A primal–dual regularized interior-point method for convex quadratic programs

  • Interior-point methods in augmented form for linear and convex quadratic programming require the solution of a sequence of symmetric indefinite linear systems which are used to derive search directions. Safeguards are typically required in order to handle free variables or rank-deficient Jacobians. We propose a consistent framework and accompanying theoretical justification for regularizing these linear systems. Our approach can be interpreted as a simultaneous proximal-point regularization of the primal and dual problems. The regularization is termed exact to emphasize that, although the problems are regularized, the algorithm recovers a solution of the original problem, for appropriate values of the regularization parameters.

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Metadaten
Author:M. P. Friedlander, D. Orban
DOI:https://doi.org/10.1007/s12532-012-0035-2
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2012
Tag:Software; Theoretical Computer Science
Volume:4
Issue:1
Page Number:37
First Page:71
Last Page:107
Mathematical Programming Computation :MPC 2012 - Issue 1
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