+如果内存工作区只能容纳个记录,则初始归并段也只能包含条记录,若文件共有条记录,则初始归并段的数量为。
+用于构建更长的初始归并段,从而减少归并次数。
+假设初始始待排文件为,初始归并段输出文件为,内存工作区为,和的初始状态为空,可容纳个记录。置换选择算法的步骤如下:
+
+- 从输入记录到工作区。
+- 从中选出其中关键字取最小值的记录,记为记录。
+- 将记录输出到中去。
+- 若不空,则从输入下一个记录到中。
+- 从中所有关键字比记录的关键字大的记录中选出最小关键字记录,作为新的记录。
+- 重复步骤三到五,如果新输入到的关键字小于的值,则驻留在中,直至在中填满选不出新的记录为止,由此得到一个初始归并段,输出一个归并段的结束标志到中去。准备输出新的归并段。
+- 重复步骤二到六,直至为空。由此得到全部初始归并段。
+
+
+此时输出的初始归并段可以超过,且初始归并段长度是不一定相等的。
+如:,长度为,为、、。
+^表示中断当前归并段,下一个开启新的归并段;√表示该值超过值,不能输出到当前归并段中,只能等待输出到下一个归并段。
+
+
+| FI | 4 | 6 | 9 | 7 | 13 | 11 | 14 | 22 | 30 | 2 | 3 | 19 | | 20 |
+| WA1 | 4 | 4 | 4 | 7 | 7 | 11 | 11 | 22 | 22 | 22 | 3√ | 3√ | 3 | 3 |
| WA2 | | 6 | 6 | 6 | 13 | 13 | 13 | 13 | 30 | 30 | 30 | 19√ | 19 | 19 |
| WA3 | | | 9 | 9 | 9 | 9 | 14 | 14 | 14 | 2√ | 2√ | 2√ | 2 | 20 |
| MINIMAX | | | 4 | 6 | 7 | 9 | 11 | 13 | 14 | 22 | 30 | 30 | 2 | 3 |
| FO | | | 4 | 6 | 7 | 9 | 11 | 13 | 14 | 22 | 30 | ^ | 2 | 3 |
+
+
+
+| FI | 17 | 1 | 23 | 5 | 36 | 12 | | 18 | 21 | 39 | | | | |
+| WA1 | 17 | 1√ | 1√ | 1√ | 1√ | 1√ | 1 | 18 | 18 | 18 | | | | |
| WA2 | 19 | 19 | 23 | 23 | 36 | 12√ | 12 | 12 | 12 | 39 | 39 | 39 | | |
| WA3 | 20 | 20 | 20 | 5√ | 5√ | 5√ | 5 | 5 | 21 | 21 | 21 | | | |
| MINIMAX | 17 | 19 | 20 | 23 | 36 | 36 | 1 | 5 | 12 | 18 | 21 | 39 | | |
| FO | 17 | 19 | 20 | 23 | 36 | ^ | 1 | 5 | 12 | 18 | 21 | 39 | ^ | |
+
+最佳归并树
+因为现实中的每个归并段的长度不同,所以归并的次序比较重要。
+最佳归并树的衡量
+每个初始归并段可以看作一个叶子结点,归并树的长度作为结点权值,则归并树的带权路径长度等于读写磁盘的次数。从而归并过程中的磁盘次数=归并树的。
+最佳归并树的构造
+所以就需要一棵类似哈夫曼树来成为最佳的归并树,不断选择最小的段进行归并。
+添加虚段
+对于叉归并来说,若初始归并段的数量无法构成严格的叉归并树,则需要补充几个长度为的虚拟段从而能保证严格叉归并,再进行叉哈夫曼树的构造。
+那么添加多少虚段呢?
