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Next to the previous article. Insert the red/black tree! The deletion of the red/black tree is further considered in various situations. First, consider the single support of the red/black tree, that is, only the parent node has one child node and the other is null. In this

Because we only see Section 13.3, we only implementRotateAndInsertFunction.Thinking: if there is no parent node, when the parent node of z needs to be asked by the operator, we can use the lookup function to find the parent node and grandfather node of z from the root node t

Because only section 13.3 is displayedRotateAndInsertFunction. Thinking: if the parent node is not included, when you need to access the parent node of Z, we can use the lookup function to find the parent and grandfather nodes of Z from the root node to the path of the Z node to be searched. Therefore, we can access th

functional programming. Here they are one of the most common persistent data structures used to construct correlated arrays and collections, after the mutation, they can be maintained as previous versions. In addition to the O (log n) time, the persistent version of The Red/black tree requires O (log n) space for each insertion or deletion.The red and black trees are 2-4 trees. Equivalent. In other words,

Red and black TreesRed-black tree is a kind of data structure and algorithm in the classroom often mentioned but do not talk about the tree, is also a technical interview often asked the tree, however, whether the book or online data, usually more rigid difficult to understand, can a more intuitive way to understand the red-black tree? This article will explain the insertion and deletion of the

and black tree to achieve some valuable content.First, the basic conceptThe basic meaning of the red-black tree must be clarified before it is specifically implemented. The red-black tree is essentially a binary search tree that satisfies the basic nature of the binary search tree-that is, any node in the tree has a value greater than its left child

Red black Tree is a self-balancing binary search tree. Red and black trees are similar to AVL trees in that they maintain the balance of binary lookup trees with specific actions when inserting and deleting operations, resulting in higher lookup performance. Red-black trees can be searched, inserted, and deleted within O (log n) time.Binary search tree can see bi

The two-fork search tree is described earlier, and he has no problem with the efficiency of finding and inserting in most cases, but he is less efficient in the worst case. The data structure of the balanced lookup tree introduced in this article and later in this article ensures that the LGN efficiency can be achieved in the worst case, and we need to make sure that the tree remains in equilibrium after the insert is complete, which is the Balanced search tree. In a tree with n nodes, we want t

Q: What are the functions of the Alipay bonus? A: There are two kinds of functions of Alipay: Red Envelopes and red envelopes. The types of red envelopes that users can send to friends include: Group red envelopes, personal red envelopes, solitaire packets, and face-to-face

(Friendship hint, red-Haishi is based on the binary search tree, if the two-fork search tree does not understand, you can look at: two fork search tree) As you can see from the Analysis in section 4th, a binary search tree is a good data structure that can quickly find a given keyword and can quickly insert and delete data items. But the binary search tree has a very troublesome problem, if the tree is inserted in random data, the execution is good, b

I. OverviewIn the previous article, we reviewed the characteristics of red-black tree and added processing, we can learn that the red and black tree is a binary search tree, on the basis of the increase in the color of the node constraints to make the red and black trees approximate balance. When we add or remove nodes

Tips for developing small program red envelopes In the past, the company developed small program red packets and shared some pitfalls they encountered during the development into their blogs. Many people still don't understand how to develop it after reading it. I also consulted. So today, I wrote another article specially. This time I will not talk about the pitfalls I encountered during development. I wil

Idea: The difficulty of this algorithm is difficult to have a reference to a random node in the list, you can not determine which node the reference points to, but we can use the choice of reference to solve the problem. Using the original linked list as a reference, head is a node of the original list, and the next of this n