red-black-tree

I am trying to clear up some things regarding complexity in some of the operations of TreeSet. On the javadoc it says: "This implementation provides guaranteed log(n) time cost for the basic operations (add, remove and contains)." So far so good. My question is what happens on addAll(), removeAll() etc. Here the javadoc for Set says: "If the specified collection is also a set, the addAll operation effectively modifies this set so that its value is the union of the two sets." Is it just explaining the logical outcome of the operation or is it giving a hint about the complexity? I mean, if the

The worst-case running time of insertion on a red-black tree is O(lg n) and if I perform a in-order walk on the tree, I essentially visit each node, so the total worst-case runtime to print the sorted collection would be O(n lg n) I am curious, why are red-black trees not preferred for sorting over quick sort (whose average-case running time is O(n lg n) . I see that maybe because red-black trees do not sort in-place, but I am not sure, so maybe someone could help. Knowing which sort algorithm performs better really depend on your data and situation. If you are talking in general/practical

I was going through the source code of TreeMap in JAVA . As per JAVA doc: A Red-Black tree based NavigableMap implementation. The map is sorted according to the natural ordering of its keys, or by a Comparator provided at map creation time, depending on which constructor is used. This implementation provides guaranteed log(n) time cost for the containsKey, get, put and remove operations. Algorithms are adaptations of those in Cormen, Leiserson, and Rivest's Introduction to Algorithms. In the source code I found that an Inner Class Entry was used as a node . static final class Entry<K,V>

I've been playing around with RB tree implementation in Haskell but having difficulty changing it a bit so the data is only placed in the leaves, like in a binary leaf tree: /+\ / \ /+\ \ / \ c a b The internal nodes would hold other information e.g. size of tree, in addition to the color of the node (like in a normal RB tree but the data is held in the leaves ony). I am also not needed to sort the data being inserted. I use RB only to get a balanced tree as i insert data but I want to keep the order in which data is inserted. The original code was (from Okasaki book): data Color = R | B data

I've seen binary trees and binary searching mentioned in several books I've read lately, but as I'm still at the beginning of my studies in Computer Science, I've yet to take a class that's really dealt with algorithms and data structures in a serious way. I've checked around the typical sources (Wikipedia, Google) and most descriptions of the usefulness and implementation of (in particular) Red-Black trees have come off as dense and difficult to understand. I'm sure for someone with the necessary background, it makes perfect sense, but at the moment it reads like a foreign language almost. So

Can someone please explain what the main differences between these two data structures are? I've been trying to find a source online that highlights the differences/similarities, but I haven't found anything too informative. In what cases would one be preferred over the other? What practical situations make one "better" to use than the other? AVL trees maintain a more rigid balance than red-black trees. The path from the root to the deepest leaf in an AVL tree is at most ~1.44 lg(n+2), while in red black trees it's at most ~2 lg (n+1). As a result, lookup in an AVL tree is typically faster,

This question already has an answer here: C++ - interval tree implementation 5 answers I'm trying to find an efficient C++ interval tree implementation (mostly likely based on red black trees) without a viral or restrictive license. Any pointers to a clean lightweight standalone implementation? For the use case I have in mind, the set of intervals is known at the outset (there would be say a million) and I want to be able to quickly obtain a list of intervals that overlap a given interval. Thus the tree once built will not change -- just needs rapid queries. I've written a template-based