+叉的最佳归并树一定是一棵严格的叉树,即树中只包含度为和的结点。
+设度为的结点有个,度为的结点有个,归并树的总结点树为,则初始归并段数量+虚段数量=。
+所以,,所以,所以一定是可以整除的。如果不整除就要添加虚段。
+
+
+
+```
+形如这类代码无法与LaTex代码同时渲染,故此处均删去
+```
+
+
+
+# 排序
+
+## 基本概念
+
++ 排序:将一个数据元素的任意序列重新排列成一个按关键字有序的序列。
++ 内部排序:待排序的记录存放在计算机的内存中所进行的排序操作称为内部排序。
++ 外部排序:待排序的记录数量很大,以致内存一次不能容纳全部记录,在排序过程中需要访问外存的排序过程称为外部排序。
++ 稳定的排序:比如一个序列是“$1,4,3,3*,2$”,按从小到大排序后变成“$1,2,3,3*,4$”,就叫做稳定排序,即3和3*相对顺序不变。如果相同关键字的顺序发生了改变,则是不稳定的排序。稳定性的需要看具体的应用场景。
++ 内部排序的算法性能取决于算法的时间复杂度和空间复杂度,而时间复杂度一般是由比较和移动次数决定的。外部排序除此之外还要考虑磁盘读写速度和次数。
++ 大部分排序算法都仅适用于顺序存储的线性表。
++ 大部分排序需要经过比较和移动两个过程。(基数排序不需要比较)排序时至少需要比较$\lceil\log_2(n!)\rceil$次。每次比较两个关键字后,仅出现两种可能的转移。假设整个排序过程至少需要做$t$次比较。则显然会有$2^t$种情况。由于$n$个记录共有$n!$种不同的排列,因而必须有$n!$种不同的比较路径,于是有$2^t\geqslant n!$,所以得到不等式。
+
+## 插入排序
+
+插入排序的排序序列分为非排序和已排序序列。插入排序就是将选定的目标值插入到对应的位置。
+
+### 直接插入排序
+
+#### 直接插入排序过程
+
+每次将一个待排序的记录按其关键字大小插入到前面已排好序的子序列中,直到全部记录插入完成为止。
+
+#### 直接插入排序性能
+
+空间复杂度为$O(1)$。
+
+时间复杂度主要来自对比关键字,移动元素,若有$n$个元素,则需要$n-1$趟处理。
+
+最好情况是原本的序列就是有序的,需要$n-1$趟处理,每次只需要对比一次关键字,不用移动元素,时间复杂度为$O(n)$。
+
+最坏情况是原本的序列是逆序的,需要$n-1$趟处理,第$i$趟处理需要对比关键字$i+1$次,移动元素$i+2$次,时间复杂度是$O(n^2)$。
+
+所以平均时间复杂度是$O(n^2)$。
+
+直接插入排序算法是稳定的。
+
+如果使用链表实现直接插入排序,移动元素的次数变少了,但是关键字对比次数仍然时$O(n^2)$,从而整体时间复杂度依然是$O(n^2)$。
+
+#### 直接插入排序特性
+
++ 在待排序的元素序列基本有序的前提下,直接插入排序效率最高的。
++ 直接插入排序进行$n$躺后能保证前$n+1$个元素是有序的,但是不能保证其都在最终的位置上(如最小的元素在最后一位,这是插入排序的共性)。
+
+### 二分插入排序
+
+#### 二分插入排序过程
+
+也称为折半插入排序,是对直接插入排序的优化,在寻找插入位置时使用二分查找的方式。
+
+当$data[mid]==data[i]$时,为了保证算法的稳定性,会继续在$mid$所指位置右边寻找插入位置。
+
+当$low>high$时停止折半查找,并将$[low,i-1]$内的元素全部右移,并把元素值赋值到$low$所指的位置。
+
+折半插入排序是找到位置了后一起移动元素,而直接插入排序是边查找边排序。
+
+#### 二分插入排序性能
+
+空间复杂度为$O(1)$。
+
+二分插入排序是稳定的。
+
+比起直接插入排序,比较关键字的次数减少为$O(n\log_2n)$,移动元素的次数没变,所以总体时间复杂度为$O(n^2)$。
+
+#### 二分插入排序特性
+
++ 对于直接插入的优化仅在于二分查找的比较次数。
++ 二分插入排序进行$n$躺后能保证前$n+1$个元素是有序的,但是不能保证其都在最终的位置上(如最小的元素在最后一位,这是插入排序的共性)。
+
+### 希尔排序
+
+又称缩小增量排序。
+
+#### 希尔排序过程
+
