Package: piqp
Title: R Interface to Proximal Interior Point Quadratic Programming
        Solver
Version: 0.6.2
Authors@R: c(person("Balasubramanian", "Narasimhan", role=c("aut", "cre"),
	   	   email = "naras@stanford.edu"),
             person("Roland", "Schwan", role = c("aut", "cph")),
             person("Yuning", "Jiang", role = c("aut")),
             person("Daniel",  "Kuhn", role = c("aut")),
	     person("Colin N.",  "Jones", role = c("aut")))		   
Description: 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.
License: BSD_2_clause + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
URL: https://predict-epfl.github.io/piqp-r/
BugReports: https://github.com/PREDICT-EPFL/piqp-r/issues
SystemRequirements: C++17
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, slam, tinytest
VignetteBuilder: knitr
Imports: Matrix, methods, Rcpp, S7
Repository: https://predict-epfl.r-universe.dev
Date/Publication: 2026-02-18 22:07:01 UTC
RemoteUrl: https://github.com/predict-epfl/piqp-r
RemoteRef: HEAD
RemoteSha: bbbfa43a2fbd0bd83fe63f5a281d9c9b75d5d842
NeedsCompilation: yes
Packaged: 2026-06-18 07:55:55 UTC; root
Author: Balasubramanian Narasimhan [aut, cre],
  Roland Schwan [aut, cph],
  Yuning Jiang [aut],
  Daniel Kuhn [aut],
  Colin N. Jones [aut]
Maintainer: Balasubramanian Narasimhan <naras@stanford.edu>