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rlppinv  

Linear Programming via Regularized Least Squares
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
[Scholar Profile | Author Map]
All associated links for this package
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.
Previous versions and publish date:
(2026-07-09 06:51), 0.1.0 (2025-12-03 21:30), 0.2.0 (2026-01-30 21:50), 0.3.0 (2026-03-10 13:20), 1.0.0 (2026-05-17 00:00), 1.1.0 (2026-06-07 18:10)
Other packages that cited rlppinv R package
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Complete documentation for rlppinv
Functions, R codes and Examples using the rlppinv R package
Full rlppinv package functions and examples
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