+希尔排序也是对直接插入排序的优化。直接插入排序对于基本有序的序列排序效果较好,所以就希望序列能尽可能基本有序。从而希尔排序的思想就是先追求表中元素部分有序,然后逐渐逼近全局有序。
+
+先将整个待排序元素序列分割成若干个子序列(由相隔某个“增量”的元素组成的),分别进行**直接插入排序**,然后缩小增量重复上述过程,直到增量为$1$。每次对比只对比两个以上的个元素进行插入交换。
+
+增量序列的选择建议是第一趟选择元素个数的一半,后面不断缩小到原来的一半。
+
+#### 希尔排序性能
+
+空间复杂度为$O(1)$。
+
+而时间复杂度和增量序列的选择有关,目前无法使用属性手段证明确切的时间复杂度。最坏时间复杂度为$O(n^2)$,在某个范围内可以达到$O(n^{1.3})$。
+
+希尔排序是不稳定的。(因为相同的元素可能分到不同的子序列中进行重排打乱原有顺序)
+
+希尔排序只适用于顺序表而不适合用于链表,无法快速进行增量的访问。
+
+#### 希尔排序特性
+
++ 希尔排序在最后一趟前都不能保证元素在最后的位置上。
++ 希尔排序在最后一趟前都不能保证元素是有序的。
+
+## 交换排序
+
+交换排序即根据序列中两个元素关键的比较结构然后交换这两个记录在序列中的位置。
+
+### 冒泡排序
+
+重点就是相邻两两比较。
+
+#### 冒泡排序过程
+
+从后往前或从前往后两两比较相邻元素的值,若逆序则交换这两个值,如果相等也不交换,直到序列比较完。这个过程是一趟冒泡排序,第$i$趟后第$i$个元素会已经排序完成。每一趟都会让关键字最小或最大的一个元素到未排序队列的第一个或最后一个。一共需要$n-1$趟排序。
+
+冒泡排序中所产生的有序子序列一定是全局有序的(不同于直接插入排序),也就是说,有序子序列中的所有元素的关键字一定小于或大于无序子序列中所有元素的关键字,这样每趟排序都会将一个元素放置到其最终的位置上。
+
+#### 冒泡排序性能
+
+空间复杂度为$O(1)$。
+
+最好情况下即本身序列有序,则比较次数是$n-1$,交换次数是$0$,从而时间复杂度是$O(n)$。
+
+最坏情况是逆序情况,比较次数和交换次数都是$\dfrac{n(n-1)}{2}$,所以时间复杂度是$O(n^2)$。
+
+从而平均时间复杂度是$O(n^2)$。
+
+冒泡排序是稳定的。
+
+冒泡排序可以用于链表。
+
+#### 冒泡优化
+
+对于每行冒泡进行优化:如果发现排序前几轮就已经实现了排序成功,那么后面的排序岂不是都浪费了时间进行比较?可以在第一轮循环中设置一个布尔值为$false$,如果在这一轮发生排序交换就设置为$true$,如果一轮结束后发现这个值还是$false$,说明这一轮没有进行交换,表示已经排序成功,就直接所有退出循环。
+
+对于每列冒泡进行优化:默认每一轮冒泡是从$[length-i]$结束,如一共$5$个元素排序,需要$4$轮排序,第二轮冒泡排序应该从$0$开始,到$3$结束,因为最后一个元素4已经在第一轮排序成功。但是如果在第二轮发现$2$,$3$已经排序成功了不需要交换,那么默认排序方法第三轮还是要从$0$到$2$进行排序,还要比较一次$1$和$2$位置的数据,这就造成了浪费,那么如何解决?记录每一轮发生比较的元素的最大索引值,下一轮比较到这个索引值直接结束,不需要继续比较后面的元素。如果最大索引值为$0$则直接退出。这就进一步优化了上面一种策略。
+
+```c
+void Bubble(int[] a) {
+ int n = a.length - 1;
+ while (true)
+ {
+ //表示最后一次交换元素位置
+ int last = 0;
+ for (int i = 0; i < n; i++)
+ {
+ if (a[i] > a[i + 1]) {
+ swap(a, i, i + 1);
+ last = i;
+ }
+ }
+ n = last;
+ if (n == 0)
+ {
+ return;
+ }
+ }
+}
+```
+
+#### 冒泡排序特性
+
++ 冒泡排序产生的序列全局有序,$n$趟排序后第$n$个元素到达最终的位置上,前$n$个或后$n$个位置的元素确定。
+
+### 快速排序
+
+排序过程类似于构建二叉排序树。基于分治法。
+
+#### 快速排序过程
+
