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Reports (Research Report) Year : 1998

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

Paul Armand
Sophie Jan-Jégou

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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Dates and versions

inria-00073185 , version 1 (24-05-2006)

Identifiers

  • HAL Id : inria-00073185 , version 1

Cite

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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