{ "_id": "6a33a6203efcd9bda43aa411", "Package": "piqp", "Title": "R Interface to Proximal Interior Point Quadratic Programming\nSolver", "Version": "0.6.2", "Authors@R": "c(person(\"Balasubramanian\", \"Narasimhan\", role=c(\"aut\", \"cre\"),\nemail = \"naras@stanford.edu\"),\nperson(\"Roland\", \"Schwan\", role = c(\"aut\", \"cph\")),\nperson(\"Yuning\", \"Jiang\", role = c(\"aut\")),\nperson(\"Daniel\", \"Kuhn\", role = c(\"aut\")),\nperson(\"Colin N.\", \"Jones\", role = c(\"aut\")))", "Description": "An embedded proximal interior point quadratic programming\nsolver, which can solve dense and sparse quadratic programs,\ndescribed in Schwan, Jiang, Kuhn, and Jones (2023)\n. Combining an infeasible\ninterior point method with the proximal method of multipliers,\nthe algorithm can handle ill-conditioned convex quadratic\nprogramming problems without the need for linear independence\nof the constraints. The solver is written in header only 'C++\n14' leveraging the 'Eigen' library for vectorized linear\nalgebra. For small dense problems, vectorized instructions and\ncache locality can be exploited more efficiently. Allocation\nfree 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", "VignetteBuilder": "knitr", "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": { "Date": "2026-06-18 07:55:55 UTC", "User": "root" }, "Author": "Balasubramanian Narasimhan [aut, cre],\nRoland Schwan [aut, cph],\nYuning Jiang [aut],\nDaniel Kuhn [aut],\nColin N. 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