swMATH ID: 33926
Software Authors: Youngseok Kim, Peter Carbonetto, Mihai Anitescu, Matthew Stephens, Jason Willwerscheid, Jean Morrison
Description: R package mixsqp: Sequential Quadratic Programming for Fast Maximum-Likelihood Estimation of Mixture Proportions. Provides an optimization method based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function ”KWDual” in the ’REBayes’ package), and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large. This implements the ”mix-SQP” algorithm, with some improvements, described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2020) <doi:10.1080/10618600.2019.1689985>.
Homepage: https://cran.r-project.org/web/packages/mixsqp/index.html
Source Code:  https://github.com/cran/mixsqp
Dependencies: R
Keywords: arXiv_stat.CO; arXiv_stat.ME; nonparametric empirical Bayes; nonparametric maximum likelihood; mixture models; convex optimization; sequential quadratic programming; active set methods; rank-revealing QR decomposition
Related Software: Mosek; ashr; REBayes; R; ebnm; flashier; Rmosek; microbenchmark; trust; deconvolveR; horseshoe; EbayesThresh; LowRankApprox.jl; JuMP; Julia
Cited in: 0 Publications

Standard Articles

1 Publication describing the Software Year
A Fast Algorithm for Maximum Likelihood Estimation of Mixture Proportions Using Sequential Quadratic Programming
Youngseok Kim, Peter Carbonetto, Matthew Stephens, Mihai Anitescu