In Anirban166/testComplexity: Asymptotic Complexity Testing Framework

knitr::opts_chunk$set(
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Abstract

Being a wrapper to a C++ implementation of the functional pruning optimal partioning (fpop) algorithm, the fpop package provides the function Fpop which is a dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. It exactly minimizes the mean squared error for a penalty linear in the number of changepoints, which solves the problem of detecting changepoints in an univariate sequence. (by minimising the mean squared error over segmentations)

Reference

The official pdf for the fpop package was used as reference for extracting the above information.

Testing

The fpop algorithm is expected to possess log-linear time complexity, as theoretically derived and empiricially observed. We can verify the same using testComplexity's functions:

library(testComplexity)
asymptoticTimeComplexityClass(asymptoticTimings(fpop::Fpop(rnorm(N), 1), data.sizes = 10^seq(1, 4, by = 0.5), max.seconds = 0.1))
#> [1] "loglinear"