+取待排序序列中的某个元素$pivot$作为基准(一般取第一个元素),通过一趟排序,将待排元素分为左右两个子序列,左子序列元素的关键字均小于或等于基准元素的关键字,右子序列的关键字则大于基准元素的关键字,称进行了一趟快速排序(一次划分),这个$pivot$已经成功排序。然后分别对两个子序列继续进行快速排序,直至整个序列有序。
+
+#### 单边循环快排
+
+即$lomuto$洛穆托分区方案。
+
+1. 选择最右边的元素值做标杆,把标杆值放入$pivot$变量中。
+2. 初始时,令$low$和$high$都指向最左边的元素。其中$low$用于被动向右移动,维护小于标杆值的元素的边界,即每次交换的目标索引,一旦交换$low$就向右移动一个;$high$用于主动向右移动,寻找比标杆值小的元素,一旦找到就与$low$指向元素进行交换。
+3. 然后$high$开始移动,判断$high$指向的元素值是否小于$pivot$值,如果不小于就继续向右移动。
+4. 当遇到比标杆小的值,$high$指向的值就和$low$指向的值进行交换,如果$high$和$low$指向的值为同一个则不进行交换,然后$low$右移一个,$high$继续右移查找。
+5. $high$继续移动,最后$high>=pivot$时将基准点元素值与$low$指向值进行交换,该轮排序结束。此时$low$指向的位置就是$pivot$值所应该在的位置。
+6. 返回基准点元素所在索引,从而确定排序上下边界,递归继续执行排序。
+
+#### 双边循环快排
+
+分为普通分区方案和$hoare$霍尔分区方案。逻辑基本上一样,只是边界选择方式不同。
+
+1. 先选择个值做标杆,一般为最左边值,把标杆值放入$pivot$变量中。
+2. 初始时,令$high$指向序列最右边的值,$low$指向序列最左边的值。
+3. 然后从$high$开始不断左移,当遇到比标杆大或等于的值时$high--$。
+4. 如果发现比标杆小的值,即$high
+
+ 调整堆的时间与树高相关$O(\log_2n)$,建立堆的时间复杂度为$O(n)$。比较总次数不超过$4n$。
+
+#### 堆排序过程
+
+由于选择排序是在每一趟都选择最大或最小的值进行排序,所以堆排序中就通过堆这个存储结构来完成对最值的选取——直接选择堆顶元素。
+
+堆排序即每次将堆顶元素与堆底元素(堆最底层最右元素)进行交换,表示这个部分已经排序完成了不需要进行调整,第$i$趟表示倒数$i$个元素已经有序,所以无序的元素就是堆前面的元素。
+
+1. 每一趟将堆顶元素加入子序列(堆顶元素与待排序序列中的最后一个元素交换)。此时后面的这个元素就排序好了。最右下的元素作为堆顶元素。
+2. 此时待排序序列已经不是堆了(堆顶不能保证是最小或最大的元素),需要将其再次调整为堆(小元素或大元素不断下坠)。
+3. 重复步骤一二。
+4. 直到$n-1$趟处理后得到有序序列。基于大根堆的堆排序会得到递增序列,而基于小根堆的堆排序会得到递减序列。
+
+调整堆从右边即序列末尾开始。
+
+#### 堆排序性能
+
+堆排序的存储就是它本身,不需要额外的存储空间,要么只需要一个用于交换或临时存放元素的辅助空间。所以空间复杂度为$O(1)$。
+
+若树高为$h$,某结点在第$i$层,则将这个结点向下调整最多只需要下坠$h-i$层,关键字对比次数不超过$2(h-i)$次。
+
+第$i$层最多$2^{i-1}$个结点,而只有第$1\cdots(h-1)$层的结点才可能需要下坠调整。所以调整时关键字对比次数不超过$\sum_{i=h-1}^12^{i-1}2(h-i)=\sum_{j=1}^{h-1}2^{h-j}j\leqslant2n\sum_{j=1}^{h-1}\dfrac{j}{2^j}\leqslant4n$。
+
+所以建堆过程中,关键字对比次数不超过$4n$,建堆的时间复杂度为$O(n)$。
+
+堆排序中处理时根结点最多下坠$h-1$层,而每下坠一层,最多对比关键字两次,所以每一趟排序的时间复杂度不超过$O(h)=O(\log_2n)$,一共$n-1$趟,所以时间复杂度为$O(n\log_2n)$。所以总的时间复杂度也是$O(n\log_2n)$。
+
+堆排序是不稳定的。
+
+堆排序适合关键字较多的情况。创如,在$1$亿个数中选出前$100$个最大值,首先使用一个大小为$100$的数组,读入前$100$个数,建立小顶堆,而后依次读入余下的教,若小于堆顶则舍弃,否则用该数取代堆顶并重新调整堆,待数据读取完毕,堆中$100$个数即为所求。
