Scalability analysis comparing the near-linear algorithm and the naïve quadratic-time algorithm.

Scalability analysis comparing the near-linear algorithm and the naïve quadratic-time algorithm.

<p>The diagrams show the average runtime with standard deviation bands over 1000 runs for each tree size () on pairs of random trees, evaluated for <i>L</i><sub><i>p</i></sub>-norms (). Crossing points indicate the tree sizes where the near-linear algorithm...

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Bibliographic Details
Main Author:	Paweł Górecki (3285255) (author)
Other Authors:	Alexey Markin (16698687) (author), Sriram Vijendran (21520036) (author), Oliver Eulenstein (3291621) (author)
Published:	2025
Subjects:	Biophysics Microbiology Genetics Molecular Biology Biotechnology Cancer Infectious Diseases Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified linear time complexity e ., naïve incorporating generalized variants >< sub >< div >< p unrestricted algorithmic framework new algorithmic framework >< sub xmlns generalized cophenetic distances generalized class ></ sub p </ cophenetic distances widely recognized vector format trees represented significantly restricts scale trees scale applications scalability study quadratic runtime practical performance l </ key components established metric dates back current best compare pairs biology used become prohibitive analyze trees also enhances 60 years
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