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rlppinv
View on CRAN: Click
here
Download and install rlppinv package within the R console
Install from CRAN:
install.packages("rlppinv")
Install from Github:
library("remotes")
install_github("cran/rlppinv") Install by package version:
library("remotes")
install_version("rlppinv", "0.1.0") Attach the package and use:
library("rlppinv")
Maintained by
Ilya Bolotov
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[Scholar Profile | Author Map]
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First Published: 2025-12-03
Latest Update: 2025-12-03
Description:
The Linear Programming via Regularized Least Squares (LPPinv) is a two-stage estimation method that reformulates linear programs as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, LPPinv solves linear inequality, equality, and bound constraints by (1) constructing a canonical constraint system and computing a pseudoinverse projection, followed by (2) a convex-programming correction stage to refine the solution under additional regularization (e.g., Lasso, Ridge, or Elastic Net). LPPinv is intended for underdetermined and ill-posed linear problems, for which standard solvers fail.
How to cite:
Ilya Bolotov (2025). rlppinv: Linear Programming via Regularized Least Squares. R package version 0.1.0, https://cran.r-project.org/web/packages/rlppinv. Accessed 29 Sep. 2026.
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