+
+#### 堆的插入
+
+新元素放到表尾(即最右下角元素),并与其$\lfloor\dfrac{i}{2}\rfloor$的父结点进行对比,若新元素比父元素更大(大根堆)或更小(小根堆),则二者互换,并保持上升,直到无法上升为止。时间复杂度为树高$O(\log_2n)$。
+
+#### 堆的删除
+
+被删除的元素用堆底元素(即最右下角元素)代替,然后让这个元素不断下坠,直到无法下坠为止。时间复杂度为树高$O(\log_2n)$。
+
+#### 堆排序特性
+
++ 适合大量数据进行排序。
++ 在含有$n$个关键字的小根堆中,关键字最大的记录存储范围为$\lfloor\dfrac{n}{2}\rfloor+1\sim n$。这是小根堆,关键字最大的记录一定存储在这个堆所对应的完全二叉树的叶子结点中;又因为二叉树中的最后一个非叶子结点存储在$\lfloor\dfrac{n}{2}\rfloor$中,所以得到范围。
+
+## 归并排序
+
+归并是指把两个(二路归并)或多个(多路归并)已经有序的序列合并为一个。
+
+该算法是采用分治法的一个非常典型的应用。将已有序的子序列合并,得到完全有序的序列;即先使每个子序列有序,再使子序列段间有序。
+
+在较大数据进行排序时为了加快速度使用归并排序,用空间换时间。
+
+### 二路归并排序
+
+二路归并排序比较常用,且基本上用于内部排序,多路排序多用于外部排序。
+
+#### 二路归并排序过程
+
+1. 把长度为$n$的输入序列分成两个长度为$\dfrac{n}{2}$的子序列。
+2. 对这两个子序列分别采用归并排序。
+3. 将两个排序好的子序列合并成一个最终的排序序列。
+
+归并排序趟数为$\lceil\log_2n\rceil$。
+
+#### 二路归并排序性能
+
+二路归并排序是一棵倒立的二叉树。
+
+空间复杂度主要来自辅助数组,所以为$O(n)$,而递归调用的调用栈的空间复杂度为$O(\log_2n)$,总的空间复杂度就是为$O(n)$,无论平均还是最坏,所以这个算法在内部排序算法中空间消耗最大。
+
+$n$个元素二路归并排序,归并一共要$\log_2n$趟,每次归并时间复杂度为$O(n)$,则算法时间复杂度为$O(n\log_2n)$
+
+归并排序是稳定的。
+
+## 分配排序
+
+分配排序过程无须比较关键字,而是通过用额外的空间来“分配”和“收集”来实现排序,它们的时间复杂度可达到线性阶$O(n)$。简言之就是:用空间换时间,所以性能与基于比较的排序才有数量级的提高。
+
+### 基数排序
+
+基数排序不是基于比较的排序算法,其核心在于将输入的数据值转化为键存储在额外开辟的数组空间中。作为一种线性时间复杂度的排序,计数排序要求输入的数据必须是有确定范围的整数。
+
++ 只能对整数进行排序。
++ 元素的移动次数与关键字的初始排列次序无关。
+
+#### 基数的定义
+
+假设长度为$n$的线性表中每个结点$a_j$的关键字由$d$元组$(k_j^{d-1},k_j^{d-2},\cdots,k_j^1,k_j^0)$组成,其中$0\geqslant k_j^i\geqslant r-1\,(0\geqslant i\geqslant d-1)$,其中$r$就是基数。
+
+#### 基数排序过程
+
+有最高位优先$MSD$和最低位优先$LSD$两种方法。
+
+若是要得到递减序列:
+
+1. 初始化:设置$r$个空辅助队列$Q_{r-1},Q_{r-2},\cdots,Q_0$。
+2. 按照每个关键字位**权重递增**的次序(个、十、百),对$d$个关键字位分别做分配和收集。
+3. 分配就是顺序扫描各个元素,若当前处理的关键字位为$x$,就将元素插入$Q_x$队尾。
+4. 收集就是把$Q_{r-1},Q_{r-2},\cdots,Q_0$各个队列的结点依次出队并链接在一起。
+
+#### 基数排序性能
+
+基数排序基本上使用链式存储而不是一般的顺序存储。
+
+需要$r$个辅助队列,所以空间复杂度为$O(r)$。
+
+一趟分配$O(n)$,一趟收集$O(r)$,一共有$d$趟分配收集,所以总的时间复杂度为$O(d(n+r))$。与序列初始状态无关。
