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In computational complexity theory, P, also known as PTIME or DTIME(nO(1)), is a fundamental complexity class. It contains all decision problems that can be solved by a deterministic Turing machine using a polynomial amount of computation time, or polynomial time.
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Explore the main themes, entities and connections around P (complexity). Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the full topic structure. 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.
See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
problems class polynomial time complexity also known polynomial-time problem np algorithm decision machine turing computational pspace displaystyle whether one deterministic
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| the unary version of any undecidable problem.In 1999 | instance of | including some undecidable problems | 0.80 | text |
| Jin-Yi Cai | instance of | including some undecidable problems | 0.80 | text |
| D | instance of | including some undecidable problems | 0.80 | text |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.