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In computer science, patience sorting is a sorting algorithm inspired by, and named after, the card game patience. A variant of the algorithm efficiently computes the length of a longest increasing subsequence in a given array.
The analysis highlights History, Applications and Science as prominent areas in the source structure around Patience sorting.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Patience sorting shows recurring relationship patterns in the source. For example, Patience sorting → According, Aldous, Colin Lingwood Mallows, Diaconis, Donald Knuth, Floyd, Floyd's, Hammersley, Initial, Mallows, Patience, Robert, Ross Another extracted example is Patience sorting → Each, Floyd's, Patience, The, This, When. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
algorithm sorting patience game card piles increasing pile longest subsequence cards array new length log first top time performance input
TTTA extracted 29 structured relationships around Patience sorting. Examples in this analysis include Patience sorting → Best-case performance → O(n); occurs when the input is pre-sorted and Patience sorting → Class → Sorting algorithm. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Patience sorting | Best-case performance | O(n); occurs when the input is pre-sorted | 1.00 | infobox |
| Patience sorting | Class | Sorting algorithm | 1.00 | infobox |
| Patience sorting | Data structure | Array | 1.00 | infobox |
| Patience sorting | Optimal | ? | 1.00 | infobox |
| Patience sorting | Worst-case performance | O(n log n) | 1.00 | infobox |
| Patience sorting | is a | sorting algorithm inspired by | 0.90 | text |
| Patience sorting | related to history | Patience | 0.60 | section |
| Patience sorting | related to history | Colin Lingwood Mallows | 0.60 | section |
| Patience sorting | related to history | Ross | 0.60 | section |
| Patience sorting | related to history | According | 0.60 | section |
| Patience sorting | related to history | Aldous | 0.60 | section |
| Patience sorting | related to history | Diaconis | 0.60 | section |
The concept neighborhoods around Patience sorting bring nearby vocabulary together. In this analysis, examples include Sorting, Sort and Game. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Patience sorting, one of the stronger structural bridges in this analysis connects Patience sorting with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Patience sorting to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Patience sorting · EN edition · Analysis: TopicsToTalkAbout