+
+基数排序是稳定的。
+
+#### 基数排序的应用
+
+对于一般的整数排序是可以按位排序的,也可以处理一些实际问题,如根据人的年龄排序,需要从年月日三个维度分别设置年份的队列、月份的队列($1$到$12$)、日的队列($1$到$31$)。
+
+所以基数排序擅长解决的问题:
+
+1. 数据元素的关键字可以方便地拆分为$d$组,且$d$较小。
+2. 每组关键字的取值范围不大,即$r$较小。
+3. 数据元素个数$n$较大。
+
+### 计数排序
+
+作为一种线性时间复杂度的排序,计数排序要求输入的数据必须是有确定范围的整数。
+
+#### 计数排序过程
+
+1. 找出待排序的数组中最大和最小的元素。
+2. 统计数组中每个值为i的元素出现的次数,存入数组C的第i项。
+3. 对所有的计数累加(从C中的第一个元素开始,每一项和前一项相加)。
+4. 反向填充目标数组:将每个元素i放在新数组的第C(i)项,每放一个元素就将C(i)减去1。
+
+当输入的元素是$n$个属于$[0,k]$的整数时,时间复杂度是$O(n+k)$,空间复杂度也是$O(n+k)$,其排序速度快于任何比较排序算法。
+
+当$k$不是很大并且序列比较集中时,计数排序是一个很有效的排序算法。
+
+计数排序是稳定的。
+
+### 桶排序
+
+桶排序是计数排序的升级版。它利用了函数的映射关系,高效与否的关键就在于这个映射函数的确定。桶排序的工作的原理:假设输入数据服从均匀分布,将数据分到有限数量的桶里,每个桶再分别排序(有可能再使用别的排序算法或是以递归方式继续使用桶排序进行排)。
+
+#### 桶排序过程
+
+1. 设置一个定量的数组当作空桶。
+2. 遍历输入数据,并且把数据一个一个放到对应的桶里去。
+3. 对每个不是空的桶进行排序。
+4. 从不是空的桶里把排好序的数据拼接起来。
+
+#### 桶排序性能
+
+桶排序最好情况下使用线性时间$O(n)$,桶排序的时间复杂度,取决与对各个桶之间数据进行排序的时间复杂度,因为其它部分的时间复杂度都为$O(n)$。桶排序的平均时间复杂度为线性的$O(n+C)$,其中$C=n\times(\log n-\log m)$,其中$m$代表桶划分的数量。
+
+很显然,桶划分的越小,各个桶之间的数据越少,排序所用的时间也会越少。但相应的空间消耗就会增大。
+
+桶排序是稳定的。
+
+## 内部排序
+
+指在排序期间元素全部存放在内存中的排序。除了分配排序,其他的内部排序往往要经过比较和移动。
+
+| 算法种类 | 最好时间复杂度 | 平均时间复杂度 | 最好时间复杂度 | 空间复杂度 | 是否稳定 | 趟数 |
+| :----------: | :------------: | :------------: | :------------: | :----------: | :------: | :-------: |
+| 直接插入排序 | $O(n)$ | $O(n^2)$ | $O(n^2)$ | $O(1)$ | 是 | $n-1$ |
+| 希尔排序 | $?$ | $?$ | $?$ | $O(1)$ | 否 | $s$ |
+| 简单选择排序 | $O(n^2)$ | $O(n^2)$ | $O(n^2)$ | $O(1)$ | 否 | $n-1$ |
+| 快速排序 | $O(n\log_2n)$ | $O(n\log_2n)$ | $O(n^2)$ | $O(\log_2n)$ | 否 | 初始序列 |
+| 冒泡排序 | $O(n)$ | $O(n^2)$ | $O(n^2)$ | $O(1)$ | 是 | 初始序列 |
+| 堆排序 | $O(n\log_2n)$ | $O(n\log_2n)$ | $O(n\log_2n)$ | $O(1)$ | 否 | 初始序列 |
+| 二路归并排序 | $O(n\log_2n)$ | $O(n\log_2n)$ | $O(n\log_2n)$ | $O(n)$ | 是 | $\log_2n$ |
+| 基数排序 | $O(d(n+r))$ | $O(d(n+r))$ | $O(d(n+r))$ | $O(r)$ | 是 | $r$ |
+
++ 每趟排序结束都至少能够确定一个元素最终位置的方法:选择、交换。(插入和归并则不行)
