• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
Fundamentals of Python: From First Programs Through Data
Fundamentals of Python: From First Programs Through Data

Tables As Trees: Merging with Wildcards Using Tree Traversal and Pruning
Tables As Trees: Merging with Wildcards Using Tree Traversal and Pruning

A Comparison of Dictionary Implementations
A Comparison of Dictionary Implementations

A Fast Contention-Friendly Binary Search Tree
A Fast Contention-Friendly Binary Search Tree

... Removing such a node is likely to cause much more contention than removing a less frequently accessed one. In order to prevent this, only nodes that are marked as deleted and have at least one of their children as an empty subtree are removed. Marked deleted nodes can ...
Chapter2
Chapter2

... Let n be the number of elements in the stack The space used is O(n) Each operation runs in time O(1) ...
Fundamental Data Structures
Fundamental Data Structures

... become its parent. This way we can build up a hierarchy of nodes of any depth. It is never possible to add a parent node because the constraint that each node has at most one parent would be violated. The node manipulation is usually relative to the current node. We can modify or remove the current ...
Lecture 6: Binary Search Trees CSCI 700
Lecture 6: Binary Search Trees CSCI 700

Scalable Classification Algorithms
Scalable Classification Algorithms

Final - Philadelphia University Jordan
Final - Philadelphia University Jordan

Functional Pearl Trouble Shared is Trouble Halved
Functional Pearl Trouble Shared is Trouble Halved

... Before we tackle optimal bracketing, let us first look at a related but simpler problem, in which each recursive call depends only on the immediate segments. ...
Creating a linked List
Creating a linked List

Geometric Data Structures
Geometric Data Structures

... and the remainder is partitioned into 2 equal sets. – The 2 sets are partitioned into sets above & below – Construct the tree recursively ...
Advanced Data Structure
Advanced Data Structure

... Case II : Removing a node with a single child  Replace the removed node with its child Case III : Removing a node with 2 children  Replace the removed node with the minimum element in the right subtree (or maximum element in the left subtree)  This may create a hole again  Apply Case I or II Som ...
Efficient Differential Timeslice Computation
Efficient Differential Timeslice Computation

... simply computing either incrementalTimeslice or decrementalTimeslice is more efficient than computing differentialTimeslice. Rather, the solution should require only a few disk accesses. Also observe that using the temporal proximity among the three times tx−1 , tx , and tx+1 as the basis for comput ...
Chapter 4: Algorithms and Data Structures
Chapter 4: Algorithms and Data Structures

Time and location: COS 226 Midterm Review Fall 2015
Time and location: COS 226 Midterm Review Fall 2015

Document
Document

TITLE BYLINE Synonym Definition Discussion
TITLE BYLINE Synonym Definition Discussion

... Assuming that x is divisible, and that x can be sent in N blocks of size n/N (assuming for simplicity that N divides n) better performance can be achieved. By pipelining through the path, such that the blocks are sent one after another, the root is busy for N consecutive rounds. The ith processor, i ...
AVL Tree - METU OCW
AVL Tree - METU OCW

hyperoctree
hyperoctree

Heaps/Trees
Heaps/Trees

Dynamic Optimality—Almost ∗ Erik D. Demaine Dion Harmon
Dynamic Optimality—Almost ∗ Erik D. Demaine Dion Harmon

The Anchors Hierarchy
The Anchors Hierarchy

Advanced Data Structure
Advanced Data Structure

Binary Trees
Binary Trees

< 1 ... 30 31 32 33 34 35 36 37 38 ... 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.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report