rlppinv
2.0.0Linear Programming via Regularized Least Squares
Overview
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.
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Health
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-06-0812 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1814 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-108 OK · 0 NOTE · 0 WARNING · 6 ERROR · 0 FAILURE
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43 avg / 43 max
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Package metadata
- First published
- 2025-12-03
- Total releases
- 6 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- not tracked yet
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