red-black-tree

I'm looking for an interval tree algorithm similar to the red-black interval tree in CLR but that supports merging of intervals by default so that there are never any overlapping intervals. In other words if you had a tree containing two intervals [2,3] and [5,6] and you added the interval [4,4], the result would be a tree containing just one interval [2,6]. Thanks Update: the use case I'm considering is calculating transitive closure. Interval sets are used to store the successor sets because they have been found to be quite compact . But if you represent interval sets just as a linked list I

The OCaml standard library has a wonderful Set implementation that uses a very efficient divide-and-conquer algorithm to compute the union of two sets. I believe it takes whole subtrees (not just single elements) from one set and inserts them into the other set, rebalancing when necessary. I'm wondering if this requires the height information that is kept in the AVL tree that OCaml uses or if this is also possible with red-black trees. For example, is it possible to concatenate a pair of red-black trees more efficiently than simply iterating over the second tree appending its elements to the

What are the applications of red-black (RB) trees? Is there any application where only RB Trees can be used and no other data structures? starblue A red-black tree is a particular implementation of a self-balancing binary search tree , and today it seems to be the most popular choice of implementation. Binary search trees are used to implement finite maps, where you store a set of keys with associated values. You can also implement sets by only using the keys and not storing any values. Balancing the tree is needed to guarantee good performance, as otherwise the tree could degenerate into a

AVL and Red black trees are both self-balancing except Red and black color in the nodes. What's the main reason for choosing Red black trees instead of AVL trees? What are the applications of Red black trees? Nikunj Banka What's the main reason for choosing Red black trees instead of AVL trees? Both red-black trees and AVL trees are the most commonly used balanced binary search trees and they support insertion, deletion and look-up in guaranteed O(logN) time . However, there are following points of comparison between the two: AVL trees are more rigidly balanced and hence provide faster look

As a programmer when should I consider using a RB tree, B- tree or an AVL tree? What are the key points that needs to be considered before deciding on the choice? Can someone please explain with a scenario for each tree structure why it is chosen over others with reference to the key points? Take this with a pinch of salt: B-tree when you're managing more than thousands of items and you're paging them from a disk or some slow storage medium. RB tree when you're doing fairly frequent inserts, deletes and retrievals on the tree. AVL tree when your inserts and deletes are infrequent relative to

Typically changes to a key in a red-black tree need to be performed by removing, then re-inserting the node. Is it possible to performs key updates to a node in a red black tree that is more efficient than delete+insert? Implement update with [search if required +] delete + insert 1 - delete the key O(log n) 2 - insert a new node with the new key O(log n) Even if you search for a key first, it's O(log n) . See this page for more details on RBT. 来源： https://stackoverflow.com/questions/22684024/possible-to-update-nodes-key-in-a-red-black-tree-without-removing-and-inserting

Apparently you could do either, but the former is more common. Why would you choose the latter and how does it work? I read this: http://www.drdobbs.com/cpp/stls-red-black-trees/184410531 ; which made me think that they did it. It says: insert_always is a status variable that tells rb_tree whether multiple instances of the same key value are allowed. This variable is set by the constructor and is used by the STL to distinguish between set and multiset and between map and multimap. set and map can only have one occurrence of a particular key, whereas multiset and multimap can have multiple