HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation

# 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 $\mu\to0$.
Keywords :
Document type :
Reports
Domain :
Complete list of metadata

https://hal.inria.fr/inria-00073185
Contributor : Rapport de Recherche Inria Connect in order to contact the contributor
Submitted on : Wednesday, May 24, 2006 - 12:06:02 PM
Last modification on : Friday, February 4, 2022 - 3:14:16 AM
Long-term archiving on: : Sunday, April 4, 2010 - 9:43:51 PM

### Identifiers

• HAL Id : inria-00073185, version 1

### Citation

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⟩

Record views

Files downloads