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A BFGS-IP Algorithm for Solving Strongly Convex Optimization Problems with Feasibility Enforced by an Exact Penalty Approach

Abstract : This paper introduces and analyses a new algorithm for minimizing a convex function subject to a finite number of convex inequality constraints. It is assumed that the Lagrangian of the problem is strongly convex. The algorithm combines interior point methods for dealing with the inequality constraints and quasi-Newton techniques for accelerating the convergence. Feasibility of the iterates is progressively enforced thanks to shift variables and an exact penalty approach. Global and $q$-superlinear convergenc- e is obtained for a fixed penalty parameter; global convergence to the analytic center of the optimal set is ensured when the barrier parameter tends to zero, provided strict complementarity holds.
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https://hal.inria.fr/inria-00072546
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 10:15:01 AM
Last modification on : Saturday, October 24, 2020 - 3:03:04 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:12:48 PM

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  • HAL Id : inria-00072546, version 1

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Jean Charles Gilbert, Sophie Jan, Paul Armand. A BFGS-IP Algorithm for Solving Strongly Convex Optimization Problems with Feasibility Enforced by an Exact Penalty Approach. [Research Report] RR-4087, INRIA. 2000. ⟨inria-00072546⟩

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