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Algorithms and Data Structures
Algorithms and Data Structures

Data Structures Lecture 1
Data Structures Lecture 1

Binary Trees 1
Binary Trees 1

(a,b) tree
(a,b) tree

Skip-list, AVL trees
Skip-list, AVL trees

What is a B
What is a B

Left-leaning Red-Black Trees
Left-leaning Red-Black Trees

Searching: Binary Tress
Searching: Binary Tress

Searching: Binary Tress
Searching: Binary Tress

data structuer Lecture 1
data structuer Lecture 1

Amortized Analysis Master MOSIG
Amortized Analysis Master MOSIG

Chapter 5-2 - Computer Science
Chapter 5-2 - Computer Science

Document
Document

... Using the union-by-rank heuristic and path compression, show the result from the following series of equivalences on a set of objects indexed by the values 1 through 16, assuming initially that each element in the set is in an equivalence class containing it alone. When the ranks of the two trees ar ...
Final Solutions
Final Solutions

... (c) W (at most one trie node for each bit in the input). Alternate solution 1: use a TST instead of a binary trie. Since the radix size is 2, the running time will still be linear: you at most double the length of a search path for a codeword. Alternate solution 2: sort all of the codewords (MSD rad ...
file_organize
file_organize

Document
Document

... Using the union-by-rank heuristic and path compression, show the result from the following series of equivalences on a set of objects indexed by the values 1 through 16, assuming initially that each element in the set is in an equivalence class containing it alone. When the ranks of the two trees ar ...
File - computergixz
File - computergixz

CS 2133: Data Structures
CS 2133: Data Structures

Introduction: Multimedia Databases
Introduction: Multimedia Databases

... smaller height than the original tree T. ...
x - Yimg
x - Yimg

Proofs, Recursion and Analysis of Algorithms
Proofs, Recursion and Analysis of Algorithms

Thirteenth Lecture
Thirteenth Lecture

Trees
Trees

Part-1
Part-1

Binary Search Trees
Binary Search Trees

< 1 ... 49 50 51 52 53 54 55 56 57 ... 62 >

Red–black tree

A red–black tree is a binary search tree with an extra bit of data per node, its color, which can be either red or black. The extra bit of storage ensures an approximately balanced tree by constraining how nodes are colored from any path from the root to the leaf. Thus, it is a data structure which is a type of self-balancing binary search tree.Balance is preserved by painting each node of the tree with one of two colors (typically called 'red' and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced the tree can become in the worst case. When the tree is modified, the new tree is subsequently rearranged and repainted to restore the coloring properties. The properties are designed in such a way that this rearranging and recoloring can be performed efficiently.The balancing of the tree is not perfect but it is good enough to allow it to guarantee searching in O(log n) time, where n is the total number of elements in the tree. The insertion and deletion operations, along with the tree rearrangement and recoloring, are also performed in O(log n) time.Tracking the color of each node requires only 1 bit of information per node because there are only two colors. The tree does not contain any other data specific to its being a red–black tree so its memory footprint is almost identical to a classic (uncolored) binary search tree. In many cases the additional bit of information can be stored at no additional memory cost.
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