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.
Package details |
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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> |
License | BSD_2_clause + file LICENSE |
Version | 0.2.2 |
URL | https://predict-epfl.github.io/piqp-r/ |
Package repository | View on CRAN |
Installation |
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