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一、一、 单选题(每题 2 分,共20分)
1.1. 栈和队列的共同特点是( )。
A.只允许在端点处插入和删除元素
B.都是先进后出
C.都是先进先出
D.没有共同点
2.2. 用链接方式存储的队列,在进行插入运算时( ).
A. 仅修改头指针 B. 头、尾指针都要修改
C. 仅修改尾指针 D.头、尾指针可能都要修改
3.3. 以下数据结构中哪一个是非线性结构?( )
A. 队列 B. 栈 C. 线性表 D. 二叉树
4.4. 设有一个二维数组A[m][n],假设A[0][0]存放位置在644(10),A[2][2]存放位置在676(10),每个元素占一个空间,问A[3][3](10)存放在什么位置?脚注(10)表示用10进制表示。
A.688 B.678 C.692 D.696
5.5. 树最适合用来表示( )。
A.有序数据元素 B.无序数据元素
C.元素之间具有分支层次关系的数据 D.元素之间无联系的数据
A.2k-1 B.2K+1 C.2K-1 D. 2k-1
7.7. 若有18个元素的有序表存放在一维数组A[19]中,第一个元素放A[1]中,现进行二分查,则查A[3]的比较序列的下标依次为( )
A. 1,2,3 B. 9,5,2,3
C. 9,5最新全国疫情实时大数据,3 D. 9,4,2,3
8.8. 对n个记录的文件进行快速排序,所需要的辅助存储空间大致为
A. O(1) B. O(n) C. O(1og2n) D. O(n2)
9.9. 对于线性表(7,34,55,25,64,46,20,10)进行散列存储时,若选用H(K)开化县属于哪个市=K %9作为散列函数,则散列地址为1的元素有( )个,
A.1 B.2 C.3 D.4
10.10. 设有6个结点的无向图,该图至少应有( )条边才能确保是一个连通图。
A.5 B.6 C.7 D.8
二、二、 填空题(每空1分,共26分)
1.1. 通常从四个方面评价算法的质量:_________、_________、_________和_________。 2.2. 一个算法的时间复杂度为(n3+n2log2n+14n)/n2,其数量级表示为________。
3.3. 假定一棵树的广义表表示为A(C,D(E,F,G),H(I,J)),则树中所含的结点数为__________个,树的深度为___________,树的度为_________。
4.4. 后缀算式9 2 3 +- 10 2 / -的值为__________。中缀算式(3+4X)-2Y/3对应的后缀算式为_______________________________。
5.5. 若用链表存储一棵二叉树时,每个结点除数据域外,还有指向左孩子和右孩子的两个指针。在这种存储结构中,n个结点的二叉树共有________个指针域,其中有________个指针域是存放了地址,有________________个指针是空指针。
6.6. 对于一个具有n个顶点和e条边的有向图和无向图,在其对应的邻接表中,所含边结点分别有_______个和________个。
7.7. AOV网是一种___________________的图。
8.8. 在一个具有n个顶点的无向完全图中,包含有________条边,在一个具有n个顶点的有向完全图中,包含有________条边。
9.9. 假定一个线性表为(12,23,74,55,63,40),若按Key % 4条件进行划分,使得同一余数的元素成为一个子表,则得到的四个子表分别为____________________________、___________________、_______________________和__________________________。
10.10. 向一棵B_树插入元素的过程中,若最终引起树根结点的分裂,则新树比原树的高度___________。
11.11. 在堆排序的过程中,对任一分支结点进行筛运算的时间复杂度为________,整个堆排序过程的时间复杂度为________。
12.12. 在快速排序、堆排序、归并排序中,_________排序是稳定的。
三、三、 运算题(每题 6 分,共24分)
1.1. 在如下数组A中链接存储了一个线性表,表头指针为A [0].next,试写出该线性表。
A 0 1 2 3 4 5 6 7
data | | 60 | 50 | 78 | 90 | 34 | | 40 |
next | 3 | 5 | 7 | 2 | 0 | 4 | | 1 |
| | | | | 昆明周边的旅游景点有哪些 | | | |
2.
2. 请画出图10的邻接矩阵和邻接表。
3.3. 已知一个图的顶点集V和边集E分别为:
V={1,2,3,4,5,6,7}; E={(1,2)3,(1,3)5,(1,4)8,(2,5)10,(2,3)6,(3,4)15,
(3,5)12,(3,6)9,(4,6)4,(4,7)20,(5,6)18,(6,7)25};
用克鲁斯卡尔算法得到最小生成树,试写出在最小生成树中依次得到的各条边。
4.4. 画出向小根堆中加入数据4, 2, 5, 8, 3时,每加入一个数据后堆的变化。
四、四、 阅读算法(每题7分,共14分)
1.1. LinkList mynote(LinkList L)
{//L是不带头结点的单链表的头指针
if(L&&L->next){
q=L;L=L->next;p=L;
S1: while(p->next) p=p->next;
中山市好玩的地方有哪些
S2: p->next=q;q->next=NULL;
}![](data:image/jpg;base64,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)
return L;
}
请回答下列问题:
(1)说明语句S1的功能;
(2)说明语句组S2的功能;![](data:image/jpg;base64,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)
(3)设链表表示的线性表为(a1,a2, …,an),写出算法执行后的返回值所表示的线性表。
2.2. void ABC(BTNode * BT)
{
if BT {
ABC (BT->left);
ABC (BT->right);
cout<<BT->data<<' ';
}
}
该算法的功能是:
五、五、 算法填空(共8分)
二叉搜索树的查——递归算法:
bool Find(BTreeNode* BST,ElemType& item)
{
if (BST==NULL)
return false; //查失败
else {
if (item==BST->data){
item=BST->data;//查成功
return ___________;}
else if(item<BST->data)
return Find(______________,item);
else return Find(_______________,item);
}//if
}
六、六、 编写算法(共8分)
统计出单链表HL中结点的值等于给定值X的结点数。
int CountX(LNode* HL,ElemType x)
参考答案
一、一、 单选题(每题2分,共20分)
1.A 2.D 3.D 4.C 5.C 6.D 7.D 8.C 9.D 10.A
二、二、 填空题(每空1分,共26分)
1.1. 正确性 易读性 强壮性 高效率
2.2. O(n)
3.3. 9 3 3
4.4. -1 3 4 X * + 2 Y * 3 / -
5.5. 2n n-1 n+1
6.6. e 2e
7.7. 有向无回路
8.8. n(n-1)/2 n(n-1)
9.9. (12,40) ( ) (74) (23,55,63)
10.10. 增加1
绍兴市地图
11.11. O(log2n) O(nlog2n)
12.12. 归并
三、三、 运算题(每题6分,共24分)
1.姑婆山旅游景点介绍1. 线性表为:(78,50,40,60,34,90)
2.2. 邻接矩阵:![](data:image/jpg;base64,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)
邻接表如图11所示:
图11
3.3. 用克鲁斯卡尔算法得到的最小生成树为: