Least-Norm Linear Programming Solution as an Unconstrained Minimization Problem.

Abstract

It is shown that the dual of the problem of minimizing the 2-norm of the primal and dual optimal variables and slacks of a linear program, can be transformed into an unconstrained minimization of a convex, parameter-free, globally differentiable, piecewise quadratic function with a Lipschitz continuous gradient. If the slacks are not included in the norm minimization, one obtains a minimization problem with a convex, parameter-free, quadratic objective function subject to nonnegativity constraints only. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1980
Accession Number
ADA096664

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  • Olvi L. Mangasarian

Organizations

  • University of Wisconsin–Madison

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  • Algorithms
  • Applied Mathematics
  • Computer Programming
  • Continents
  • Convex Sets
  • Inequalities
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • North Carolina
  • Perturbation Theory
  • Perturbations
  • Quadratic Programming
  • Simplex Method
  • Theorems
  • United States
  • Wisconsin

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