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Article Dans Une Revue SIAM Journal on Optimization Année : 2000

A feasible BFGS interior point algorithm for solving strongly convex minimization problems

Résumé

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

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Paul Armand, Jean Charles Gilbert, Sophie Jan-Jégou. A feasible BFGS interior point algorithm for solving strongly convex minimization problems. SIAM Journal on Optimization, 2000, 11 (1), pp.199-222. ⟨10.1137/S1052623498344720⟩. ⟨inria-00073185⟩
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