Chapter 13 Exercises

本文解析了红黑树的几个关键习题,包括红黑树中红色内部节点与黑色内部节点比例的最大与最小值,右转换可能性及复杂度分析,以及插入删除操作对红黑树的影响。
We use the following expression S(T) to show a tree T:
if T is null, then S(T) is a null string
if T contains only a root node, S(T) = root
otherwise, let L and R are T's left and right subtrees, respectively, S(T) = root(S(L),S(R))

Exercises
13.1-7 Describe a red-black tree on n keys that realizes the largest possible ratio of red internal nodes to black internal nodes. What is the ratio? What tree has the smallest possible ratio, and what is the ratio?
Largest: Tree: 2(1,3), when 1,3 are red. The ratio is 2.
Smallest: a tree with only a root node. The ratio is 0.

13.2-5 We say that a binary search tree T1 can be right-converted to binary search tree T2 if it is possible to obtain T2 from T1 via a series of calls to RIGHT-ROTATE. Give an example of two trees T1 and T2 such that T1 cannot be right-converted to T2. Then show that if a tree T1 can be right-converted to T2, it can be right-converted using O(n^2) calls to RIGHT-ROTATE
The counter-example is : T1: 1(,2), T2: 2(1,). Or, a simple case that T2 can be right-converted to T1. It can be proved that when T2 can be right-converted to T1, T1 is not so to T2.
The O(n^2) proof is also not difficult. If T1's root is different from T2's, O(n) RIGHT-ROTATEs are needed to turn T1's root to T2's. Then, think of the right subtrees of T1's and T2's. We can conclude a recurrence of T(n) = T(n-1) + O(n), thus T(n) = O(n^2)

13.4-7 Suppose that a node x is inserted into a red-black tree with RB-INSERT and then immediately deleted with RB-DELETE. Is the resulting red-black tree the same as the initial red-black tree? Justify your answer.
The answer is no. Consider the tree T = 2(1,3), where 1 and 3 are the two red nodes. When we insert 1.5, which is initially inserted as 1's right node, 1 and 3 nodes will turn black, and 1.5 will be red. Deleting 1.5 node doesn't effect on other nodes. Thus, after inserting and deleting 1.5, the 1 and 3 nodes will turn black.
Another example is the Figure 13.4 on the book. The procedure of inserting 4 is shown on Figure 13.4, and deleting 4 also has no effect on other nodes. The resulting tree is also different from Figure 13.4(a).

转载于:https://www.cnblogs.com/FancyMouse/articles/1164799.html

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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