+
+## 外部排序
+
+指在排序期间元素无法全部同时存放在内存中,必须在排序过程中根据要求不断地在内、外存之间移动的排序。
+
+### 外部排序的原理
+
+#### 外部排序过程
+
+磁盘的读写是以块为单位,数据读入内存后才能被修改,修改完成后还需要写回磁盘。
+
+外部排序就是针对数据元素太多,无法一次性全部读入内存进行排序而进行处理的在外部磁盘进行的排序处理方式。
+
+使用归并排序的方式,最少只用在内存分配三块大小的缓冲区(两个输入缓冲一个输出缓冲)即可堆任意一个大文件进行排序。然后对缓冲区里的数据进行内部排序。
+
+外部排序过程:
+
+1. 生成初始归并段(大小为输入缓冲区的总大小),需要读写并进行内部排序。
+2. 重复读写,进行内部归并排序。填满输出缓冲就可以输出。输入缓冲空就可以输入新数据。
+
+外部排序时间开销=读写外存时间(最大的时间开销)+内部排序所需时间+内部归并所需时间。
+
+#### 外部排序的优化方法
+
+优化方法就是使用更多路的多路归并,减少归并趟数。
+
+$k$路平衡归并:最多只能有$k$个段归并为一个,需要一个输出缓冲区和$k$个输入缓冲区;每一趟归并中,若有$m$个归并段参与归并,则经过这一趟处理得到$\lceil\dfrac{m}{k}\rceil$个新的归并段。
+
+对$r$个初始归并段,使用$k$路归并,则归并树可以使用$k$叉树表示,若树高为$h$,则归并趟数最小为$h=\lceil\log_kr\rceil+1$。
+
+但是多路归并会带来负面影响:
+
+1. $k$路归并时,需要开辟$k$个输入缓冲区,内存开销增加。
+2. 每挑选一个关键字需要对比关键字$(k-1)$次,内部归并时间增加。
+
+同时,若能增加初始归并段的长度$k$,也可以减少初始归并段数量$r$从而进行优化。
+
+### 败者树
+
+用于通过过去归并的经历减少归并次数。败者树可以看作一棵多了一个单个的根的完全二叉树。$k$个叶结点分别是当前参加比较的元素,非叶子结点用来记忆左右子树中的失败者,而让胜者往上继续比较,一直到根结点。
+
+如要用败者树排序$27,12,1,17,2,9,11,4$,格式:元素值$($归并段索引值$)$:
+
+```terminal
+ 1(3)
+ |
+ 2(5)
+ |
+ ----------------------
+ | |
+ 12(2) 4(8)
+ | |
+ ------------ ------------
+ | | | |
+ 27(1) 17(4) 9(6) 11(7)
+ | | | |
+ ------ ------ ------ ------
+ | | | | | | | |
+27 12 1 17 2 9 11 4
+```
+
+传统方法从$k$个归并段选出一个最大或最小元素需要对比关键字$k-1$次,而使用$k$路归并的败者树只需要对比关键字$\lceil\log_2k\rceil$次(败者树层数,不包括成功结点)。
+
+构建败者树还是需要$n-1$次对比。
+
+### 置换选择排序
+
+如果内存工作区只能容纳$l$个记录,则初始归并段也只能包含$l$条记录,若文件共有$n$条记录,则初始归并段的数量为$r=\lceil n/l\rceil$。
+
+用于构建更长的初始归并段,从而减少归并次数。
+
+假设初始始待排文件为$FI$,初始归并段输出文件为$FO$,内存工作区为$WA$,$FO$和$WA$的初始状态为空,$WA$可容纳$w$个记录。置换选择算法的步骤如下:
+
+1. 从$FI$输入$w$记录到工作区$WA$。
+2. 从$WA$中选出其中关键字取最小值的记录,记为$MINIMAX$记录。
+3. 将$MINIMAX$记录输出到$FO$中去。
+4. 若$FI$不空,则从$FI$输入下一个记录到$WA$中。
+5. 从$WA$中所有关键字比$MINIMAX$记录的关键字大的记录中选出最小关键字记录,作为新的$MINIMAX$记录。
+6. 重复步骤三到五,如果新输入到$FI$的关键字小于$MINIMAX$的值,则驻留在$WA$中,直至在$WA$中填满选不出新的$MINIMAX$记录为止,由此得到一个初始归并段,输出一个归并段的结束标志到$FO$中去。准备输出新的归并段。
+7. 重复步骤二到六,直至$WA$为空。由此得到全部初始归并段。
