Package: rlppinv 2.0.0
Ilya Bolotov
rlppinv: Linear Programming via Regularized Least Squares
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.
Authors:
rlppinv_2.0.0.tar.gz
rlppinv_2.0.0.zip(r-4.7-any)rlppinv_2.0.0.zip(r-4.6-any)rlppinv_2.0.0.zip(r-4.5-any)
rlppinv_2.0.0.tgz(r-4.6-any)rlppinv_2.0.0.tgz(r-4.5-any)
rlppinv_2.0.0.tar.gz(r-4.7-any)rlppinv_2.0.0.tar.gz(r-4.6-any)
rlppinv_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rlppinv/json (API)
| # Install 'rlppinv' in R: |
| install.packages('rlppinv', repos = c('https://econcz.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/econcz/rlppinv/issues
convex-optimizationgeneralized-inverseleast-squareslinear-programingregularization
Last updated from:e334995db7. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 124 | ||
| source / vignettes | OK | 193 | ||
| linux-release | OK | 110 | ||
| macos-release | OK | 73 | ||
| macos-oldrel | OK | 97 | ||
| windows-devel | OK | 67 | ||
| windows-release | OK | 81 | ||
| windows-oldrel | OK | 74 | ||
| wasm-release | OK | 126 |
Exports:lppinv
Dependencies:backportscheckmateclarabelcliCVXRgmphighslatticeMatrixosqprclspRcppRcppEigenS7scsslam
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Solve a linear program via Convex Least Squares Programming (CLSP). | lppinv |