An embedded proximal interior point quadratic programming solver, which can solve dense and sparse quadratic programs, described in Schwan, Jiang, Kuhn, and Jones (2023) <doi:10.48550/arXiv.2304.00290>. Combining an infeasible interior point method with the proximal method of multipliers, the algorithm can handle ill-conditioned convex quadratic programming problems without the need for linear independence of the constraints. The solver is written in header only 'C++ 14' leveraging the 'Eigen' library for vectorized linear algebra. For small dense problems, vectorized instructions and cache locality can be exploited more efficiently. Allocation free problem updates and re-solves are also provided.
Version: 0.2.2 Imports: Matrix, methods, R6, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, slam, tinytest Published: 2023-08-14 DOI: 10.32614/CRAN.package.piqp Author: Balasubramanian Narasimhan [aut, cre], Roland Schwan [aut, cph], Yuning Jiang [aut], Daniel Kuhn [aut], Colin N. Jones [aut] Maintainer: Balasubramanian Narasimhan <naras at stanford.edu> BugReports: https://github.com/PREDICT-EPFL/piqp-r/issues License: BSD_2_clause + file LICENSE URL: https://predict-epfl.github.io/piqp-r/ NeedsCompilation: yes Citation: piqp citation info Materials: README In views: Optimization CRAN checks: piqp results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=piqp to link to this page.
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