+
+此时输出的初始归并段可以超过$WA$,且初始归并段长度是不一定相等的。
+
+如$FI$:$4,6,9,7,13,11,14,22,30,2,3,19,20,17,1,23,5,36,12,18,21,39$,$WA$长度为$3$,$FO$为$4,6,7,9,11,13,14,16,22,30$、$2,3,10,17,19,20,23,36$、$1,5,12,18,21,39$。
+
+^表示中断当前归并段,下一个$FI$开启新的归并段;√表示该$WA$值超过$MINIMAX$值,不能输出到当前$FO$归并段中,只能等待输出到下一个归并段。
+
+| FI | 4 | 6 | 9 | 7 | 13 | 11 | 14 | 22 | 30 | 2 | 3 | 19 | | 20 |
+| :-----: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: |
+| WA1 | 4 | 4 | 4 | 7 | 7 | 11 | 11 | 22 | 22 | 22 | 3√ | 3√ | 3 | 3 |
+| WA2 | | 6 | 6 | 6 | 13 | 13 | 13 | 13 | 30 | 30 | 30 | 19√ | 19 | 19 |
+| WA3 | | | 9 | 9 | 9 | 9 | 14 | 14 | 14 | 2√ | 2√ | 2√ | 2 | 20 |
+| MINIMAX | | | 4 | 6 | 7 | 9 | 11 | 13 | 14 | 22 | 30 | 30 | 2 | 3 |
+| FO | | | 4 | 6 | 7 | 9 | 11 | 13 | 14 | 22 | 30 | ^ | 2 | 3 |
+
+| FI | 17 | 1 | 23 | 5 | 36 | 12 | | 18 | 21 | 39 | | | | |
+| :-----: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | ---- |
+| WA1 | 17 | 1√ | 1√ | 1√ | 1√ | 1√ | 1 | 18 | 18 | 18 | | | | |
+| WA2 | 19 | 19 | 23 | 23 | 36 | 12√ | 12 | 12 | 12 | 39 | 39 | 39 | | |
+| WA3 | 20 | 20 | 20 | 5√ | 5√ | 5√ | 5 | 5 | 21 | 21 | 21 | | | |
+| MINIMAX | 17 | 19 | 20 | 23 | 36 | 36 | 1 | 5 | 12 | 18 | 21 | 39 | | |
+| FO | 17 | 19 | 20 | 23 | 36 | ^ | 1 | 5 | 12 | 18 | 21 | 39 | ^ | |
+
+### 最佳归并树
+
+因为现实中的每个归并段的长度不同,所以归并的次序比较重要。
+
+#### 最佳归并树的衡量
+
+每个初始归并段可以看作一个叶子结点,归并树的长度作为结点权值,则归并树的带权路径长度$WPL$等于读写磁盘的次数。从而归并过程中的磁盘$I/O$次数=归并树的$WPL\times2$。
+
+#### 最佳归并树的构造
+
+所以就需要一棵类似哈夫曼树来成为最佳的归并树,不断选择最小的$k$段进行归并。
+
+#### 添加虚段
+
+对于$k$叉归并来说,若初始归并段的数量无法构成严格的$k$叉归并树,则需要补充几个长度为$0$的虚拟段从而能保证严格$k$叉归并,再进行$k$叉哈夫曼树的构造。
+
+那么添加多少虚段呢?
+
+$k$叉的最佳归并树一定是一棵严格的$k$叉树,即树中只包含度为$k$和$0$的结点。
+
+设度为$k$的结点有$n_k$个,度为$0$的结点有$n_0$个,归并树的总结点树为$n$,则初始归并段数量+虚段数量=$n_0$。
+
+所以$n=n_0+n_k$,$kn_k=n-1$,所以$n_0=(k-1)n_k+1$,所以$n_k=\dfrac{(n_0-1)}{(k-1)}$一定是可以整除的。如果不整除就要添加虚段。
+
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