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A Feasible BFGS Interior Point Algorithm for Solving Strongly Convex Minimization Problems

Abstract : We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters $\mu$ converging to zero. We prove that it converges q-superlinearly for each fixed $\mu$ and that it is globally convergent when $\mu\to0$.
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https://hal.inria.fr/inria-00073185
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 12:06:02 PM
Last modification on : Wednesday, November 27, 2019 - 9:44:02 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:43:51 PM

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  • HAL Id : inria-00073185, version 1

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Paul Armand, Jean Charles Gilbert, Sophie Jan-Jégou. A Feasible BFGS Interior Point Algorithm for Solving Strongly Convex Minimization Problems. [Research Report] RR-3500, INRIA. 1998. ⟨inria-00073185⟩

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