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Active-set prediction for interior point methods using controlled perturbations

Coralia Cartis(cartis***at***maths.ox.ac.uk)
Yiming Yan(y.yan-4***at***sms.ed.ac.uk)

Abstract: We propose the use of controlled perturbations to address the challenging question of optimal active-set prediction for interior point methods. Namely, in the context of linear programming, we consider perturbing the inequality constraints/bounds so as to enlarge the feasible set. We show that if the perturbations are chosen appropriately, the solution of the original problem lies on or close to the central path of the perturbed problem. We also find that a primal-dual path-following algorithm applied to the perturbed problem is able to accurately predict the optimal active set of the original problem when the duality gap for the perturbed problem is not too small; furthermore, depending on problem conditioning, this prediction can happen sooner than predicting the active-set for the perturbed problem or for the original one if no perturbations are used. Encouraging preliminary numerical experience is reported when comparing activity prediction for the perturbed and unperturbed problem formulations.

Keywords: active-set prediction, interior point methods, linear programming

Category 1: Linear, Cone and Semidefinite Programming

Citation: Technical Report NA-14/07, Mathematical Institute, University of Oxford, 2014

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Entry Submitted: 05/18/2014
Entry Accepted: 05/18/2014
Entry Last Modified: 05/18/2014

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