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Treap

In computer science, the treap and the randomized binary search tree are two closely related forms of binary search tree data structures that maintain a dynamic set of ordered keys and allow binary searches among the keys. After any sequence of insertions and deletions of keys, the shape of the tree is a random variable with the same probability…

Science, Description & Operations

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Research this topic

Explore the main themes, entities and connections around Treap. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Delete
O(log n)
Insert
O(log n)
Operation
Average
Search
O(log n)
Space
O(n)
Time complexity in big O notation
Time complexity in big O notationOperation Average Worst caseSearch O(log n) O(n)Insert O(log n) O(n)Delete O(log n) O(n)Space complexitySpace O(n) O(n)

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Description

Operations

Randomized binary search tree

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Treap

Nodes30
Edges29
Triples73
Avg. degree1.93
Density0.066667
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Treap

Top relations

related to External links · 19
Treap → ActionScript3, Archived, By Roy ClemmonsPure Go, Cecilia AragonOpen Data Structures, Collection, COM, Cython, Despite, Go, Jeff Erickson, Junyi SunVB6, Lecture, Pat MorinAnimated, Python, Randomized Binary Search Tree, Section, UIUC, Visual, Wayback MachineRandomized
related to Description · 14
Treap → An, Aragon, As, Because, Cartesian, Cecilia, If, It, Raimund Seidel, The, Therefore, This, Thus, Treaps
related to Bulk operations · 10
Treap → After, Create, In, Joining, More, Rotate, The, These, This, To
related to Randomized binary search tree · 8
Treap → Aragon, Martínez, Placing, Rather, Roura, Seidel, The, When
related to Basic operations · 7
Treap → Binary, Finally, If, In, Then, To, Treaps
related to Comparison · 5
Treap → Although, For, However, In, The
related to Building a treap · 2
Treap → Therefore, To
Delete · 1
Treap → O(log n)
Insert · 1
Treap → O(log n)
Operation · 1
Treap → Average

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

tree binary search two treaps random node randomized number insertion priority nodes trees root algorithm log time key order left

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
TreapDeleteO(log n)1.00infobox
TreapInsertO(log n)1.00infobox
TreapOperationAverage1.00infobox
TreapSearchO(log n)1.00infobox
TreapSpaceO(n)1.00infobox
TreapTime complexity in big O notationTime complexity in big O notationOperation Average Worst caseSearch O(log n) O(n)Insert O(log n) O(n)Delete O(log n) O(n)Space complexitySpace O(n) O(n)1.00infobox
TreapTypeRandomized binary search tree1.00infobox
Treaprelated to Basic operationsTreaps0.60section
Treaprelated to Basic operationsTo0.60section
Treaprelated to Basic operationsBinary0.60section
Treaprelated to Basic operationsThen0.60section
Treaprelated to Basic operationsIf0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
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