LPPINV: Stata module providing Linear Programming via Regularized Least Squares (LPPinv) is a modular two-step estimator for solving underdetermined, ill-posed, or structurally constrained linear and quadratic programming problems using least-squares and convex optimization

Ilya Bolotov

Statistical Software Components from Boston College Department of Economics

Abstract: 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.

Language: Stata
Requires: Stata version 16 and Python modules pylppinv, numpy, cvxpy
Keywords: linear programming; least squares; pseudoinverse; singular value decomposition (search for similar items in EconPapers)
Date: 2022-02-06, Revised 2026-01-20
Note: This module should be installed from within Stata by typing "ssc install lppinv". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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http://fmwww.bc.edu/repec/bocode/l/lppinv.ado program code (text/plain)
http://fmwww.bc.edu/repec/bocode/l/lppinv.sthlp help file (text/plain)

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